A novel method to identify hub pathways of rheumatoid arthritis based on differential pathway networks.

PubMed

Wei, Shi-Tong; Sun, Yong-Hua; Zong, Shi-Hua

2017-09-01

The aim of the current study was to identify hub pathways of rheumatoid arthritis (RA) using a novel method based on differential pathway network (DPN) analysis. The present study proposed a DPN where protein‑protein interaction (PPI) network was integrated with pathway‑pathway interactions. Pathway data was obtained from background PPI network and the Reactome pathway database. Subsequently, pathway interactions were extracted from the pathway data by building randomized gene‑gene interactions and a weight value was assigned to each pathway interaction using Spearman correlation coefficient (SCC) to identify differential pathway interactions. Differential pathway interactions were visualized using Cytoscape to construct a DPN. Topological analysis was conducted to identify hub pathways that possessed the top 5% degree distribution of DPN. Modules of DPN were mined according to ClusterONE. A total of 855 pathways were selected to build pathway interactions. By filtrating pathway interactions of weight values >0.7, a DPN with 312 nodes and 791 edges was obtained. Topological degree analysis revealed 15 hub pathways, such as heparan sulfate/heparin‑glycosaminoglycan (HS‑GAG) degradation, HS‑GAG metabolism and keratan sulfate degradation for RA based on DPN. Furthermore, hub pathways were also important in modules, which validated the significance of hub pathways. In conclusion, the proposed method is a computationally efficient way to identify hub pathways of RA, which identified 15 hub pathways that may be potential biomarkers and provide insight to future investigation and treatment of RA.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

PubMed Central

Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

PubMed

Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

2017-01-01

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations.

PubMed

Zhang, Han; Wheeler, William; Hyland, Paula L; Yang, Yifan; Shi, Jianxin; Chatterjee, Nilanjan; Yu, Kai

2016-06-01

Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP through empirical and simulated data. We conducted a comprehensive pathway-based meta-analysis with sARTP on type 2 diabetes (T2D) by integrating SNP-level summary statistics from two large studies consisting of 19,809 T2D cases and 111,181 controls with European ancestry. Among 4,713 candidate pathways from which genes in neighborhoods of 170 GWAS established T2D loci were excluded, we detected 43 T2D globally significant pathways (with Bonferroni corrected p-values < 0.05), which included the insulin signaling pathway and T2D pathway defined by KEGG, as well as the pathways defined according to specific gene expression patterns on pancreatic adenocarcinoma, hepatocellular carcinoma, and bladder carcinoma. Using summary data from 8 eastern Asian T2D GWAS with 6,952 cases and 11,865 controls, we showed 7 out of the 43 pathways identified in European populations remained to be significant in eastern Asians at the false discovery rate of 0.1. We created an R package and a web-based tool for sARTP with the capability to analyze pathways with thousands of genes and tens of thousands of SNPs.

A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations

PubMed Central

Zhang, Han; Wheeler, William; Hyland, Paula L.; Yang, Yifan; Shi, Jianxin; Chatterjee, Nilanjan; Yu, Kai

2016-01-01

Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP through empirical and simulated data. We conducted a comprehensive pathway-based meta-analysis with sARTP on type 2 diabetes (T2D) by integrating SNP-level summary statistics from two large studies consisting of 19,809 T2D cases and 111,181 controls with European ancestry. Among 4,713 candidate pathways from which genes in neighborhoods of 170 GWAS established T2D loci were excluded, we detected 43 T2D globally significant pathways (with Bonferroni corrected p-values < 0.05), which included the insulin signaling pathway and T2D pathway defined by KEGG, as well as the pathways defined according to specific gene expression patterns on pancreatic adenocarcinoma, hepatocellular carcinoma, and bladder carcinoma. Using summary data from 8 eastern Asian T2D GWAS with 6,952 cases and 11,865 controls, we showed 7 out of the 43 pathways identified in European populations remained to be significant in eastern Asians at the false discovery rate of 0.1. We created an R package and a web-based tool for sARTP with the capability to analyze pathways with thousands of genes and tens of thousands of SNPs. PMID:27362418

Pathway Analysis and Omics Data Visualization Using Pathway Genome Databases: FragariaCyc, a Case Study.

PubMed

Naithani, Sushma; Jaiswal, Pankaj

2017-01-01

The species-specific plant Pathway Genome Databases (PGDBs) based on the BioCyc platform provide a conceptual model of the cellular metabolic network of an organism. Such frameworks allow analysis of the genome-scale expression data to understand changes in the overall metabolisms of an organism (or organs, tissues, and cells) in response to various extrinsic (e.g. developmental and differentiation) and/or extrinsic signals (e.g. pathogens and abiotic stresses) from the surrounding environment. Using FragariaCyc, a pathway database for the diploid strawberry Fragaria vesca, we show (1) the basic navigation across a PGDB; (2) a case study of pathway comparison across plant species; and (3) an example of RNA-Seq data analysis using Omics Viewer tool. The protocols described here generally apply to other Pathway Tools-based PGDBs.

Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes.

PubMed

Fan, Wufeng; Zhou, Yuhan; Li, Hao

2017-01-01

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.

Pathview: an R/Bioconductor package for pathway-based data integration and visualization.

PubMed

Luo, Weijun; Brouwer, Cory

2013-07-15

Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. luo_weijun@yahoo.com Supplementary data are available at Bioinformatics online.

A System-Level Pathway-Phenotype Association Analysis Using Synthetic Feature Random Forest

PubMed Central

Pan, Qinxin; Hu, Ting; Malley, James D.; Andrew, Angeline S.; Karagas, Margaret R.; Moore, Jason H.

2015-01-01

As the cost of genome-wide genotyping decreases, the number of genome-wide association studies (GWAS) has increased considerably. However, the transition from GWAS findings to the underlying biology of various phenotypes remains challenging. As a result, due to its system-level interpretability, pathway analysis has become a popular tool for gaining insights on the underlying biology from high-throughput genetic association data. In pathway analyses, gene sets representing particular biological processes are tested for significant associations with a given phenotype. Most existing pathway analysis approaches rely on single-marker statistics and assume that pathways are independent of each other. As biological systems are driven by complex biomolecular interactions, embracing the complex relationships between single-nucleotide polymorphisms (SNPs) and pathways needs to be addressed. To incorporate the complexity of gene-gene interactions and pathway-pathway relationships, we propose a system-level pathway analysis approach, synthetic feature random forest (SF-RF), which is designed to detect pathway-phenotype associations without making assumptions about the relationships among SNPs or pathways. In our approach, the genotypes of SNPs in a particular pathway are aggregated into a synthetic feature representing that pathway via Random Forest (RF). Multiple synthetic features are analyzed using RF simultaneously and the significance of a synthetic feature indicates the significance of the corresponding pathway. We further complement SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis that evaluates interactions among pathways. By investigating the pathway SEN, we hope to gain additional insights into the genetic mechanisms contributing to the pathway-phenotype association. We apply SF-RF to a population-based genetic study of bladder cancer and further investigate the mechanisms that help explain the pathway-phenotype associations using SEN. The bladder cancer associated pathways we found are both consistent with existing biological knowledge and reveal novel and plausible hypotheses for future biological validations. PMID:24535726

minepath.org: a free interactive pathway analysis web server.

PubMed

Koumakis, Lefteris; Roussos, Panos; Potamias, George

2017-07-03

( www.minepath.org ) is a web-based platform that elaborates on, and radically extends the identification of differentially expressed sub-paths in molecular pathways. Besides the network topology, the underlying MinePath algorithmic processes exploit exact gene-gene molecular relationships (e.g. activation, inhibition) and are able to identify differentially expressed pathway parts. Each pathway is decomposed into all its constituent sub-paths, which in turn are matched with corresponding gene expression profiles. The highly ranked, and phenotype inclined sub-paths are kept. Apart from the pathway analysis algorithm, the fundamental innovation of the MinePath web-server concerns its advanced visualization and interactive capabilities. To our knowledge, this is the first pathway analysis server that introduces and offers visualization of the underlying and active pathway regulatory mechanisms instead of genes. Other features include live interaction, immediate visualization of functional sub-paths per phenotype and dynamic linked annotations for the engaged genes and molecular relations. The user can download not only the results but also the corresponding web viewer framework of the performed analysis. This feature provides the flexibility to immediately publish results without publishing source/expression data, and get all the functionality of a web based pathway analysis viewer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Correlation analysis of targeted proteins and metabolites to assess and engineer microbial isopentenol production.

PubMed

George, Kevin W; Chen, Amy; Jain, Aakriti; Batth, Tanveer S; Baidoo, Edward E K; Wang, George; Adams, Paul D; Petzold, Christopher J; Keasling, Jay D; Lee, Taek Soon

2014-08-01

The ability to rapidly assess and optimize heterologous pathway function is critical for effective metabolic engineering. Here, we develop a systematic approach to pathway analysis based on correlations between targeted proteins and metabolites and apply it to the microbial production of isopentenol, a promising biofuel. Starting with a seven-gene pathway, we performed a correlation analysis to reduce pathway complexity and identified two pathway proteins as the primary determinants of efficient isopentenol production. Aided by the targeted quantification of relevant pathway intermediates, we constructed and subsequently validated a conceptual model of isopentenol pathway function. Informed by our analysis, we assembled a strain which produced isopentenol at a titer 1.5 g/L, or 46% of theoretical yield. Our engineering approach allowed us to accurately identify bottlenecks and determine appropriate pathway balance. Paired with high-throughput cloning techniques and analytics, this strategy should prove useful for the analysis and optimization of increasingly complex heterologous pathways. © 2014 Wiley Periodicals, Inc.

Mathematical Justification of Expression-Based Pathway Activation Scoring (PAS).

PubMed

Aliper, Alexander M; Korzinkin, Michael B; Kuzmina, Natalia B; Zenin, Alexander A; Venkova, Larisa S; Smirnov, Philip Yu; Zhavoronkov, Alex A; Buzdin, Anton A; Borisov, Nikolay M

2017-01-01

Although modeling of activation kinetics for various cell signaling pathways has reached a high grade of sophistication and thoroughness, most such kinetic models still remain of rather limited practical value for biomedicine. Nevertheless, recent advancements have been made in application of signaling pathway science for real needs of prescription of the most effective drugs for individual patients. The methods for such prescription evaluate the degree of pathological changes in the signaling machinery based on two types of data: first, on the results of high-throughput gene expression profiling, and second, on the molecular pathway graphs that reflect interactions between the pathway members. For example, our algorithm OncoFinder evaluates the activation of molecular pathways on the basis of gene/protein expression data in the objects of the interest.Yet, the question of assessment of the relative importance for each gene product in a molecular pathway remains unclear unless one call for the methods of parameter sensitivity /stiffness analysis in the interactomic kinetic models of signaling pathway activation in terms of total concentrations of each gene product.Here we show two principal points: 1. First, the importance coefficients for each gene in pathways that were obtained using the extremely time- and labor-consuming stiffness analysis of full-scaled kinetic models generally differ from much easier-to-calculate expression-based pathway activation score (PAS) not more than by 30%, so the concept of PAS is kinetically justified. 2. Second, the use of pathway-based approach instead of distinct gene analysis, due to the law of large numbers, allows restoring the correlation between the similar samples that were examined using different transcriptome investigation techniques.

Analysis of differential gene expression by bead-based fiber-optic array in growth-hormone-secreting pituitary adenomas.

PubMed

Jiang, Zhiquan; Gui, Songbo; Zhang, Yazhuo

2010-09-01

Growth-hormone-secreting pituitary adenomas (GHomas) account for approximately 20% of all pituitary neoplasms. However, the pathogenesis of GHomas remains to be elucidated. To explore the possible pathogenesis of GHomas, we used bead-based fiber-optic arrays to examine the gene expression in five GHomas and compared them to three healthy pituitaries. Four differentially expressed genes were chosen randomly for validation by quantitative real-time reverse transcription-polymerase chain reaction. We then performed pathway analysis on the identified differentially expressed genes using the Kyoto Encyclopedia of Genes and Genomes. Array analysis showed significant increases in the expression of 353 genes and 206 expressed sequence tags (ESTs) and decreases in 565 genes and 29 ESTs. Bioinformatic analysis showed that the genes HIGD1B, HOXB2, ANGPT2, HPGD and BTG2 may play an important role in the tumorigenesis and progression of GHomas. Pathway analysis showed that the wingless-type signaling pathway and extracellular-matrix receptor interactions may play a key role in the tumorigenesis and progression of GHomas. Our data suggested that there are numerous aberrantly expressed genes and pathways involved in the pathogenesis of GHomas. Bead-based fiber-optic arrays combined with pathway analysis of differentially expressed genes appear to be a valid method for investigating the pathogenesis of tumors.

Analysis of differential gene expression by bead-based fiber-optic array in growth-hormone-secreting pituitary adenomas

PubMed Central

JIANG, ZHIQUAN; GUI, SONGBO; ZHANG, YAZHUO

2010-01-01

Growth-hormone-secreting pituitary adenomas (GHomas) account for approximately 20% of all pituitary neoplasms. However, the pathogenesis of GHomas remains to be elucidated. To explore the possible pathogenesis of GHomas, we used bead-based fiber-optic arrays to examine the gene expression in five GHomas and compared them to three healthy pituitaries. Four differentially expressed genes were chosen randomly for validation by quantitative real-time reverse transcription-polymerase chain reaction. We then performed pathway analysis on the identified differentially expressed genes using the Kyoto Encyclopedia of Genes and Genomes. Array analysis showed significant increases in the expression of 353 genes and 206 expressed sequence tags (ESTs) and decreases in 565 genes and 29 ESTs. Bioinformatic analysis showed that the genes HIGD1B, HOXB2, ANGPT2, HPGD and BTG2 may play an important role in the tumorigenesis and progression of GHomas. Pathway analysis showed that the wingless-type signaling pathway and extracellular-matrix receptor interactions may play a key role in the tumorigenesis and progression of GHomas. Our data suggested that there are numerous aberrantly expressed genes and pathways involved in the pathogenesis of GHomas. Bead-based fiber-optic arrays combined with pathway analysis of differentially expressed genes appear to be a valid method for investigating the pathogenesis of tumors. PMID:22993617

Metabolic changes associated with papillary thyroid carcinoma: A nuclear magnetic resonance-based metabolomics study.

PubMed

Li, Yanyun; Chen, Minjian; Liu, Cuiping; Xia, Yankai; Xu, Bo; Hu, Yanhui; Chen, Ting; Shen, Meiping; Tang, Wei

2018-05-01

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. Nuclear magnetic resonance (NMR)‑based metabolomic technique is the gold standard in metabolite structural elucidation, and can provide different coverage of information compared with other metabolomic techniques. Here, we firstly conducted NMR based metabolomics study regarding detailed metabolic changes especially metabolic pathway changes related to PTC pathogenesis. 1H NMR-based metabolomic technique was adopted in conju-nction with multivariate analysis to analyze matched tumor and normal thyroid tissues obtained from 16 patients. The results were further annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG), and Human Metabolome Database, and then were analyzed using modules of pathway analysis and enrichment analysis of MetaboAnalyst 3.0. Based on the analytical techniques, we established the models of principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS‑DA) which could discriminate PTC from normal thyroid tissue, and found 15 robust differentiated metabolites from two OPLS-DA models. We identified 8 KEGG pathways and 3 pathways of small molecular pathway database which were significantly related to PTC by using pathway analysis and enrichment analysis, respectively, through which we identified metabolisms related to PTC including branched chain amino acid metabolism (leucine and valine), other amino acid metabolism (glycine and taurine), glycolysis (lactate), tricarboxylic acid cycle (citrate), choline metabolism (choline, ethanolamine and glycerolphosphocholine) and lipid metabolism (very-low‑density lipoprotein and low-density lipoprotein). In conclusion, the PTC was characterized with increased glycolysis and inhibited tricarboxylic acid cycle, increased oncogenic amino acids as well as abnormal choline and lipid metabolism. The findings in this study provide new insights into detailed metabolic changes of PTC, and hold great potential in the treatment of PTC.

Tissue Non-Specific Genes and Pathways Associated with Diabetes: An Expression Meta-Analysis.

PubMed

Mei, Hao; Li, Lianna; Liu, Shijian; Jiang, Fan; Griswold, Michael; Mosley, Thomas

2017-01-21

We performed expression studies to identify tissue non-specific genes and pathways of diabetes by meta-analysis. We searched curated datasets of the Gene Expression Omnibus (GEO) database and identified 13 and five expression studies of diabetes and insulin responses at various tissues, respectively. We tested differential gene expression by empirical Bayes-based linear method and investigated gene set expression association by knowledge-based enrichment analysis. Meta-analysis by different methods was applied to identify tissue non-specific genes and gene sets. We also proposed pathway mapping analysis to infer functions of the identified gene sets, and correlation and independent analysis to evaluate expression association profile of genes and gene sets between studies and tissues. Our analysis showed that PGRMC1 and HADH genes were significant over diabetes studies, while IRS1 and MPST genes were significant over insulin response studies, and joint analysis showed that HADH and MPST genes were significant over all combined data sets. The pathway analysis identified six significant gene sets over all studies. The KEGG pathway mapping indicated that the significant gene sets are related to diabetes pathogenesis. The results also presented that 12.8% and 59.0% pairwise studies had significantly correlated expression association for genes and gene sets, respectively; moreover, 12.8% pairwise studies had independent expression association for genes, but no studies were observed significantly different for expression association of gene sets. Our analysis indicated that there are both tissue specific and non-specific genes and pathways associated with diabetes pathogenesis. Compared to the gene expression, pathway association tends to be tissue non-specific, and a common pathway influencing diabetes development is activated through different genes at different tissues.

A Method for Gene-Based Pathway Analysis Using Genomewide Association Study Summary Statistics Reveals Nine New Type 1 Diabetes Associations

PubMed Central

Evangelou, Marina; Smyth, Deborah J; Fortune, Mary D; Burren, Oliver S; Walker, Neil M; Guo, Hui; Onengut-Gumuscu, Suna; Chen, Wei-Min; Concannon, Patrick; Rich, Stephen S; Todd, John A; Wallace, Chris

2014-01-01

Pathway analysis can complement point-wise single nucleotide polymorphism (SNP) analysis in exploring genomewide association study (GWAS) data to identify specific disease-associated genes that can be candidate causal genes. We propose a straightforward methodology that can be used for conducting a gene-based pathway analysis using summary GWAS statistics in combination with widely available reference genotype data. We used this method to perform a gene-based pathway analysis of a type 1 diabetes (T1D) meta-analysis GWAS (of 7,514 cases and 9,045 controls). An important feature of the conducted analysis is the removal of the major histocompatibility complex gene region, the major genetic risk factor for T1D. Thirty-one of the 1,583 (2%) tested pathways were identified to be enriched for association with T1D at a 5% false discovery rate. We analyzed these 31 pathways and their genes to identify SNPs in or near these pathway genes that showed potentially novel association with T1D and attempted to replicate the association of 22 SNPs in additional samples. Replication P-values were skewed () with 12 of the 22 SNPs showing . Support, including replication evidence, was obtained for nine T1D associated variants in genes ITGB7 (rs11170466, ), NRP1 (rs722988, ), BAD (rs694739, ), CTSB (rs1296023, ), FYN (rs11964650, ), UBE2G1 (rs9906760, ), MAP3K14 (rs17759555, ), ITGB1 (rs1557150, ), and IL7R (rs1445898, ). The proposed methodology can be applied to other GWAS datasets for which only summary level data are available. PMID:25371288

Estimating pathway-specific contributions to biodegradation in aquifers based on dual isotope analysis: theoretical analysis and reactive transport simulations.

PubMed

Centler, Florian; Heße, Falk; Thullner, Martin

2013-09-01

At field sites with varying redox conditions, different redox-specific microbial degradation pathways contribute to total contaminant degradation. The identification of pathway-specific contributions to total contaminant removal is of high practical relevance, yet difficult to achieve with current methods. Current stable-isotope-fractionation-based techniques focus on the identification of dominant biodegradation pathways under constant environmental conditions. We present an approach based on dual stable isotope data to estimate the individual contributions of two redox-specific pathways. We apply this approach to carbon and hydrogen isotope data obtained from reactive transport simulations of an organic contaminant plume in a two-dimensional aquifer cross section to test the applicability of the method. To take aspects typically encountered at field sites into account, additional simulations addressed the effects of transverse mixing, diffusion-induced stable-isotope fractionation, heterogeneities in the flow field, and mixing in sampling wells on isotope-based estimates for aerobic and anaerobic pathway contributions to total contaminant biodegradation. Results confirm the general applicability of the presented estimation method which is most accurate along the plume core and less accurate towards the fringe where flow paths receive contaminant mass and associated isotope signatures from the core by transverse dispersion. The presented method complements the stable-isotope-fractionation-based analysis toolbox. At field sites with varying redox conditions, it provides a means to identify the relative importance of individual, redox-specific degradation pathways. © 2013.

Identification of metabolic pathways using pathfinding approaches: a systematic review.

PubMed

Abd Algfoor, Zeyad; Shahrizal Sunar, Mohd; Abdullah, Afnizanfaizal; Kolivand, Hoshang

2017-03-01

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in Omics studies and "Big data" biology.

PubMed

Vivar, Juan C; Pemu, Priscilla; McPherson, Ruth; Ghosh, Sujoy

2013-08-01

Abstract Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association to obesity compared to pathways identified from the original databases.

Pathway-GPS and SIGORA: identifying relevant pathways based on the over-representation of their gene-pair signatures

PubMed Central

Foroushani, Amir B.K.; Brinkman, Fiona S.L.

2013-01-01

Motivation. Predominant pathway analysis approaches treat pathways as collections of individual genes and consider all pathway members as equally informative. As a result, at times spurious and misleading pathways are inappropriately identified as statistically significant, solely due to components that they share with the more relevant pathways. Results. We introduce the concept of Pathway Gene-Pair Signatures (Pathway-GPS) as pairs of genes that, as a combination, are specific to a single pathway. We devised and implemented a novel approach to pathway analysis, Signature Over-representation Analysis (SIGORA), which focuses on the statistically significant enrichment of Pathway-GPS in a user-specified gene list of interest. In a comparative evaluation of several published datasets, SIGORA outperformed traditional methods by delivering biologically more plausible and relevant results. Availability. An efficient implementation of SIGORA, as an R package with precompiled GPS data for several human and mouse pathway repositories is available for download from http://sigora.googlecode.com/svn/. PMID:24432194

Investigating a multigene prognostic assay based on significant pathways for Luminal A breast cancer through gene expression profile analysis.

PubMed

Gao, Haiyan; Yang, Mei; Zhang, Xiaolan

2018-04-01

The present study aimed to investigate potential recurrence-risk biomarkers based on significant pathways for Luminal A breast cancer through gene expression profile analysis. Initially, the gene expression profiles of Luminal A breast cancer patients were downloaded from The Cancer Genome Atlas database. The differentially expressed genes (DEGs) were identified using a Limma package and the hierarchical clustering analysis was conducted for the DEGs. In addition, the functional pathways were screened using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and rank ratio calculation. The multigene prognostic assay was exploited based on the statistically significant pathways and its prognostic function was tested using train set and verified using the gene expression data and survival data of Luminal A breast cancer patients downloaded from the Gene Expression Omnibus. A total of 300 DEGs were identified between good and poor outcome groups, including 176 upregulated genes and 124 downregulated genes. The DEGs may be used to effectively distinguish Luminal A samples with different prognoses verified by hierarchical clustering analysis. There were 9 pathways screened as significant pathways and a total of 18 DEGs involved in these 9 pathways were identified as prognostic biomarkers. According to the survival analysis and receiver operating characteristic curve, the obtained 18-gene prognostic assay exhibited good prognostic function with high sensitivity and specificity to both the train and test samples. In conclusion the 18-gene prognostic assay including the key genes, transcription factor 7-like 2, anterior parietal cortex and lymphocyte enhancer factor-1 may provide a new method for predicting outcomes and may be conducive to the promotion of precision medicine for Luminal A breast cancer.

User-centered evaluation of Arizona BioPathway: an information extraction, integration, and visualization system.

PubMed

Quiñones, Karin D; Su, Hua; Marshall, Byron; Eggers, Shauna; Chen, Hsinchun

2007-09-01

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This paper presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature-viewing method. Relation aggregation significantly contributes to knowledge-acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multiview, relation-based interface that supports user-controlled exploration of pathway information across multiple granularities.

KEGGParser: parsing and editing KEGG pathway maps in Matlab.

PubMed

Arakelyan, Arsen; Nersisyan, Lilit

2013-02-15

KEGG pathway database is a collection of manually drawn pathway maps accompanied with KGML format files intended for use in automatic analysis. KGML files, however, do not contain the required information for complete reproduction of all the events indicated in the static image of a pathway map. Several parsers and editors of KEGG pathways exist for processing KGML files. We introduce KEGGParser-a MATLAB based tool for KEGG pathway parsing, semiautomatic fixing, editing, visualization and analysis in MATLAB environment. It also works with Scilab. The source code is available at http://www.mathworks.com/matlabcentral/fileexchange/37561.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data

PubMed Central

Yi, Ming; Horton, Jay D; Cohen, Jonathan C; Hobbs, Helen H; Stephens, Robert M

2006-01-01

Background Analysis of High Throughput (HTP) Data such as microarray and proteomics data has provided a powerful methodology to study patterns of gene regulation at genome scale. A major unresolved problem in the post-genomic era is to assemble the large amounts of data generated into a meaningful biological context. We have developed a comprehensive software tool, WholePathwayScope (WPS), for deriving biological insights from analysis of HTP data. Result WPS extracts gene lists with shared biological themes through color cue templates. WPS statistically evaluates global functional category enrichment of gene lists and pathway-level pattern enrichment of data. WPS incorporates well-known biological pathways from KEGG (Kyoto Encyclopedia of Genes and Genomes) and Biocarta, GO (Gene Ontology) terms as well as user-defined pathways or relevant gene clusters or groups, and explores gene-term relationships within the derived gene-term association networks (GTANs). WPS simultaneously compares multiple datasets within biological contexts either as pathways or as association networks. WPS also integrates Genetic Association Database and Partial MedGene Database for disease-association information. We have used this program to analyze and compare microarray and proteomics datasets derived from a variety of biological systems. Application examples demonstrated the capacity of WPS to significantly facilitate the analysis of HTP data for integrative discovery. Conclusion This tool represents a pathway-based platform for discovery integration to maximize analysis power. The tool is freely available at . PMID:16423281

Prioritizing biological pathways by recognizing context in time-series gene expression data.

PubMed

Lee, Jusang; Jo, Kyuri; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

2016-12-23

The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP .

Candidate genetic pathways for attention-deficit/hyperactivity disorder (ADHD) show association to hyperactive/impulsive symptoms in children with ADHD.

PubMed

Bralten, Janita; Franke, Barbara; Waldman, Irwin; Rommelse, Nanda; Hartman, Catharina; Asherson, Philip; Banaschewski, Tobias; Ebstein, Richard P; Gill, Michael; Miranda, Ana; Oades, Robert D; Roeyers, Herbert; Rothenberger, Aribert; Sergeant, Joseph A; Oosterlaan, Jaap; Sonuga-Barke, Edmund; Steinhausen, Hans-Christoph; Faraone, Stephen V; Buitelaar, Jan K; Arias-Vásquez, Alejandro

2013-11-01

Because multiple genes with small effect sizes are assumed to play a role in attention-deficit/hyperactivity disorder (ADHD) etiology, considering multiple variants within the same analysis likely increases the total explained phenotypic variance, thereby boosting the power of genetic studies. This study investigated whether pathway-based analysis could bring scientists closer to unraveling the biology of ADHD. The pathway was described as a predefined gene selection based on a well-established database or literature data. Common genetic variants in pathways involved in dopamine/norepinephrine and serotonin neurotransmission and genes involved in neuritic outgrowth were investigated in cases from the International Multicentre ADHD Genetics (IMAGE) study. Multivariable analysis was performed to combine the effects of single genetic variants within the pathway genes. Phenotypes were DSM-IV symptom counts for inattention and hyperactivity/impulsivity (n = 871) and symptom severity measured with the Conners Parent (n = 930) and Teacher (n = 916) Rating Scales. Summing genetic effects of common genetic variants within the pathways showed a significant association with hyperactive/impulsive symptoms ((p)empirical = .007) but not with inattentive symptoms ((p)empirical = .73). Analysis of parent-rated Conners hyperactive/impulsive symptom scores validated this result ((p)empirical = .0018). Teacher-rated Conners scores were not associated. Post hoc analyses showed a significant contribution of all pathways to the hyperactive/impulsive symptom domain (dopamine/norepinephrine, (p)empirical = .0004; serotonin, (p)empirical = .0149; neuritic outgrowth, (p)empirical = .0452). The present analysis shows an association between common variants in 3 genetic pathways and the hyperactive/impulsive component of ADHD. This study demonstrates that pathway-based association analyses, using quantitative measurements of ADHD symptom domains, can increase the power of genetic analyses to identify biological risk factors involved in this disorder. Copyright © 2013 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network

PubMed Central

RUAN, XIYUN; LI, HONGYUN; LIU, BO; CHEN, JIE; ZHANG, SHIBAO; SUN, ZEQIANG; LIU, SHUANGQING; SUN, FAHAI; LIU, QINGYONG

2015-01-01

The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson’s correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson’s correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis. PMID:26058425

User Interface Requirements for Web-Based Integrated Care Pathways: Evidence from the Evaluation of an Online Care Pathway Investigation Tool.

PubMed

Balatsoukas, Panos; Williams, Richard; Davies, Colin; Ainsworth, John; Buchan, Iain

2015-11-01

Integrated care pathways (ICPs) define a chronological sequence of steps, most commonly diagnostic or treatment, to be followed in providing care for patients. Care pathways help to ensure quality standards are met and to reduce variation in practice. Although research on the computerisation of ICP progresses, there is still little knowledge on what are the requirements for designing user-friendly and usable electronic care pathways, or how users (normally health care professionals) interact with interfaces that support design, analysis and visualisation of ICPs. The purpose of the study reported in this paper was to address this gap by evaluating the usability of a novel web-based tool called COCPIT (Collaborative Online Care Pathway Investigation Tool). COCPIT supports the design, analysis and visualisation of ICPs at the population level. In order to address the aim of this study, an evaluation methodology was designed based on heuristic evaluations and a mixed method usability test. The results showed that modular visualisation and direct manipulation of information related to the design and analysis of ICPs is useful for engaging and stimulating users. However, designers should pay attention to issues related to the visibility of the system status and the match between the system and the real world, especially in relation to the display of statistical information about care pathways and the editing of clinical information within a care pathway. The paper concludes with recommendations for interface design.

Pathway analysis of genome-wide association study data highlights pancreatic development genes as susceptibility factors for pancreatic cancer

PubMed Central

Duell, Eric J.; Yu, Kai; Risch, Harvey A.; Olson, Sara H.; Kooperberg, Charles; Wolpin, Brian M.; Jiao, Li; Dong, Xiaoqun; Wheeler, Bill; Arslan, Alan A.; Bueno-de-Mesquita, H. Bas; Fuchs, Charles S.; Gallinger, Steven; Gross, Myron; Hartge, Patricia; Hoover, Robert N.; Holly, Elizabeth A.; Jacobs, Eric J.; Klein, Alison P.; LaCroix, Andrea; Mandelson, Margaret T.; Petersen, Gloria; Zheng, Wei; Agalliu, Ilir; Albanes, Demetrius; Boutron-Ruault, Marie-Christine; Bracci, Paige M.; Buring, Julie E.; Canzian, Federico; Chang, Kenneth; Chanock, Stephen J.; Cotterchio, Michelle; Gaziano, J.Michael; Giovannucci, Edward L.; Goggins, Michael; Hallmans, Göran; Hankinson, Susan E.; Hoffman Bolton, Judith A.; Hunter, David J.; Hutchinson, Amy; Jacobs, Kevin B.; Jenab, Mazda; Khaw, Kay-Tee; Kraft, Peter; Krogh, Vittorio; Kurtz, Robert C.; McWilliams, Robert R.; Mendelsohn, Julie B.; Patel, Alpa V.; Rabe, Kari G.; Riboli, Elio; Shu, Xiao-Ou; Tjønneland, Anne; Tobias, Geoffrey S.; Trichopoulos, Dimitrios; Virtamo, Jarmo; Visvanathan, Kala; Watters, Joanne; Yu, Herbert; Zeleniuch-Jacquotte, Anne; Stolzenberg-Solomon, Rachael Z.

2012-01-01

Four loci have been associated with pancreatic cancer through genome-wide association studies (GWAS). Pathway-based analysis of GWAS data is a complementary approach to identify groups of genes or biological pathways enriched with disease-associated single-nucleotide polymorphisms (SNPs) whose individual effect sizes may be too small to be detected by standard single-locus methods. We used the adaptive rank truncated product method in a pathway-based analysis of GWAS data from 3851 pancreatic cancer cases and 3934 control participants pooled from 12 cohort studies and 8 case–control studies (PanScan). We compiled 23 biological pathways hypothesized to be relevant to pancreatic cancer and observed a nominal association between pancreatic cancer and five pathways (P < 0.05), i.e. pancreatic development, Helicobacter pylori lacto/neolacto, hedgehog, Th1/Th2 immune response and apoptosis (P = 2.0 × 10−6, 1.6 × 10−5, 0.0019, 0.019 and 0.023, respectively). After excluding previously identified genes from the original GWAS in three pathways (NR5A2, ABO and SHH), the pancreatic development pathway remained significant (P = 8.3 × 10−5), whereas the others did not. The most significant genes (P < 0.01) in the five pathways were NR5A2, HNF1A, HNF4G and PDX1 for pancreatic development; ABO for H. pylori lacto/neolacto; SHH for hedgehog; TGFBR2 and CCL18 for Th1/Th2 immune response and MAPK8 and BCL2L11 for apoptosis. Our results provide a link between inherited variation in genes important for pancreatic development and cancer and show that pathway-based approaches to analysis of GWAS data can yield important insights into the collective role of genetic risk variants in cancer. PMID:22523087

Pathway-Based Genome-Wide Association Studies for Two Meat Production Traits in Simmental Cattle.

PubMed

Fan, Huizhong; Wu, Yang; Zhou, Xiaojing; Xia, Jiangwei; Zhang, Wengang; Song, Yuxin; Liu, Fei; Chen, Yan; Zhang, Lupei; Gao, Xue; Gao, Huijiang; Li, Junya

2015-12-17

Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

PubMed

Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

2018-08-01

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Heading in the right direction: thermodynamics-based network analysis and pathway engineering.

PubMed

Ataman, Meric; Hatzimanikatis, Vassily

2015-12-01

Thermodynamics-based network analysis through the introduction of thermodynamic constraints in metabolic models allows a deeper analysis of metabolism and guides pathway engineering. The number and the areas of applications of thermodynamics-based network analysis methods have been increasing in the last ten years. We review recent applications of these methods and we identify the areas that such analysis can contribute significantly, and the needs for future developments. We find that organisms with multiple compartments and extremophiles present challenges for modeling and thermodynamics-based flux analysis. The evolution of current and new methods must also address the issues of the multiple alternatives in flux directionalities and the uncertainties and partial information from analytical methods. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Convergent evidence from systematic analysis of GWAS revealed genetic basis of esophageal cancer.

PubMed

Gao, Xue-Xin; Gao, Lei; Wang, Jiu-Qiang; Qu, Su-Su; Qu, Yue; Sun, Hong-Lei; Liu, Si-Dang; Shang, Ying-Li

2016-07-12

Recent genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with risk of esophageal cancer (EC). However, investigation of genetic basis from the perspective of systematic biology and integrative genomics remains scarce.In this study, we explored genetic basis of EC based on GWAS data and implemented a series of bioinformatics methods including functional annotation, expression quantitative trait loci (eQTL) analysis, pathway enrichment analysis and pathway grouped network analysis.Two hundred and thirteen risk SNPs were identified, in which 44 SNPs were found to have significantly differential gene expression in esophageal tissues by eQTL analysis. By pathway enrichment analysis, 170 risk genes mapped by risk SNPs were enriched into 38 significant GO terms and 17 significant KEGG pathways, which were significantly grouped into 9 sub-networks by pathway grouped network analysis. The 9 groups of interconnected pathways were mainly involved with muscle cell proliferation, cellular response to interleukin-6, cell adhesion molecules, and ethanol oxidation, which might participate in the development of EC.Our findings provide genetic evidence and new insight for exploring the molecular mechanisms of EC.

Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways.

PubMed

Liu, Guiyou; Zhang, Fang; Jiang, Yongshuai; Hu, Yang; Gong, Zhongying; Liu, Shoufeng; Chen, Xiuju; Jiang, Qinghua; Hao, Junwei

2017-02-01

Much effort has been expended on identifying the genetic determinants of multiple sclerosis (MS). Existing large-scale genome-wide association study (GWAS) datasets provide strong support for using pathway and network-based analysis methods to investigate the mechanisms underlying MS. However, no shared genetic pathways have been identified to date. We hypothesize that shared genetic pathways may indeed exist in different MS-GWAS datasets. Here, we report results from a three-stage analysis of GWAS and expression datasets. In stage 1, we conducted multiple pathway analyses of two MS-GWAS datasets. In stage 2, we performed a candidate pathway analysis of the large-scale MS-GWAS dataset. In stage 3, we performed a pathway analysis using the dysregulated MS gene list from seven human MS case-control expression datasets. In stage 1, we identified 15 shared pathways. In stage 2, we successfully replicated 14 of these 15 significant pathways. In stage 3, we found that dysregulated MS genes were significantly enriched in 10 of 15 MS risk pathways identified in stages 1 and 2. We report shared genetic pathways in different MS-GWAS datasets and highlight some new MS risk pathways. Our findings provide new insights on the genetic determinants of MS.

Deriving pathway maps from automated text analysis using a grammar-based approach.

PubMed

Olsson, Björn; Gawronska, Barbara; Erlendsson, Björn

2006-04-01

We demonstrate how automated text analysis can be used to support the large-scale analysis of metabolic and regulatory pathways by deriving pathway maps from textual descriptions found in the scientific literature. The main assumption is that correct syntactic analysis combined with domain-specific heuristics provides a good basis for relation extraction. Our method uses an algorithm that searches through the syntactic trees produced by a parser based on a Referent Grammar formalism, identifies relations mentioned in the sentence, and classifies them with respect to their semantic class and epistemic status (facts, counterfactuals, hypotheses). The semantic categories used in the classification are based on the relation set used in KEGG (Kyoto Encyclopedia of Genes and Genomes), so that pathway maps using KEGG notation can be automatically generated. We present the current version of the relation extraction algorithm and an evaluation based on a corpus of abstracts obtained from PubMed. The results indicate that the method is able to combine a reasonable coverage with high accuracy. We found that 61% of all sentences were parsed, and 97% of the parse trees were judged to be correct. The extraction algorithm was tested on a sample of 300 parse trees and was found to produce correct extractions in 90.5% of the cases.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

PubMed

Nam, Seungyoon

2017-04-01

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data.

PubMed

Hernández-de-Diego, Rafael; Tarazona, Sonia; Martínez-Mira, Carlos; Balzano-Nogueira, Leandro; Furió-Tarí, Pedro; Pappas, Georgios J; Conesa, Ana

2018-05-25

The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org.

Detecting Disease Specific Pathway Substructures through an Integrated Systems Biology Approach

PubMed Central

Alaimo, Salvatore; Marceca, Gioacchino Paolo; Ferro, Alfredo; Pulvirenti, Alfredo

2017-01-01

In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states. PMID:29657291

Association genetics and transcriptome analysis reveal a gibberellin-responsive pathway involved in regulating photosynthesis.

PubMed

Xie, Jianbo; Tian, Jiaxing; Du, Qingzhang; Chen, Jinhui; Li, Ying; Yang, Xiaohui; Li, Bailian; Zhang, Deqiang

2016-05-01

Gibberellins (GAs) regulate a wide range of important processes in plant growth and development, including photosynthesis. However, the mechanism by which GAs regulate photosynthesis remains to be understood. Here, we used multi-gene association to investigate the effect of genes in the GA-responsive pathway, as constructed by RNA sequencing, on photosynthesis, growth, and wood property traits, in a population of 435 Populus tomentosa By analyzing changes in the transcriptome following GA treatment, we identified many key photosynthetic genes, in agreement with the observed increase in measurements of photosynthesis. Regulatory motif enrichment analysis revealed that 37 differentially expressed genes related to photosynthesis shared two essential GA-related cis-regulatory elements, the GA response element and the pyrimidine box. Thus, we constructed a GA-responsive pathway consisting of 47 genes involved in regulating photosynthesis, including GID1, RGA, GID2, MYBGa, and 37 photosynthetic differentially expressed genes. Single nucleotide polymorphism (SNP)-based association analysis showed that 142 SNPs, representing 40 candidate genes in this pathway, were significantly associated with photosynthesis, growth, and wood property traits. Epistasis analysis uncovered interactions between 310 SNP-SNP pairs from 37 genes in this pathway, revealing possible genetic interactions. Moreover, a structural gene-gene matrix based on a time-course of transcript abundances provided a better understanding of the multi-gene pathway affecting photosynthesis. The results imply a functional role for these genes in mediating photosynthesis, growth, and wood properties, demonstrating the potential of combining transcriptome-based regulatory pathway construction and genetic association approaches to detect the complex genetic networks underlying quantitative traits. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

RNA sequencing-based longitudinal transcriptomic profiling gives novel insights into the disease mechanism of generalized pustular psoriasis.

PubMed

Wang, Lingyan; Yu, Xiaoling; Wu, Chao; Zhu, Teng; Wang, Wenming; Zheng, Xiaofeng; Jin, Hongzhong

2018-06-05

Generalized pustular psoriasis (GPP) is a rare, episodic, potentially life-threatening inflammatory disease. However, the pathogenesis of GPP, and universally accepted therapies for treating it, remain undefined. To better understand the disease mechanism of GPP, we performed a transcriptome analysis to profile the gene expression of peripheral blood mononuclear cells (PBMCs) from patients enrolled at the time of diagnosis and receiving follow-up treatment for up to 6 months. RNA sequencing data revealed that gene expression in five GPP patients' PBMCs was profoundly altered following acitretin treatment. Differentially expressed gene (DEG) analysis suggested that genes related to psoriatic inflammation, including CXCL1, CXCL8 (IL-8), S100A8, S100A9, S100A12 and LCN2, were significantly downregulated in patients in remission from GPP. Functional enrichment and annotation analysis unveiled a cluster of DEGs significantly associated with the function of leukocytes, particularly neutrophils. Pathway analysis suggested that a variety of pro-inflammatory pathways were inhibited in patients in remission. This analysis not only reaffirmed known signaling pathways in GPP pathogenesis, but also implicated novel factors and pathways, such as cell cycle regulation pathways. Furthermore, regulator network analysis provided bioinformatics-based support for upstream molecules as potential therapeutic targets such as oncostatin M. This longitudinal analysis of blood transcriptomes provides the first evidence that dysregulated gene expression in peripheral blood may significantly contribute to psoriatic inflammation in GPP patients. Novel canonical pathways and biomarkers identified in the current research may provide insights to help understand GPP pathobiology and advance novel therapeutics.

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults.

PubMed

Nho, Kwangsik; Ramanan, Vijay K; Horgusluoglu, Emrin; Kim, Sungeun; Inlow, Mark H; Risacher, Shannon L; McDonald, Brenna C; Farlow, Martin R; Foroud, Tatiana M; Gao, Sujuan; Callahan, Christopher M; Hendrie, Hugh C; Niculescu, Alexander B; Saykin, Andrew J

2015-01-01

Depressive symptoms are common in older adults and are particularly prevalent in those with or at elevated risk for dementia. Although the heritability of depression is estimated to be substantial, single nucleotide polymorphism-based genome-wide association studies of depressive symptoms have had limited success. In this study, we performed genome-wide gene- and pathway-based analyses of depressive symptom burden. Study participants included non-Hispanic Caucasian subjects (n = 6,884) from three independent cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Health and Retirement Study (HRS), and the Indiana Memory and Aging Study (IMAS). Gene-based meta-analysis identified genome-wide significant associations (ANGPT4 and FAM110A, q-value = 0.026; GRM7-AS3 and LRFN5, q-value = 0.042). Pathway analysis revealed enrichment of association in 105 pathways, including multiple pathways related to ERK/MAPK signaling, GSK3 signaling in bipolar disorder, cell development, and immune activation and inflammation. GRM7, ANGPT4, and LRFN5 have been previously implicated in psychiatric disorders, including the GRM7 region displaying association with major depressive disorder. The ERK/MAPK signaling pathway is a known target of antidepressant drugs and has important roles in neuronal plasticity, and GSK3 signaling has been previously implicated in Alzheimer's disease and as a promising therapeutic target for depression. Our results warrant further investigation in independent and larger cohorts and add to the growing understanding of the genetics and pathobiology of depressive symptoms in aging and neurodegenerative disorders. In particular, the genes and pathways demonstrating association with depressive symptoms may be potential therapeutic targets for these symptoms in older adults.

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

Plant MetGenMAP: an integrative analysis system for plant systems biology

USDA-ARS?s Scientific Manuscript database

We have developed a web-based system, Plant MetGenMAP, which can identify significantly altered biochemical pathways and highly affected biological processes, predict functional roles of pathway genes, and potential pathway-related regulatory motifs from transcript and metabolite profile datasets. P...

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights

PubMed Central

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-01

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher’s exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO’s usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher. PMID:26750448

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

PubMed

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome

PubMed Central

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-01-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS. PMID:28949383

PathwaySplice: An R package for unbiased pathway analysis of alternative splicing in RNA-Seq data.

PubMed

Yan, Aimin; Ban, Yuguang; Gao, Zhen; Chen, Xi; Wang, Lily

2018-04-24

Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the "significant" gene list in alternative splicing. We present PathwaySplice, an R package that (1) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene; (2) Visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression; (3) Supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets; (4) Identifies the significant genes driving pathway significance and (5) Organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph. https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html. lily.wangg@gmail.com, xi.steven.chen@gmail.com.

Pathway Distiller - multisource biological pathway consolidation

PubMed Central

2012-01-01

Background One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. Methods After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. Results We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. Conclusions By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments. PMID:23134636

Pathway Distiller - multisource biological pathway consolidation.

PubMed

Doderer, Mark S; Anguiano, Zachry; Suresh, Uthra; Dashnamoorthy, Ravi; Bishop, Alexander J R; Chen, Yidong

2012-01-01

One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments.

Pathways to Medical Home Recognition: A Qualitative Comparative Analysis of the PCMH Transformation Process.

PubMed

Mendel, Peter; Chen, Emily K; Green, Harold D; Armstrong, Courtney; Timbie, Justin W; Kress, Amii M; Friedberg, Mark W; Kahn, Katherine L

2017-12-15

To understand the process of practice transformation by identifying pathways for attaining patient-centered medical home (PCMH) recognition. The CMS Federally Qualified Health Center (FQHC) Advanced Primary Care Practice Demonstration was designed to help FQHCs achieve NCQA Level 3 PCMH recognition and improve patient outcomes. We used a stratified random sample of 20 (out of 503) participating sites for this analysis. We developed a conceptual model of structural, cultural, and implementation factors affecting PCMH transformation based on literature and initial qualitative interview themes. We then used conventional cross-case analysis, followed by qualitative comparative analysis (QCA), a cross-case method based on Boolean logic algorithms, to systematically identify pathways (i.e., combinations of factors) associated with attaining-or not attaining-Level 3 recognition. Site-level indicators were derived from semistructured interviews with site leaders at two points in time (mid- and late-implementation) and administrative data collected prior to and during the demonstration period. The QCA results identified five distinct pathways to attaining PCMH recognition and four distinct pathways to not attaining recognition by the end of the demonstration. Across these pathways, one condition (change leader capacity) was common to all pathways for attaining recognition, and another (previous improvement or recognition experience) was absent in all pathways for not attaining recognition. In general, sites could compensate for deficiencies in one factor with capacity in others, but they needed a threshold of strengths in cultural and implementation factors to attain PCMH recognition. Future efforts at primary care transformation should take into account multiple pathways sites may pursue. Sites should be assessed on key cultural and implementation factors, in addition to structural components, in order to differentiate interventions and technical assistance. © Health Research and Educational Trust.

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways

NASA Astrophysics Data System (ADS)

Yang, Yunlai; Arouri, Khaled

2016-03-01

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways

PubMed Central

Yang, Yunlai; Arouri, Khaled

2016-01-01

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations. PMID:26965479

A Simple Geotracer Compositional Correlation Analysis Reveals Oil Charge and Migration Pathways.

PubMed

Yang, Yunlai; Arouri, Khaled

2016-03-11

A novel approach, based on geotracer compositional correlation analysis is reported, which reveals the oil charge sequence and migration pathways for five oil fields in Saudi Arabia. The geotracers utilised are carbazoles, a family of neutral pyrrolic nitrogen compounds known to occur naturally in crude oils. The approach is based on the concept that closely related fields, with respect to filling sequence, will show a higher carbazole compositional correlation, than those fields that are less related. That is, carbazole compositional correlation coefficients can quantify the charge and filling relationships among different fields. Consequently, oil migration pathways can be defined based on the established filling relationships. The compositional correlation coefficients of isomers of C1 and C2 carbazoles, and benzo[a]carbazole for all different combination pairs of the five fields were found to vary extremely widely (0.28 to 0.94). A wide range of compositional correlation coefficients allows adequate differentiation of separate filling relationships. Based on the established filling relationships, three distinct migration pathways were inferred, with each apparently being charged from a different part of a common source kitchen. The recognition of these charge and migration pathways will greatly aid the search for new accumulations.

Interaction of Herbal Compounds with Biological Targets: A Case Study with Berberine

PubMed Central

Chen, Xiao-Wu; Di, Yuan Ming; Zhang, Jian; Zhou, Zhi-Wei; Li, Chun Guang; Zhou, Shu-Feng

2012-01-01

Berberine is one of the main alkaloids found in the Chinese herb Huang lian (Rhizoma Coptidis), which has been reported to have multiple pharmacological activities. This study aimed to analyze the molecular targets of berberine based on literature data followed by a pathway analysis using the PANTHER program. PANTHER analysis of berberine targets showed that the most classes of molecular functions include receptor binding, kinase activity, protein binding, transcription activity, DNA binding, and kinase regulator activity. Based on the biological process classification of in vitro berberine targets, those targets related to signal transduction, intracellular signalling cascade, cell surface receptor-linked signal transduction, cell motion, cell cycle control, immunity system process, and protein metabolic process are most frequently involved. In addition, berberine was found to interact with a mixture of biological pathways, such as Alzheimer's disease-presenilin and -secretase pathways, angiogenesis, apoptosis signalling pathway, FAS signalling pathway, Hungtington disease, inflammation mediated by chemokine and cytokine signalling pathways, interleukin signalling pathway, and p53 pathways. We also explored the possible mechanism of action for the anti-diabetic effect of berberine. Further studies are warranted to elucidate the mechanisms of action of berberine using systems biology approach. PMID:23213296

A method for gene-based pathway analysis using genomewide association study summary statistics reveals nine new type 1 diabetes associations.

PubMed

Evangelou, Marina; Smyth, Deborah J; Fortune, Mary D; Burren, Oliver S; Walker, Neil M; Guo, Hui; Onengut-Gumuscu, Suna; Chen, Wei-Min; Concannon, Patrick; Rich, Stephen S; Todd, John A; Wallace, Chris

2014-12-01

Pathway analysis can complement point-wise single nucleotide polymorphism (SNP) analysis in exploring genomewide association study (GWAS) data to identify specific disease-associated genes that can be candidate causal genes. We propose a straightforward methodology that can be used for conducting a gene-based pathway analysis using summary GWAS statistics in combination with widely available reference genotype data. We used this method to perform a gene-based pathway analysis of a type 1 diabetes (T1D) meta-analysis GWAS (of 7,514 cases and 9,045 controls). An important feature of the conducted analysis is the removal of the major histocompatibility complex gene region, the major genetic risk factor for T1D. Thirty-one of the 1,583 (2%) tested pathways were identified to be enriched for association with T1D at a 5% false discovery rate. We analyzed these 31 pathways and their genes to identify SNPs in or near these pathway genes that showed potentially novel association with T1D and attempted to replicate the association of 22 SNPs in additional samples. Replication P-values were skewed (P=9.85×10-11) with 12 of the 22 SNPs showing P<0.05. Support, including replication evidence, was obtained for nine T1D associated variants in genes ITGB7 (rs11170466, P=7.86×10-9), NRP1 (rs722988, 4.88×10-8), BAD (rs694739, 2.37×10-7), CTSB (rs1296023, 2.79×10-7), FYN (rs11964650, P=5.60×10-7), UBE2G1 (rs9906760, 5.08×10-7), MAP3K14 (rs17759555, 9.67×10-7), ITGB1 (rs1557150, 1.93×10-6), and IL7R (rs1445898, 2.76×10-6). The proposed methodology can be applied to other GWAS datasets for which only summary level data are available. © 2014 The Authors. ** Genetic Epidemiology published by Wiley Periodicals, Inc.

Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network.

PubMed

Song, Xian-Dong; Song, Xian-Xu; Liu, Gui-Bo; Ren, Chun-Hui; Sun, Yuan-Bo; Liu, Ke-Xin; Liu, Bo; Liang, Shuang; Zhu, Zhu

2018-03-01

The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.

On-line metabolic pathway analysis based on metabolic signal flow diagram.

PubMed

Shi, H; Shimizu, K

In this work, an integrated modeling approach based on a metabolic signal flow diagram and cellular energetics was used to model the metabolic pathway analysis for the cultivation of yeast on glucose. This approach enables us to make a clear analysis of the flow direction of the carbon fluxes in the metabolic pathways as well as of the degree of activation of a particular pathway for the synthesis of biomaterials for cell growth. The analyses demonstrate that the main metabolic pathways of Saccharomyces cerevisiae change significantly during batch culture. Carbon flow direction is toward glycolysis to satisfy the increase of requirement for precursors and energy. The enzymatic activation of TCA cycle seems to always be at normal level, which may result in the overflow of ethanol due to its limited capacity. The advantage of this approach is that it adopts both virtues of the metabolic signal flow diagram and the simple network analysis method, focusing on the investigation of the flow directions of carbon fluxes and the degree of activation of a particular pathway or reaction loop. All of the variables used in the model equations were determined on-line; the information obtained from the calculated metabolic coefficients may result in a better understanding of cell physiology and help to evaluate the state of the cell culture process. Copyright 1998 John Wiley & Sons, Inc.

Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - part A.

PubMed

Sinha, Shriprakash

2017-12-04

Ever since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials, augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues. This manuscript ∙ explores the strength of contributing factors in the signaling pathway, ∙ analyzes the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and ∙ investigates the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices. The results show the advantage of using density based indices over variance based indices mainly due to the former's employment of distance measures & the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis & representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs & reveal hidden biological information within colorectal cancer samples.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

PubMed Central

Koumakis, Lefteris; Kartsaki, Evgenia; Chatzimina, Maria; Zervakis, Michalis; Vassou, Despoina; Marias, Kostas; Moustakis, Vassilis; Potamias, George

2016-01-01

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes. PMID:27832067

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.

PubMed

Koumakis, Lefteris; Kanterakis, Alexandros; Kartsaki, Evgenia; Chatzimina, Maria; Zervakis, Michalis; Tsiknakis, Manolis; Vassou, Despoina; Kafetzopoulos, Dimitris; Marias, Kostas; Moustakis, Vassilis; Potamias, George

2016-11-01

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Mining disease fingerprints from within genetic pathways.

PubMed

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.

Mining Disease Fingerprints From Within Genetic Pathways

PubMed Central

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components (‘fingerprints’) of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ∼77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways. PMID:23304411

Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.

PubMed

Shchetynsky, Klementy; Diaz-Gallo, Lina-Marcella; Folkersen, Lasse; Hensvold, Aase Haj; Catrina, Anca Irinel; Berg, Louise; Klareskog, Lars; Padyukov, Leonid

2017-02-02

Here we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA). RNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of "connector" genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls. There were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples. Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.

Pathway and network-based analysis of genome-wide association studies and RT-PCR validation in polycystic ovary syndrome.

PubMed

Shen, Haoran; Liang, Zhou; Zheng, Saihua; Li, Xuelian

2017-11-01

The purpose of this study was to identify promising candidate genes and pathways in polycystic ovary syndrome (PCOS). Microarray dataset GSE345269 obtained from the Gene Expression Omnibus database includes 7 granulosa cell samples from PCOS patients, and 3 normal granulosa cell samples. Differentially expressed genes (DEGs) were screened between PCOS and normal samples. Pathway enrichment analysis was conducted for DEGs using ClueGO and CluePedia plugin of Cytoscape. A Reactome functional interaction (FI) network of the DEGs was built using ReactomeFIViz, and then network modules were extracted, followed by pathway enrichment analysis for the modules. Expression of DEGs in granulosa cell samples was measured using quantitative RT-PCR. A total of 674 DEGs were retained, which were significantly enriched with inflammation and immune-related pathways. Eight modules were extracted from the Reactome FI network. Pathway enrichment analysis revealed significant pathways of each module: module 0, Regulation of RhoA activity and Signaling by Rho GTPases pathways shared ARHGAP4 and ARHGAP9; module 2, GlycoProtein VI-mediated activation cascade pathway was enriched with RHOG; module 3, Thromboxane A2 receptor signaling, Chemokine signaling pathway, CXCR4-mediated signaling events pathways were enriched with LYN, the hub gene of module 3. Results of RT-PCR confirmed the finding of the bioinformatic analysis that ARHGAP4, ARHGAP9, RHOG and LYN were significantly upregulated in PCOS. RhoA-related pathways, GlycoProtein VI-mediated activation cascade pathway, ARHGAP4, ARHGAP9, RHOG and LYN may be involved in the pathogenesis of PCOS.

Application of Petri net based analysis techniques to signal transduction pathways.

PubMed

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-11-02

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules.

Application of Petri net based analysis techniques to signal transduction pathways

PubMed Central

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-01-01

Background Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. Methods We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. Results We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. Conclusion The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules. PMID:17081284

Dynamic transcriptomic analysis in hircine longissimus dorsi muscle from fetal to neonatal development stages.

PubMed

Zhan, Siyuan; Zhao, Wei; Song, Tianzeng; Dong, Yao; Guo, Jiazhong; Cao, Jiaxue; Zhong, Tao; Wang, Linjie; Li, Li; Zhang, Hongping

2018-01-01

Muscle growth and development from fetal to neonatal stages consist of a series of delicately regulated and orchestrated changes in expression of genes. In this study, we performed whole transcriptome profiling based on RNA-Seq of caprine longissimus dorsi muscle tissue obtained from prenatal stages (days 45, 60, and 105 of gestation) and neonatal stage (the 3-day-old newborn) to identify genes that are differentially expressed and investigate their temporal expression profiles. A total of 3276 differentially expressed genes (DEGs) were identified (Q value < 0.01). Time-series expression profile clustering analysis indicated that DEGs were significantly clustered into eight clusters which can be divided into two classes (Q value < 0.05), class I profiles with downregulated patterns and class II profiles with upregulated patterns. Based on cluster analysis, GO enrichment analysis found that 75, 25, and 8 terms to be significantly enriched in biological process (BP), cellular component (CC), and molecular function (MF) categories in class I profiles, while 35, 21, and 8 terms to be significantly enriched in BP, CC, and MF in class II profiles. KEGG pathway analysis revealed that DEGs from class I profiles were significantly enriched in 22 pathways and the most enriched pathway was Rap1 signaling pathway. DEGs from class II profiles were significantly enriched in 17 pathways and the mainly enriched pathway was AMPK signaling pathway. Finally, six selected DEGs from our sequencing results were confirmed by qPCR. Our study provides a comprehensive understanding of the molecular mechanisms during goat skeletal muscle development from fetal to neonatal stages and valuable information for future studies of muscle development in goats.

Comparative Proteomics Analysis Reveals L-Arginine Activates Ethanol Degradation Pathways in HepG2 Cells.

PubMed

Yan, Guokai; Lestari, Retno; Long, Baisheng; Fan, Qiwen; Wang, Zhichang; Guo, Xiaozhen; Yu, Jie; Hu, Jun; Yang, Xingya; Chen, Changqing; Liu, Lu; Li, Xiuzhi; Purnomoadi, Agung; Achmadi, Joelal; Yan, Xianghua

2016-03-17

L-Arginine (Arg) is a versatile amino acid that plays crucial roles in a wide range of physiological and pathological processes. In this study, to investigate the alteration induced by Arg supplementation in proteome scale, isobaric tags for relative and absolute quantification (iTRAQ) based proteomic approach was employed to comparatively characterize the differentially expressed proteins between Arg deprivation (Ctrl) and Arg supplementation (+Arg) treated human liver hepatocellular carcinoma (HepG2) cells. A total of 21 proteins were identified as differentially expressed proteins and these 21 proteins were all up-regulated by Arg supplementation. Six amino acid metabolism-related proteins, mostly metabolic enzymes, showed differential expressions. Intriguingly, Ingenuity Pathway Analysis (IPA) based pathway analysis suggested that the three ethanol degradation pathways were significantly altered between Ctrl and +Arg. Western blotting and enzymatic activity assays validated that the key enzymes ADH1C, ALDH1A1, and ALDH2, which are mainly involved in ethanol degradation pathways, were highly differentially expressed, and activated between Ctrl and +Arg in HepG2 cells. Furthermore, 10 mM Arg significantly attenuated the cytotoxicity induced by 100 mM ethanol treatment (P < 0.0001). This study is the first time to reveal that Arg activates ethanol degradation pathways in HepG2 cells.

SLEPR: A Sample-Level Enrichment-Based Pathway Ranking Method — Seeking Biological Themes through Pathway-Level Consistency

PubMed Central

Yi, Ming; Stephens, Robert M.

2008-01-01

Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data. PMID:18818771

Installation Restoration Program. Remedial Investigation Report: Minnesota Air National Guard Base Duluth International Airport, Duluth, Minnesota. Volume 7

DTIC Science & Technology

1990-01-01

Selection of Indicator Chemicals 6-36 6.2.2 Estimation of Exposure Point Concentrations or Emission Rates 6-38 6.2.2.1 Exposure Pathway Analysis 6-38...Exposure Point Concentrations or Emission Rates 6-50 j 6.3.2.1 Exposure Pathway Analysis 6-52 6.3.2.2 Exposure Point Concentrations 6-55 6.3.2.3...Exposure Point Concentrations or Emission Rates 6-62 6.4.2.1 Exposure Pathway Analysis 6-62 6.4.2.2 Exposure Point Concentrations 6-69 6.4.2.3

Effect of curcumin on aged Drosophila melanogaster: a pathway prediction analysis.

PubMed

Zhang, Zhi-guo; Niu, Xu-yan; Lu, Ai-ping; Xiao, Gary Guishan

2015-02-01

To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpring GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection (MCODE) were used to establish the protein-protein interaction (PPI) network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for pathway analysis. A total of 87 genes expressed differentially in D. melanogaster melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Genes and their associated pathways in D. melanogaster melanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curcumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.

Large-scale transcriptome analysis reveals arabidopsis metabolic pathways are frequently influenced by different pathogens.

PubMed

Jiang, Zhenhong; He, Fei; Zhang, Ziding

2017-07-01

Through large-scale transcriptional data analyses, we highlighted the importance of plant metabolism in plant immunity and identified 26 metabolic pathways that were frequently influenced by the infection of 14 different pathogens. Reprogramming of plant metabolism is a common phenomenon in plant defense responses. Currently, a large number of transcriptional profiles of infected tissues in Arabidopsis (Arabidopsis thaliana) have been deposited in public databases, which provides a great opportunity to understand the expression patterns of metabolic pathways during plant defense responses at the systems level. Here, we performed a large-scale transcriptome analysis based on 135 previously published expression samples, including 14 different pathogens, to explore the expression pattern of Arabidopsis metabolic pathways. Overall, metabolic genes are significantly changed in expression during plant defense responses. Upregulated metabolic genes are enriched on defense responses, and downregulated genes are enriched on photosynthesis, fatty acid and lipid metabolic processes. Gene set enrichment analysis (GSEA) identifies 26 frequently differentially expressed metabolic pathways (FreDE_Paths) that are differentially expressed in more than 60% of infected samples. These pathways are involved in the generation of energy, fatty acid and lipid metabolism as well as secondary metabolite biosynthesis. Clustering analysis based on the expression levels of these 26 metabolic pathways clearly distinguishes infected and control samples, further suggesting the importance of these metabolic pathways in plant defense responses. By comparing with FreDE_Paths from abiotic stresses, we find that the expression patterns of 26 FreDE_Paths from biotic stresses are more consistent across different infected samples. By investigating the expression correlation between transcriptional factors (TFs) and FreDE_Paths, we identify several notable relationships. Collectively, the current study will deepen our understanding of plant metabolism in plant immunity and provide new insights into disease-resistant crop improvement.

Microarray‑based bioinformatics analysis of the prospective target gene network of key miRNAs influenced by long non‑coding RNA PVT1 in HCC.

PubMed

Zhang, Yu; Mo, Wei-Jia; Wang, Xiao; Zhang, Tong-Tong; Qin, Yuan; Wang, Han-Lin; Chen, Gang; Wei, Dan-Ming; Dang, Yi-Wu

2018-05-02

The long non‑coding RNA (lncRNA) PVT1 plays vital roles in the tumorigenesis and development of various types of cancer. However, the potential expression profiling, functions and pathways of PVT1 in HCC remain unknown. PVT1 was knocked down in SMMC‑7721 cells, and a miRNA microarray analysis was performed to detect the differentially expressed miRNAs. Twelve target prediction algorithms were used to predict the underlying targets of these differentially expressed miRNAs. Bioinformatics analysis was performed to explore the underlying functions, pathways and networks of the targeted genes. Furthermore, the relationship between PVT1 and the clinical parameters in HCC was confirmed based on the original data in the TCGA database. Among the differentially expressed miRNAs, the top two upregulated and downregulated miRNAs were selected for further analysis based on the false discovery rate (FDR), fold‑change (FC) and P‑values. Based on the TCGA database, PVT1 was obviously highly expressed in HCC, and a statistically higher PVT1 expression was found for sex (male), ethnicity (Asian) and pathological grade (G3+G4) compared to the control groups (P<0.05). Furthermore, Gene Ontology (GO) analysis revealed that the target genes were involved in complex cellular pathways, such as the macromolecule biosynthetic process, compound metabolic process, and transcription. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the MAPK and Wnt signaling pathways may be correlated with the regulation of the four candidate miRNAs. The results therefore provide significant information on the differentially expressed miRNAs associated with PVT1 in HCC, and we hypothesized that PVT1 may play vital roles in HCC by regulating different miRNAs or target gene expression (particularly MAPK8) via the MAPK or Wnt signaling pathways. Thus, further investigation of the molecular mechanism of PVT1 in HCC is needed.

Effect of occupational exposures on lung cancer susceptibility: a study of gene-environment interaction analysis.

PubMed

Malhotra, Jyoti; Sartori, Samantha; Brennan, Paul; Zaridze, David; Szeszenia-Dabrowska, Neonila; Świątkowska, Beata; Rudnai, Peter; Lissowska, Jolanta; Fabianova, Eleonora; Mates, Dana; Bencko, Vladimir; Gaborieau, Valerie; Stücker, Isabelle; Foretova, Lenka; Janout, Vladimir; Boffetta, Paolo

2015-03-01

Occupational exposures are known risk factors for lung cancer. Role of genetically determined host factors in occupational exposure-related lung cancer is unclear. We used genome-wide association (GWA) data from a case-control study conducted in 6 European countries from 1998 to 2002 to identify gene-occupation interactions and related pathways for lung cancer risk. GWA analysis was performed for each exposure using logistic regression and interaction term for genotypes, and exposure was included in this model. Both SNP-based and gene-based interaction P values were calculated. Pathway analysis was performed using three complementary methods, and analyses were adjusted for multiple comparisons. We analyzed 312,605 SNPs and occupational exposure to 70 agents from 1,802 lung cancer cases and 1,725 cancer-free controls. Mean age of study participants was 60.1 ± 9.1 years and 75% were male. Largest number of significant associations (P ≤ 1 × 10(-5)) at SNP level was demonstrated for nickel, brick dust, concrete dust, and cement dust, and for brick dust and cement dust at the gene-level (P ≤ 1 × 10(-4)). Approximately 14 occupational exposures showed significant gene-occupation interactions with pathways related to response to environmental information processing via signal transduction (P < 0.001 and FDR < 0.05). Other pathways that showed significant enrichment were related to immune processes and xenobiotic metabolism. Our findings suggest that pathways related to signal transduction, immune process, and xenobiotic metabolism may be involved in occupational exposure-related lung carcinogenesis. Our study exemplifies an integrative approach using pathway-based analysis to demonstrate the role of genetic variants in occupational exposure-related lung cancer susceptibility. Cancer Epidemiol Biomarkers Prev; 24(3); 570-9. ©2015 AACR. ©2015 American Association for Cancer Research.

Carcinogenic Effects of Oil Dispersants: a KEGG Pathway-based RNA-seq Study of Human Airway Epithelial Cells

PubMed Central

Liu, Yao-Zhong; Zhang, Lei; Roy-Engel, Astrid M; Saito, Shigeki; Lasky, Joseph A; Wang, Guangdi; Wang, He

2016-01-01

The health impacts of the BP oil spill are yet to be further revealed as the toxicological effects of oil products and dispersants on human respiratory system may be latent and complex, and hence difficult to study and follow up. Here we performed RNA-seq analyses of a system of human airway epithelial cells treated with the BP crude oil and/or dispersants Corexit 9500 and Corexit 9527 that were used to help break up the oil spill. Based on the RNA-seq data, we then systemically analyzed the transcriptomic perturbations of the cells at the KEGG pathway level using two pathway-based analysis tools, GAGE (generally applicable gene set enrichment) and GSNCA (Gene Sets Net Correlations Analysis). Our results suggested a pattern of change towards carcinogenesis for the treated cells marked by upregulation of ribosomal biosynthesis (hsa03008) (p = 1.97e-13), protein processing (hsa04141) (p = 4.09e-7), Wnt signaling (hsa04310) (p = 6.76e-3), neurotrophin signaling (hsa04722) (p = 7.73e-3) and insulin signaling (hsa04910) (p = 1.16e-2) pathways under the dispersant Corexit 9527 treatment, as identified by GAGE analysis. Furthermore, through GSNCA analysis, we identified gene co-expression changes for several KEGG cancer pathways, including small cell lung cancer pathway (hsa05222, p = 9.99e-5), under various treatments of oil/dispersant, especially the mixture of oil and Corexit 9527. Overall, our results suggested carcinogenic effects of dispersants (in particular Corexit 9527) and their mixtures with the BP crude oil, and provided further support for more stringent safety precautions and regulations for operations involving long-term respiratory exposure to oil and dispersants. PMID:27866042

Plant Reactome: a resource for plant pathways and comparative analysis

PubMed Central

Naithani, Sushma; Preece, Justin; D'Eustachio, Peter; Gupta, Parul; Amarasinghe, Vindhya; Dharmawardhana, Palitha D.; Wu, Guanming; Fabregat, Antonio; Elser, Justin L.; Weiser, Joel; Keays, Maria; Fuentes, Alfonso Munoz-Pomer; Petryszak, Robert; Stein, Lincoln D.; Ware, Doreen; Jaiswal, Pankaj

2017-01-01

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX. PMID:27799469

Pathway-based variant enrichment analysis on the example of dilated cardiomyopathy.

PubMed

Backes, Christina; Meder, Benjamin; Lai, Alan; Stoll, Monika; Rühle, Frank; Katus, Hugo A; Keller, Andreas

2016-01-01

Genome-wide association (GWA) studies have significantly contributed to the understanding of human genetic variation and its impact on clinical traits. Frequently only a limited number of highly significant associations were considered as biologically relevant. Increasingly, network analysis of affected genes is used to explore the potential role of the genetic background on disease mechanisms. Instead of first determining affected genes or calculating scores for genes and performing pathway analysis on the gene level, we integrated both steps and directly calculated enrichment on the genetic variant level. The respective approach has been tested on dilated cardiomyopathy (DCM) GWA data as showcase. To compute significance values, 5000 permutation tests were carried out and p values were adjusted for multiple testing. For 282 KEGG pathways, we computed variant enrichment scores and significance values. Of these, 65 were significant. Surprisingly, we discovered the "nucleotide excision repair" and "tuberculosis" pathways to be most significantly associated with DCM (p = 10(-9)). The latter pathway is driven by genes of the HLA-D antigen group, a finding that closely resembles previous discoveries made by expression quantitative trait locus analysis in the context of DCM-GWA. Next, we implemented a sub-network-based analysis, which searches for affected parts of KEGG, however, independent on the pre-defined pathways. Here, proteins of the contractile apparatus of cardiac cells as well as the FAS sub-network were found to be affected by common polymorphisms in DCM. In this work, we performed enrichment analysis directly on variants, leveraging the potential to discover biological information in thousands of published GWA studies. The applied approach is cutoff free and considers a ranked list of genetic variants as input.

Pathview Web: user friendly pathway visualization and data integration

PubMed Central

Pant, Gaurav; Bhavnasi, Yeshvant K.; Blanchard, Steven G.; Brouwer, Cory

2017-01-01

Abstract Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/. PMID:28482075

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.

Pathway-based analyses.

PubMed

Kent, Jack W

2016-02-03

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing. The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

A multi-pathway hypothesis for human visual fear signaling

PubMed Central

Silverstein, David N.; Ingvar, Martin

2015-01-01

A hypothesis is proposed for five visual fear signaling pathways in humans, based on an analysis of anatomical connectivity from primate studies and human functional connectvity and tractography from brain imaging studies. Earlier work has identified possible subcortical and cortical fear pathways known as the “low road” and “high road,” which arrive at the amygdala independently. In addition to a subcortical pathway, we propose four cortical signaling pathways in humans along the visual ventral stream. All four of these traverse through the LGN to the visual cortex (VC) and branching off at the inferior temporal area, with one projection directly to the amygdala; another traversing the orbitofrontal cortex; and two others passing through the parietal and then prefrontal cortex, one excitatory pathway via the ventral-medial area and one regulatory pathway via the ventral-lateral area. These pathways have progressively longer propagation latencies and may have progressively evolved with brain development to take advantage of higher-level processing. Using the anatomical path lengths and latency estimates for each of these five pathways, predictions are made for the relative processing times at selective ROIs and arrival at the amygdala, based on the presentation of a fear-relevant visual stimulus. Partial verification of the temporal dynamics of this hypothesis might be accomplished using experimental MEG analysis. Possible experimental protocols are suggested. PMID:26379513

AOP: An R Package For Sufficient Causal Analysis in Pathway-based Screening of Drugs and Chemicals for Adversity

EPA Science Inventory

Summary: How can I quickly find the key events in a pathway that I need to monitor to predict that a/an beneficial/adverse event/outcome will occur? This is a key question when using signaling pathways for drug/chemical screening in pharma-cology, toxicology and risk assessment. ...

Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

PubMed

Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

2015-03-09

Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold ([Formula: see text]). These phenotypes are more heritable ([Formula: see text]) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. Copyright © 2015 Brown et al.

Pathway-Based Factor Analysis of Gene Expression Data Produces Highly Heritable Phenotypes That Associate with Age

PubMed Central

Anand Brown, Andrew; Ding, Zhihao; Viñuela, Ana; Glass, Dan; Parts, Leopold; Spector, Tim; Winn, John; Durbin, Richard

2015-01-01

Statistical factor analysis methods have previously been used to remove noise components from high-dimensional data prior to genetic association mapping and, in a guided fashion, to summarize biologically relevant sources of variation. Here, we show how the derived factors summarizing pathway expression can be used to analyze the relationships between expression, heritability, and aging. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarize patterns of gene expression to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 “pathway phenotypes” that summarized patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold (P<5.38×10−5). These phenotypes are more heritable (h2=0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolizing sugars and fatty acids; others relate to insulin signaling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors. PMID:25758824

Investigating dysregulated pathways in Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network.

PubMed

Zhou, Wei; Zhang, Yan; Li, Yue-Hua; Wang, Shuang; Zhang, Jing-Jing; Zhang, Cui-Xia; Zhang, Zhi-Sheng

2017-02-01

This work aimed to identify dysregulated pathways for Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network (PIN). The inference of dysregulated pathways was comprised of four steps: preparing gene expression data, protein-protein interaction (PPI) data and pathway data; constructing a PIN dependent on the data and Pearson correlation coefficient (PCC); selecting seed pathway from PIN by computing activity score for each pathway according to principal component analysis (PCA) method; and investigating dysregulated pathways in a minimum set of pathways (MSP) utilizing seed pathway and the area under the receiver operating characteristics curve (AUC) index implemented in support vector machines (SVM) model. A total of 20,545 genes, 449,833 interactions and 1189 pathways were obtained in the gene expression data, PPI data and pathway data, respectively. The PIN was consisted of 8388 interactions and 1189 nodes, and Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins was identified as the seed pathway. Finally, 15 dysregulated pathways in MSP (AUC=0.999) were obtained for SA infected samples, such as Respiratory electron transport and DNA Replication. We have identified 15 dysregulated pathways for SA infected macrophages based on PIN. The findings might provide potential biomarkers for early detection and therapy of SA infection, and give insights to reveal the molecular mechanism underlying SA infections. However, how these dysregulated pathways worked together still needs to be studied. Copyright © 2016 Elsevier Ltd. All rights reserved.

In silico study of protein to protein interaction analysis of AMP-activated protein kinase and mitochondrial activity in three different farm animal species

NASA Astrophysics Data System (ADS)

Prastowo, S.; Widyas, N.

2018-03-01

AMP-activated protein kinase (AMPK) is cellular energy censor which works based on ATP and AMP concentration. This protein interacts with mitochondria in determine its activity to generate energy for cell metabolism purposes. For that, this paper aims to compare the protein to protein interaction of AMPK and mitochondrial activity genes in the metabolism of known animal farm (domesticated) that are cattle (Bos taurus), pig (Sus scrofa) and chicken (Gallus gallus). In silico study was done using STRING V.10 as prominent protein interaction database, followed with biological function comparison in KEGG PATHWAY database. Set of genes (12 in total) were used as input analysis that are PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PPARGC1, ACC, CPT1B, NRF2 and SOD. The first 7 genes belong to gene in AMPK family, while the last 5 belong to mitochondrial activity genes. The protein interaction result shows 11, 8 and 5 metabolism pathways in Bos taurus, Sus scrofa and Gallus gallus, respectively. The top pathway in Bos taurus is AMPK signaling pathway (10 genes), Sus scrofa is Adipocytokine signaling pathway (8 genes) and Gallus gallus is FoxO signaling pathway (5 genes). Moreover, the common pathways found in those 3 species are Adipocytokine signaling pathway, Insulin signaling pathway and FoxO signaling pathway. Genes clustered in Adipocytokine and Insulin signaling pathway are PRKAA2, PPARGC1A, PRKAB1 and PRKAG2. While, in FoxO signaling pathway are PRKAA2, PRKAB1, PRKAG2. According to that, we found PRKAA2, PRKAB1 and PRKAG2 are the common genes. Based on the bioinformatics analysis, we can demonstrate that protein to protein interaction shows distinct different of metabolism in different species. However, further validation is needed to give a clear explanation.

Pathway analyses and understanding disease associations

PubMed Central

Liu, Yu; Chance, Mark R

2013-01-01

High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease-associations and help to improve treatment. However it is challenging to derive biological insight from conventional single gene based analysis of “omics” data from high throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway and network based approaches were developed to integrate various “omics” data, such as gene expression, copy number alteration, Genome Wide Association Studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions. PMID:24319650

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction.

PubMed

Dhanasekaran, A Ranjitha; Pearson, Jon L; Ganesan, Balasubramanian; Weimer, Bart C

2015-02-25

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism's genome as a database restricts metabolite identification to only those compounds that the organism can produce. To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment's MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

Applications of pathology-assisted image analysis of immunohistochemistry-based biomarkers in oncology.

PubMed

Shinde, V; Burke, K E; Chakravarty, A; Fleming, M; McDonald, A A; Berger, A; Ecsedy, J; Blakemore, S J; Tirrell, S M; Bowman, D

2014-01-01

Immunohistochemistry-based biomarkers are commonly used to understand target inhibition in key cancer pathways in preclinical models and clinical studies. Automated slide-scanning and advanced high-throughput image analysis software technologies have evolved into a routine methodology for quantitative analysis of immunohistochemistry-based biomarkers. Alongside the traditional pathology H-score based on physical slides, the pathology world is welcoming digital pathology and advanced quantitative image analysis, which have enabled tissue- and cellular-level analysis. An automated workflow was implemented that includes automated staining, slide-scanning, and image analysis methodologies to explore biomarkers involved in 2 cancer targets: Aurora A and NEDD8-activating enzyme (NAE). The 2 workflows highlight the evolution of our immunohistochemistry laboratory and the different needs and requirements of each biological assay. Skin biopsies obtained from MLN8237 (Aurora A inhibitor) phase 1 clinical trials were evaluated for mitotic and apoptotic index, while mitotic index and defects in chromosome alignment and spindles were assessed in tumor biopsies to demonstrate Aurora A inhibition. Additionally, in both preclinical xenograft models and an acute myeloid leukemia phase 1 trial of the NAE inhibitor MLN4924, development of a novel image algorithm enabled measurement of downstream pathway modulation upon NAE inhibition. In the highlighted studies, developing a biomarker strategy based on automated image analysis solutions enabled project teams to confirm target and pathway inhibition and understand downstream outcomes of target inhibition with increased throughput and quantitative accuracy. These case studies demonstrate a strategy that combines a pathologist's expertise with automated image analysis to support oncology drug discovery and development programs.

An Alternative Avenue to Teacher Certification: A Cost Analysis of the Pathways to Teaching Careers Program.

ERIC Educational Resources Information Center

Rice, Jennifer King; Brent, Brian O.

2002-01-01

Analyzes cost effectiveness of the Pathways to Teaching Careers, a program that supports an alternative route to university-based teacher certification primarily for noncertified teachers, paraprofessionals, and Peace Corps volunteers. (PKP)

Serrated colorectal cancer: Molecular classification, prognosis, and response to chemotherapy

PubMed Central

Murcia, Oscar; Juárez, Miriam; Hernández-Illán, Eva; Egoavil, Cecilia; Giner-Calabuig, Mar; Rodríguez-Soler, María; Jover, Rodrigo

2016-01-01

Molecular advances support the existence of an alternative pathway of colorectal carcinogenesis that is based on the hypermethylation of specific DNA regions that silences tumor suppressor genes. This alternative pathway has been called the serrated pathway due to the serrated appearance of tumors in histological analysis. New classifications for colorectal cancer (CRC) were proposed recently based on genetic profiles that show four types of molecular alterations: BRAF gene mutations, KRAS gene mutations, microsatellite instability, and hypermethylation of CpG islands. This review summarizes what is known about the serrated pathway of CRC, including CRC molecular and clinical features, prognosis, and response to chemotherapy. PMID:27053844

Expression analysis in response to drought stress in soybean: Shedding light on the regulation of metabolic pathway genes.

PubMed

Guimarães-Dias, Fábia; Neves-Borges, Anna Cristina; Viana, Antonio Americo Barbosa; Mesquita, Rosilene Oliveira; Romano, Eduardo; de Fátima Grossi-de-Sá, Maria; Nepomuceno, Alexandre Lima; Loureiro, Marcelo Ehlers; Alves-Ferreira, Márcio

2012-06-01

Metabolomics analysis of wild type Arabidopsis thaliana plants, under control and drought stress conditions revealed several metabolic pathways that are induced under water deficit. The metabolic response to drought stress is also associated with ABA dependent and independent pathways, allowing a better understanding of the molecular mechanisms in this model plant. Through combining an in silico approach and gene expression analysis by quantitative real-time PCR, the present work aims at identifying genes of soybean metabolic pathways potentially associated with water deficit. Digital expression patterns of Arabidopsis genes, which were selected based on the basis of literature reports, were evaluated under drought stress condition by Genevestigator. Genes that showed strong induction under drought stress were selected and used as bait to identify orthologs in the soybean genome. This allowed us to select 354 genes of putative soybean orthologs of 79 Arabidopsis genes belonging to 38 distinct metabolic pathways. The expression pattern of the selected genes was verified in the subtractive libraries available in the GENOSOJA project. Subsequently, 13 genes from different metabolic pathways were selected for validation by qPCR experiments. The expression of six genes was validated in plants undergoing drought stress in both pot-based and hydroponic cultivation systems. The results suggest that the metabolic response to drought stress is conserved in Arabidopsis and soybean plants.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2014-01-01

Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jannik, Tim; Hartman, Larry

During the operational history of Savannah River Site, many different radionuclides have been released from site facilities. However, as shown in this analysis, only a relatively small number of the released radionuclides have been significant contributors to doses to the offsite public. This report is an update to the 2011 analysis, Critical Radionuclide and Pathway Analysis for the Savannah River Site. SRS-based Performance Assessments for E-Area, Saltstone, F-Tank Farm, H-Tank Farm, and a Comprehensive SRS Composite Analysis have been completed. The critical radionuclides and pathways identified in those extensive reports are also detailed and included in this analysis.

Microarray and network-based identification of functional modules and pathways of active tuberculosis.

PubMed

Bian, Zhong-Rui; Yin, Juan; Sun, Wen; Lin, Dian-Jie

2017-04-01

Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

PubMed

Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

2017-04-27

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

PubMed Central

Towfic, Fadi; Gupta, Shakti; Honavar, Vasant; Subramaniam, Shankar

2012-01-01

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Collectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches (e.g., gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells. PMID:22917187

Plant Reactome: a resource for plant pathways and comparative analysis.

PubMed

Naithani, Sushma; Preece, Justin; D'Eustachio, Peter; Gupta, Parul; Amarasinghe, Vindhya; Dharmawardhana, Palitha D; Wu, Guanming; Fabregat, Antonio; Elser, Justin L; Weiser, Joel; Keays, Maria; Fuentes, Alfonso Munoz-Pomer; Petryszak, Robert; Stein, Lincoln D; Ware, Doreen; Jaiswal, Pankaj

2017-01-04

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Analysis of Differentially Expressed Genes and Signaling Pathways Related to Intramuscular Fat Deposition in Skeletal Muscle of Sex-Linked Dwarf Chickens

PubMed Central

Ye, Yaqiong; Lin, Shumao; Mu, Heping; Tang, Xiaohong; Ou, Yangdan; Chen, Jian; Ma, Yongjiang; Li, Yugu

2014-01-01

Intramuscular fat (IMF) plays an important role in meat quality. However, the molecular mechanisms underlying IMF deposition in skeletal muscle have not been addressed for the sex-linked dwarf (SLD) chicken. In this study, potential candidate genes and signaling pathways related to IMF deposition in chicken leg muscle tissue were characterized using gene expression profiling of both 7-week-old SLD and normal chickens. A total of 173 differentially expressed genes (DEGs) were identified between the two breeds. Subsequently, 6 DEGs related to lipid metabolism or muscle development were verified in each breed based on gene ontology (GO) analysis. In addition, KEGG pathway analysis of DEGs indicated that some of them (GHR, SOCS3, and IGF2BP3) participate in adipocytokine and insulin signaling pathways. To investigate the role of the above signaling pathways in IMF deposition, the gene expression of pathway factors and other downstream genes were measured by using qRT-PCR and Western blot analyses. Collectively, the results identified potential candidate genes related to IMF deposition and suggested that IMF deposition in skeletal muscle of SLD chicken is regulated partially by pathways of adipocytokine and insulin and other downstream signaling pathways (TGF-β/SMAD3 and Wnt/catenin-β pathway). PMID:24757673

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.

PubMed

Li, Qike; Schissler, A Grant; Gardeux, Vincent; Achour, Ikbel; Kenost, Colleen; Berghout, Joanne; Li, Haiquan; Zhang, Hao Helen; Lussier, Yves A

2017-05-24

Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

Pathway-Based Concentration Response Profiles from Toxicogenomics Data

EPA Science Inventory

Microarray analysis of gene expression of in vitro systems could be a powerful tool for assessing chemical hazard. Differentially expressed genes specific to cells, chemicals, and concentrations can be organized into molecular pathways that inform mode of action. An important par...

New challenges for text mining: mapping between text and manually curated pathways

PubMed Central

Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

2008-01-01

Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

Benchmarking pathway interaction network for colorectal cancer to identify dysregulated pathways.

PubMed

Wang, Q; Shi, C-J; Lv, S-H

2017-03-30

Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC) based on the functional dependency among pathways. Protein-protein interaction (PPI) information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA) method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN), where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC.

Altered retinal microRNA expression profiles in early diabetic retinopathy: an in silico analysis.

PubMed

Xiong, Fen; Du, Xinhua; Hu, Jianyan; Li, Tingting; Du, Shanshan; Wu, Qiang

2014-07-01

MicroRNAs (miRNAs) - as negative regulators of target genes - are associated with various human diseases, but their precise role(s) in diabetic retinopathy (DR) remains to be elucidated. The aim of this study was to elucidate the involvement of miRNAs in early DR using in silico analysis to explore their gene expression patterns. We used the streptozotocin (STZ)-induced diabetic rat to investigate the roles of miRNAs in early DR. Retinal miRNA expression profiles from diabetic versus healthy control rats were examined by miRNA array analysis. Based on several bioinformatic systems, specifically, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we identified signatures of the potential pathological processes, gene functions, and signaling pathways that are influenced by dysregulated miRNAs. We used quantitative real-time polymerase chain reaction (qRT-PCR) to validate six (i.e. those with significant changes in expression levels) of the 17 miRNAs that were detected in the miRNA array. We also describe the significant role of the miRNA-gene network, which is based on the interactions between miRNAs and target genes. GO analysis of the 17 miRNAs detected in the miRNA array analysis revealed the most prevalent miRNAs to be those related to biological processes, olfactory bulb development and axonogenesis. These miRNAs also exert significant influence on additional pathways, including the mitogen-activated protein and calcium signaling pathways. Six of the seventeen miRNAs were chosen for qRT-PCR validation. With the exception of a slight difference in miRNA-350, our results are in close agreement with the differential expressions detected by array analysis. This study, which describes miRNA expression during the early developmental phases of DR, revealed extensive miRNA interactions. Based on both their target genes and signaling pathways, we suggest that miRNAs perform critical regulatory functions during the early stages of DR evolution.

Enriched pathways for major depressive disorder identified from a genome-wide association study.

PubMed

Kao, Chung-Feng; Jia, Peilin; Zhao, Zhongming; Kuo, Po-Hsiu

2012-11-01

Major depressive disorder (MDD) has caused a substantial burden of disease worldwide with moderate heritability. Despite efforts through conducting numerous association studies and now, genome-wide association (GWA) studies, the success of identifying susceptibility loci for MDD has been limited, which is partially attributed to the complex nature of depression pathogenesis. A pathway-based analytic strategy to investigate the joint effects of various genes within specific biological pathways has emerged as a powerful tool for complex traits. The present study aimed to identify enriched pathways for depression using a GWA dataset for MDD. For each gene, we estimated its gene-wise p value using combined and minimum p value, separately. Canonical pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta were used. We employed four pathway-based analytic approaches (gene set enrichment analysis, hypergeometric test, sum-square statistic, sum-statistic). We adjusted for multiple testing using Benjamini & Hochberg's method to report significant pathways. We found 17 significantly enriched pathways for depression, which presented low-to-intermediate crosstalk. The top four pathways were long-term depression (p⩽1×10-5), calcium signalling (p⩽6×10-5), arrhythmogenic right ventricular cardiomyopathy (p⩽1.6×10-4) and cell adhesion molecules (p⩽2.2×10-4). In conclusion, our comprehensive pathway analyses identified promising pathways for depression that are related to neurotransmitter and neuronal systems, immune system and inflammatory response, which may be involved in the pathophysiological mechanisms underlying depression. We demonstrated that pathway enrichment analysis is promising to facilitate our understanding of complex traits through a deeper interpretation of GWA data. Application of this comprehensive analytic strategy in upcoming GWA data for depression could validate the findings reported in this study.

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis.

PubMed

Del Sorbo, Maria Rosaria; Balzano, Walter; Donato, Michele; Draghici, Sorin

2013-11-01

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components. Copyright © 2013. Published by Elsevier Ireland Ltd.

Pathview Web: user friendly pathway visualization and data integration.

PubMed

Luo, Weijun; Pant, Gaurav; Bhavnasi, Yeshvant K; Blanchard, Steven G; Brouwer, Cory

2017-07-03

Pathway analysis is widely used in omics studies. Pathway-based data integration and visualization is a critical component of the analysis. To address this need, we recently developed a novel R package called Pathview. Pathview maps, integrates and renders a large variety of biological data onto molecular pathway graphs. Here we developed the Pathview Web server, as to make pathway visualization and data integration accessible to all scientists, including those without the special computing skills or resources. Pathview Web features an intuitive graphical web interface and a user centered design. The server not only expands the core functions of Pathview, but also provides many useful features not available in the offline R package. Importantly, the server presents a comprehensive workflow for both regular and integrated pathway analysis of multiple omics data. In addition, the server also provides a RESTful API for programmatic access and conveniently integration in third-party software or workflows. Pathview Web is openly and freely accessible at https://pathview.uncc.edu/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Metabolomics-Based Elucidation of Active Metabolic Pathways in Erythrocytes and HSC-Derived Reticulocytes.

PubMed

Srivastava, Anubhav; Evans, Krystal J; Sexton, Anna E; Schofield, Louis; Creek, Darren J

2017-04-07

A detailed analysis of the metabolic state of human-stem-cell-derived erythrocytes allowed us to characterize the existence of active metabolic pathways in younger reticulocytes and compare them to mature erythrocytes. Using high-resolution LC-MS-based untargeted metabolomics, we found that reticulocytes had a comparatively much richer repertoire of metabolites, which spanned a range of metabolite classes. An untargeted metabolomics analysis using stable-isotope-labeled glucose showed that only glycolysis and the pentose phosphate pathway actively contributed to the biosynthesis of metabolites in erythrocytes, and these pathways were upregulated in reticulocytes. Most metabolite species found to be enriched in reticulocytes were residual pools of metabolites produced by earlier erythropoietic processes, and their systematic depletion in mature erythrocytes aligns with the simplification process, which is also seen at the cellular and the structural level. Our work shows that high-resolution LC-MS-based untargeted metabolomics provides a global coverage of the biochemical species that are present in erythrocytes. However, the incorporation of stable isotope labeling provides a more accurate description of the active metabolic processes that occur in each developmental stage. To our knowledge, this is the first detailed characterization of the active metabolic pathways of the erythroid lineage, and it provides a rich database for understanding the physiology of the maturation of reticulocytes into mature erythrocytes.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

PubMed

Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

2016-06-01

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Proteomics-based network analysis characterizes biological processes and pathways activated by preconditioned mesenchymal stem cells in cardiac repair mechanisms.

PubMed

Di Silvestre, Dario; Brambilla, Francesca; Scardoni, Giovanni; Brunetti, Pietro; Motta, Sara; Matteucci, Marco; Laudanna, Carlo; Recchia, Fabio A; Lionetti, Vincenzo; Mauri, Pierluigi

2017-05-01

We have demonstrated that intramyocardial delivery of human mesenchymal stem cells preconditioned with a hyaluronan mixed ester of butyric and retinoic acid (MSCp + ) is more effective in preventing the decay of regional myocardial contractility in a swine model of myocardial infarction (MI). However, the understanding of the role of MSCp + in proteomic remodeling of cardiac infarcted tissue is not complete. We therefore sought to perform a comprehensive analysis of the proteome of infarct remote (RZ) and border zone (BZ) of pigs treated with MSCp + or unconditioned stem cells. Heart tissues were analyzed by MudPIT and differentially expressed proteins were selected by a label-free approach based on spectral counting. Protein profiles were evaluated by using PPI networks and their topological analysis. The proteomic remodeling was largely prevented in MSCp + group. Extracellular proteins involved in fibrosis were down-regulated, while energetic pathways were globally up-regulated. Cardioprotectant pathways involved in the production of keto acid metabolites were also activated. Additionally, we found that new hub proteins support the cardioprotective phenotype characterizing the left ventricular BZ treated with MSCp + . In fact, the up-regulation of angiogenic proteins NCL and RAC1 can be explained by the increase of capillary density induced by MSCp + . Our results show that angiogenic pathways appear to be uniquely positioned to integrate signaling with energetic pathways involving cardiac repair. Our findings prompt the use of proteomics-based network analysis to optimize new approaches preventing the post-ischemic proteomic remodeling that may underlie the limited self-repair ability of adult heart. Copyright © 2017 Elsevier B.V. All rights reserved.

HIV-1 Vpr modulates macrophage metabolic pathways: a SILAC-based quantitative analysis.

PubMed

Barrero, Carlos A; Datta, Prasun K; Sen, Satarupa; Deshmane, Satish; Amini, Shohreh; Khalili, Kamel; Merali, Salim

2013-01-01

Human immunodeficiency virus type 1 encoded viral protein Vpr is essential for infection of macrophages by HIV-1. Furthermore, these macrophages are resistant to cell death and are viral reservoir. However, the impact of Vpr on the macrophage proteome is yet to be comprehended. The goal of the present study was to use a stable-isotope labeling by amino acids in cell culture (SILAC) coupled with mass spectrometry-based proteomics approach to characterize the Vpr response in macrophages. Cultured human monocytic cells, U937, were differentiated into macrophages and transduced with adenovirus construct harboring the Vpr gene. More than 600 proteins were quantified in SILAC coupled with LC-MS/MS approach, among which 136 were significantly altered upon Vpr overexpression in macrophages. Quantified proteins were selected and clustered by biological functions, pathway and network analysis using Ingenuity computational pathway analysis. The proteomic data illustrating increase in abundance of enzymes in the glycolytic pathway (pentose phosphate and pyruvate metabolism) was further validated by western blot analysis. In addition, the proteomic data demonstrate down regulation of some key mitochondrial enzymes such as glutamate dehydrogenase 2 (GLUD2), adenylate kinase 2 (AK2) and transketolase (TKT). Based on these observations we postulate that HIV-1 hijacks the macrophage glucose metabolism pathway via the Vpr-hypoxia inducible factor 1 alpha (HIF-1 alpha) axis to induce expression of hexokinase (HK), glucose-6-phosphate dehyrogenase (G6PD) and pyruvate kinase muscle type 2 (PKM2) that facilitates viral replication and biogenesis, and long-term survival of macrophages. Furthermore, dysregulation of mitochondrial glutamate metabolism in macrophages can contribute to neurodegeneration via neuroexcitotoxic mechanisms in the context of NeuroAIDS.

Adaptation Pathway of Low Impact Development Planning under Climate Change for a Sustainable Rural Community

NASA Astrophysics Data System (ADS)

Chen, P. Y.; Tung, C. P.

2016-12-01

The study focuses on developing the methodology of adaptation pathway for storm water management in a community scale. Following previous results on adaptation procedures including problem and goal setup, current risk assessment and analysis, future risk assessment and analysis, and adaptation options identification and evaluation, the study aims at analyzing adaptation pathway planning and implementation, namely the fifth step, for applying low impact development (LID). Based on the efficacy analyses of the feasible adaptation options, an adaptation pathway map can be build. Each pathway is a combination of the adaptation measures arranged in certain order. The developed adaptation pathway map visualizes the relative effectiveness and the connection of the adaptation measures. In addition, the tipping points of the system can be clearly identified and the triggers can be defined accordingly. There are multiple choices of pathways in an adaptation pathway map, which can be referred as pathway candidates. To ensure the applicability and operability, the methodology of adaptation pathway analysis is applied to a case study. Required information for developing an adaptation pathway map includes the scores of the adaptation options on the criteria, namely the effects, costs, immediacy, and side effect. Feasible adaptation options for the design case are dredging, pipeline expansion, pumping station, LID and detention pond. By ranking the options according to the criteria, LID is found dominating dredging and pumping station in this case. The information of the pathway candidates can be further used by the stakeholders to select the most suitable and promising pathway.

Modeling the optimal central carbon metabolic pathways under feedback inhibition using flux balance analysis.

PubMed

De, Rajat K; Tomar, Namrata

2012-12-01

Metabolism is a complex process for energy production for cellular activity. It consists of a cascade of reactions that form a highly branched network in which the product of one reaction is the reactant of the next reaction. Metabolic pathways efficiently produce maximal amount of biomass while maintaining a steady-state behavior. The steady-state activity of such biochemical pathways necessarily incorporates feedback inhibition of the enzymes. This observation motivates us to incorporate feedback inhibition for modeling the optimal activity of metabolic pathways using flux balance analysis (FBA). We demonstrate the effectiveness of the methodology on a synthetic pathway with and without feedback inhibition. Similarly, for the first time, the Central Carbon Metabolic (CCM) pathways of Saccharomyces cerevisiae and Homo sapiens have been modeled and compared based on the above understanding. The optimal pathway, which maximizes the amount of the target product(s), is selected from all those obtained by the proposed method. For this, we have observed the concentration of the product inhibited enzymes of CCM pathway and its influence on its corresponding metabolite/substrate. We have also studied the concentration of the enzymes which are responsible for the synthesis of target products. We further hypothesize that an optimal pathway would opt for higher flux rate reactions. In light of these observations, we can say that an optimal pathway should have lower enzyme concentration and higher flux rates. Finally, we demonstrate the superiority of the proposed method by comparing it with the extreme pathway analysis.

Metabolomic analysis reveals altered metabolic pathways in a rat model of gastric carcinogenesis.

PubMed

Gu, Jinping; Hu, Xiaomin; Shao, Wei; Ji, Tianhai; Yang, Wensheng; Zhuo, Huiqin; Jin, Zeyu; Huang, Huiying; Chen, Jiacheng; Huang, Caihua; Lin, Donghai

2016-09-13

Gastric cancer (GC) is one of the most malignant tumors with a poor prognosis. Alterations in metabolic pathways are inextricably linked to GC progression. However, the underlying molecular mechanisms remain elusive. We performed NMR-based metabolomic analysis of sera derived from a rat model of gastric carcinogenesis, revealed significantly altered metabolic pathways correlated with the progression of gastric carcinogenesis. Rats were histologically classified into four pathological groups (gastritis, GS; low-grade gastric dysplasia, LGD; high-grade gastric dysplasia, HGD; GC) and the normal control group (CON). The metabolic profiles of the five groups were clearly distinguished from each other. Furthermore, significant inter-metabolite correlations were extracted and used to reconstruct perturbed metabolic networks associated with the four pathological stages compared with the normal stage. Then, significantly altered metabolic pathways were identified by pathway analysis. Our results showed that oxidative stress-related metabolic pathways, choline phosphorylation and fatty acid degradation were continually disturbed during gastric carcinogenesis. Moreover, amino acid metabolism was perturbed dramatically in gastric dysplasia and GC. The GC stage showed more changed metabolite levels and more altered metabolic pathways. Two activated pathways (glycolysis; glycine, serine and threonine metabolism) substantially contributed to the metabolic alterations in GC. These results lay the basis for addressing the molecular mechanisms underlying gastric carcinogenesis and extend our understanding of GC progression.

NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

PubMed Central

Shirdel, Elize A.; Xie, Wing; Mak, Tak W.; Jurisica, Igor

2011-01-01

Background MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome – referred to as the micronome – to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal — mirDIP (http://ophid.utoronto.ca/mirDIP). Results mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. Conclusions Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level. PMID:21364759

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites

PubMed Central

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K.

2018-01-01

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly. PMID:29470400

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

PubMed

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K; Mathé, Ewy A

2018-02-22

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

Metabolomics Coupled with Multivariate Data and Pathway Analysis on Potential Biomarkers in Gastric Ulcer and Intervention Effects of Corydalis yanhusuo Alkaloid

PubMed Central

Shuai, Wang; Yongrui, Bao; Shanshan, Guan; Bo, Liu; Lu, Chen; Lei, Wang; Xiaorong, Ran

2014-01-01

Metabolomics, the systematic analysis of potential metabolites in a biological specimen, has been increasingly applied to discovering biomarkers, identifying perturbed pathways, measuring therapeutic targets, and discovering new drugs. By analyzing and verifying the significant difference in metabolic profiles and changes of metabolite biomarkers, metabolomics enables us to better understand substance metabolic pathways which can clarify the mechanism of Traditional Chinese Medicines (TCM). Corydalis yanhusuo alkaloid (CA) is a major component of Qizhiweitong (QZWT) prescription which has been used for treating gastric ulcer for centuries and its mechanism remains unclear completely. Metabolite profiling was performed by high-performance liquid chromatography combined with time-of-flight mass spectrometry (HPLC/ESI-TOF-MS) and in conjunction with multivariate data analysis and pathway analysis. The statistic software Mass Profiller Prossional (MPP) and statistic method including ANOVA and principal component analysis (PCA) were used for discovering novel potential biomarkers to clarify mechanism of CA in treating acid injected rats with gastric ulcer. The changes in metabolic profiling were restored to their base-line values after CA treatment according to the PCA score plots. Ten different potential biomarkers and seven key metabolic pathways contributing to the treatment of gastric ulcer were discovered and identified. Among the pathways, sphingophospholipid metabolism and fatty acid metabolism related network were acutely perturbed. Quantitative real time polymerase chain reaction (RT-PCR) analysis were performed to evaluate the expression of genes related to the two pathways for verifying the above results. The results show that changed biomarkers and pathways may provide evidence to insight into drug action mechanisms and enable us to increase research productivity toward metabolomics drug discovery. PMID:24454691

A fast and simple LC-MS-based characterization of the flavonoid biosynthesis pathway for few seed(ling)s.

PubMed

Jaegle, Benjamin; Uroic, Miran Kalle; Holtkotte, Xu; Lucas, Christina; Termath, Andreas Ole; Schmalz, Hans-Günther; Bucher, Marcel; Hoecker, Ute; Hülskamp, Martin; Schrader, Andrea

2016-09-01

(Pro)anthocyanidins are synthesized by the flavonoid biosynthesis pathway with multi-layered regulatory control. Methods for the analysis of the flavonoid composition in plants are well established for different purposes. However, they typically compromise either on speed or on depth of analysis. In this work we combined and optimized different protocols to enable the analysis of the flavonoid biosynthesis pathway with as little as possible biological material. We chose core substances of this metabolic pathway that serve as a fingerprint to recognize alterations in the main branches of the pathway. We used a simplified sample preparation, two deuterated internal standards, a short and efficient LC separation, highly sensitive detection with tandem MS in multiple reaction monitoring (MRM) mode and hydrolytic release of the core substances to reduce complexity. The method was optimized for Arabidopsis thaliana seeds and seedlings. We demonstrate that one Col-0 seed/seedling is sufficient to obtain a fingerprint of the core substances of the flavonoid biosynthesis pathway. For comparative analysis of different genotypes, we suggest the use of 10 seed(lings). The analysis of Arabidopsis thaliana mutants affecting steps in the pathway revealed foreseen and unexpected alterations of the pathway. For example, HY5 was found to differentially regulate kaempferol in seeds vs. seedlings. Furthermore, our results suggest that COP1 is a master regulator of flavonoid biosynthesis in seedlings but not of flavonoid deposition in seeds. When sample numbers are high and the plant material is limited, this method effectively facilitates metabolic fingerprinting with one seed(ling), revealing shifts and differences in the pathway. Moreover the combination of extracted non-hydrolysed, extracted hydrolysed and non-extracted hydrolysed samples proved useful to deduce the class of derivative from which the individual flavonoids have been released.

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.

PubMed

Cavill, Rachel; Kamburov, Atanas; Ellis, James K; Athersuch, Toby J; Blagrove, Marcus S C; Herwig, Ralf; Ebbels, Timothy M D; Keun, Hector C

2011-03-01

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

New features of triacylglycerol biosynthetic pathways of peanut seeds in early developmental stages.

PubMed

Yu, Mingli; Liu, Fengzhen; Zhu, Weiwei; Sun, Meihong; Liu, Jiang; Li, Xinzheng

2015-11-01

The peanut (Arachis hypogaea L.) is one of the three most important oil crops in the world due to its high average oil content (50 %). To reveal the biosynthetic pathways of seed oil in the early developmental stages of peanut pods with the goal of improving the oil quality, we presented a method combining deep sequencing analysis of the peanut pod transcriptome and quantitative real-time PCR (RT-PCR) verification of seed oil-related genes. From the sequencing data, approximately 1500 lipid metabolism-associated Unigenes were identified. The RT-PCR results quantified the different expression patterns of these triacylglycerol (TAG) synthesis-related genes in the early developmental stages of peanut pods. Based on these results and analysis, we proposed a novel construct of the metabolic pathways involved in the biosynthesis of TAG, including the Kennedy pathway, acyl-CoA-independent pathway and proposed monoacylglycerol pathway. It showed that the biosynthetic pathways of TAG in the early developmental stages of peanut pods were much more complicated than a simple, unidirectional, linear pathway.

PerSubs: A Graph-Based Algorithm for the Identification of Perturbed Subpathways Caused by Complex Diseases.

PubMed

Vrahatis, Aristidis G; Rapti, Angeliki; Sioutas, Spyros; Tsakalidis, Athanasios

2017-01-01

In the era of Systems Biology and growing flow of omics experimental data from high throughput techniques, experimentalists are in need of more precise pathway-based tools to unravel the inherent complexity of diseases and biological processes. Subpathway-based approaches are the emerging generation of pathway-based analysis elucidating the biological mechanisms under the perspective of local topologies onto a complex pathway network. Towards this orientation, we developed PerSub, a graph-based algorithm which detects subpathways perturbed by a complex disease. The perturbations are imprinted through differentially expressed and co-expressed subpathways as recorded by RNA-seq experiments. Our novel algorithm is applied on data obtained from a real experimental study and the identified subpathways provide biological evidence for the brain aging.

Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes.

PubMed

Feng, Yinling; Wang, Xuefeng

2017-03-01

In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co‑expression networks and clinical information was adopted, using weighted gene co‑expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co‑pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution‑based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD‑associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis.

PubMed

Chen, X Y; Chen, Y H; Zhang, L J; Wang, Y; Tong, Z C

2017-02-16

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor.

Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis

PubMed Central

Chen, X.Y.; Chen, Y.H.; Zhang, L.J.; Wang, Y.; Tong, Z.C.

2017-01-01

Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor. PMID:28225867

Reconstruction of metabolic pathways for the cattle genome

PubMed Central

Seo, Seongwon; Lewin, Harris A

2009-01-01

Background Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement. Results An amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly. Conclusion CattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology. PMID:19284618

Application of Monte Carlo cross-validation to identify pathway cross-talk in neonatal sepsis.

PubMed

Zhang, Yuxia; Liu, Cui; Wang, Jingna; Li, Xingxia

2018-03-01

To explore genetic pathway cross-talk in neonates with sepsis, an integrated approach was used in this paper. To explore the potential relationships between differently expressed genes between normal uninfected neonates and neonates with sepsis and pathways, genetic profiling and biologic signaling pathway were first integrated. For different pathways, the score was obtained based upon the genetic expression by quantitatively analyzing the pathway cross-talk. The paired pathways with high cross-talk were identified by random forest classification. The purpose of the work was to find the best pairs of pathways able to discriminate sepsis samples versus normal samples. The results found 10 pairs of pathways, which were probably able to discriminate neonates with sepsis versus normal uninfected neonates. Among them, the best two paired pathways were identified according to analysis of extensive literature. Impact statement To find the best pairs of pathways able to discriminate sepsis samples versus normal samples, an RF classifier, the DS obtained by DEGs of paired pathways significantly associated, and Monte Carlo cross-validation were applied in this paper. Ten pairs of pathways were probably able to discriminate neonates with sepsis versus normal uninfected neonates. Among them, the best two paired pathways ((7) IL-6 Signaling and Phospholipase C Signaling (PLC); (8) Glucocorticoid Receptor (GR) Signaling and Dendritic Cell Maturation) were identified according to analysis of extensive literature.

Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways.

PubMed

Kong, Wei; Mou, Xiaoyang; Di, Benteng; Deng, Jin; Zhong, Ruxing; Wang, Shuaiqun

2017-11-20

Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. The method proposed in this study, takes into account the internal correlations not only between genes but also pathways to identifying dysregulated pathways related to Alzheimer's disease (AD), the most common form of dementia. In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Furthermore, the insights of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes and provide important theoretical guidance for drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Applying meta-pathway analyses through metagenomics to identify the functional properties of the major bacterial communities of a single spontaneous cocoa bean fermentation process sample.

PubMed

Illeghems, Koen; Weckx, Stefan; De Vuyst, Luc

2015-09-01

A high-resolution functional metagenomic analysis of a representative single sample of a Brazilian spontaneous cocoa bean fermentation process was carried out to gain insight into its bacterial community functioning. By reconstruction of microbial meta-pathways based on metagenomic data, the current knowledge about the metabolic capabilities of bacterial members involved in the cocoa bean fermentation ecosystem was extended. Functional meta-pathway analysis revealed the distribution of the metabolic pathways between the bacterial members involved. The metabolic capabilities of the lactic acid bacteria present were most associated with the heterolactic fermentation and citrate assimilation pathways. The role of Enterobacteriaceae in the conversion of substrates was shown through the use of the mixed-acid fermentation and methylglyoxal detoxification pathways. Furthermore, several other potential functional roles for Enterobacteriaceae were indicated, such as pectinolysis and citrate assimilation. Concerning acetic acid bacteria, metabolic pathways were partially reconstructed, in particular those related to responses toward stress, explaining their metabolic activities during cocoa bean fermentation processes. Further, the in-depth metagenomic analysis unveiled functionalities involved in bacterial competitiveness, such as the occurrence of CRISPRs and potential bacteriocin production. Finally, comparative analysis of the metagenomic data with bacterial genomes of cocoa bean fermentation isolates revealed the applicability of the selected strains as functional starter cultures. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reliable pre-eclampsia pathways based on multiple independent microarray data sets.

PubMed

Kawasaki, Kaoru; Kondoh, Eiji; Chigusa, Yoshitsugu; Ujita, Mari; Murakami, Ryusuke; Mogami, Haruta; Brown, J B; Okuno, Yasushi; Konishi, Ikuo

2015-02-01

Pre-eclampsia is a multifactorial disorder characterized by heterogeneous clinical manifestations. Gene expression profiling of preeclamptic placenta have provided different and even opposite results, partly due to data compromised by various experimental artefacts. Here we aimed to identify reliable pre-eclampsia-specific pathways using multiple independent microarray data sets. Gene expression data of control and preeclamptic placentas were obtained from Gene Expression Omnibus. Single-sample gene-set enrichment analysis was performed to generate gene-set activation scores of 9707 pathways obtained from the Molecular Signatures Database. Candidate pathways were identified by t-test-based screening using data sets, GSE10588, GSE14722 and GSE25906. Additionally, recursive feature elimination was applied to arrive at a further reduced set of pathways. To assess the validity of the pre-eclampsia pathways, a statistically-validated protocol was executed using five data sets including two independent other validation data sets, GSE30186, GSE44711. Quantitative real-time PCR was performed for genes in a panel of potential pre-eclampsia pathways using placentas of 20 women with normal or severe preeclamptic singleton pregnancies (n = 10, respectively). A panel of ten pathways were found to discriminate women with pre-eclampsia from controls with high accuracy. Among these were pathways not previously associated with pre-eclampsia, such as the GABA receptor pathway, as well as pathways that have already been linked to pre-eclampsia, such as the glutathione and CDKN1C pathways. mRNA expression of GABRA3 (GABA receptor pathway), GCLC and GCLM (glutathione metabolic pathway), and CDKN1C was significantly reduced in the preeclamptic placentas. In conclusion, ten accurate and reliable pre-eclampsia pathways were identified based on multiple independent microarray data sets. A pathway-based classification may be a worthwhile approach to elucidate the pathogenesis of pre-eclampsia. © The Author 2014. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Biomarkers of the Hedgehog/Smoothened pathway in healthy volunteers

PubMed Central

Kadam, Sunil K; Patel, Bharvin K R; Jones, Emma; Nguyen, Tuan S; Verma, Lalit K; Landschulz, Katherine T; Stepaniants, Sergey; Li, Bin; Brandt, John T; Brail, Leslie H

2012-01-01

The Hedgehog (Hh) pathway is involved in oncogenic transformation and tumor maintenance. The primary objective of this study was to select surrogate tissue to measure messenger ribonucleic acid (mRNA) levels of Hh pathway genes for measurement of pharmacodynamic effect. Expression of Hh pathway specific genes was measured by quantitative real time polymerase chain reaction (qRT-PCR) and global gene expression using Affymetrix U133 microarrays. Correlations were made between the expression of specific genes determined by qRT-PCR and normalized microarray data. Gene ontology analysis using microarray data for a broader set of Hh pathway genes was performed to identify additional Hh pathway-related markers in the surrogate tissue. RNA extracted from blood, hair follicle, and skin obtained from healthy subjects was analyzed by qRT-PCR for 31 genes, whereas 8 samples were analyzed for a 7-gene subset. Twelve sample sets, each with ≤500 ng total RNA derived from hair, skin, and blood, were analyzed using Affymetrix U133 microarrays. Transcripts for several Hh pathway genes were undetectable in blood using qRT-PCR. Skin was the most desirable matrix, followed by hair follicle. Whether processed by robust multiarray average or microarray suite 5 (MAS5), expression patterns of individual samples showed co-clustered signals; both normalization methods were equally effective for unsupervised analysis. The MAS5- normalized probe sets appeared better suited for supervised analysis. This work provides the basis for selection of a surrogate tissue and an expression analysis-based approach to evaluate pathway-related genes as markers of pharmacodynamic effect with novel inhibitors of the Hh pathway. PMID:22611475

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

PubMed

Voyle, Nicola; Keohane, Aoife; Newhouse, Stephen; Lunnon, Katie; Johnston, Caroline; Soininen, Hilkka; Kloszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon; Hodges, Angela; Kiddle, Steven; Dobson, Richard Jb

2016-01-01

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Cells of Origin of Epithelial Ovarian Cancers

DTIC Science & Technology

2015-09-01

cells in oral squamous cell carcinomas by a novel pathway-based lineage tracing approach in a murine model. ! 13! Specific aims: 1. Determine...SUNDARESAN Lineage tracing and clonal analysis of oral cancer initiating cells The goal of this project is to study cancer stem cells /cancer initiating...whether oral cancer cells genetically marked based on their activities for stem cell -related pathways exhibit cancer stem cell properties in vivo by

Formal reasoning about systems biology using theorem proving

PubMed Central

Hasan, Osman; Siddique, Umair; Tahar, Sofiène

2017-01-01

System biology provides the basis to understand the behavioral properties of complex biological organisms at different levels of abstraction. Traditionally, analysing systems biology based models of various diseases have been carried out by paper-and-pencil based proofs and simulations. However, these methods cannot provide an accurate analysis, which is a serious drawback for the safety-critical domain of human medicine. In order to overcome these limitations, we propose a framework to formally analyze biological networks and pathways. In particular, we formalize the notion of reaction kinetics in higher-order logic and formally verify some of the commonly used reaction based models of biological networks using the HOL Light theorem prover. Furthermore, we have ported our earlier formalization of Zsyntax, i.e., a deductive language for reasoning about biological networks and pathways, from HOL4 to the HOL Light theorem prover to make it compatible with the above-mentioned formalization of reaction kinetics. To illustrate the usefulness of the proposed framework, we present the formal analysis of three case studies, i.e., the pathway leading to TP53 Phosphorylation, the pathway leading to the death of cancer stem cells and the tumor growth based on cancer stem cells, which is used for the prognosis and future drug designs to treat cancer patients. PMID:28671950

Biomass-to-electricity: analysis and optimization of the complete pathway steam explosion--enzymatic hydrolysis--anaerobic digestion with ICE vs SOFC as biogas users.

PubMed

Santarelli, M; Barra, S; Sagnelli, F; Zitella, P

2012-11-01

The paper deals with the energy analysis and optimization of a complete biomass-to-electricity energy pathway, starting from raw biomass towards the production of renewable electricity. The first step (biomass-to-biogas) is based on a real pilot plant located in Environment Park S.p.A. (Torino, Italy) with three main steps ((1) impregnation; (2) steam explosion; (3) enzymatic hydrolysis), completed by a two-step anaerobic fermentation. In the second step (biogas-to-electricity), the paper considers two technologies: internal combustion engines and a stack of solid oxide fuel cells. First, the complete pathway has been modeled and validated through experimental data. After, the model has been used for an analysis and optimization of the complete thermo-chemical and biological process, with the objective function of maximization of the energy balance at minimum consumption. The comparison between ICE and SOFC shows the better performance of the integrated plants based on SOFC. Copyright © 2012 Elsevier Ltd. All rights reserved.

A pathway-based view of human diseases and disease relationships.

PubMed

Li, Yong; Agarwal, Pankaj

2009-01-01

It is increasingly evident that human diseases are not isolated from each other. Understanding how different diseases are related to each other based on the underlying biology could provide new insights into disease etiology, classification, and shared biological mechanisms. We have taken a computational approach to studying disease relationships through 1) systematic identification of disease associated genes by literature mining, 2) associating diseases to biological pathways where disease genes are enriched, and 3) linking diseases together based on shared pathways. We identified 4,195 candidate disease associated genes for 1028 diseases. On average, about 50% of disease associated genes of a disease are statistically mapped to pathways. We generated a disease network which consists of 591 diseases and 6,931 disease relationships. We examined properties of this network and provided examples of novel disease relationships which cannot be readily captured through simple literature search or gene overlap analysis. Our results could potentially provide insights into the design of novel, pathway-guided therapeutic interventions for diseases.

iTRAQ-Based Quantitative Proteomic Analysis of the Antimicrobial Mechanism of Peptide F1 against Escherichia coli.

PubMed

Miao, Jianyin; Chen, Feilong; Duan, Shan; Gao, Xiangyang; Liu, Guo; Chen, Yunjiao; Dixon, William; Xiao, Hang; Cao, Yong

2015-08-19

Antimicrobial peptides have received increasing attention in the agricultural and food industries due to their potential to control pathogens. However, to facilitate the development of novel peptide-based antimicrobial agents, details regarding the molecular mechanisms of these peptides need to be elucidated. The aim of this study was to investigate the antimicrobial mechanism of peptide F1, a bacteriocin found in Tibetan kefir, against Escherichia coli at protein levels using iTRAQ-based quantitative proteomic analysis. In response to treatment with peptide F1, 31 of the 280 identified proteins in E. coli showed alterations in their expression, including 10 down-regulated proteins and 21 up-regulated proteins. These 31 proteins all possess different molecular functions and are involved in different molecular pathways, as is evident in referencing the Kyoto Encyclopedia of Genes and Genomes pathways. Specifically, pathways that were significantly altered in E. coli in response to peptide F1 treatment include the tricarboxylic acid cycle, oxidative phosphorylation, glycerophospholipid metabolism, and the cell cycle-caulobacter pathways, which was also associated with inhibition of the cell growth, induction of morphological changes, and cell death. The results provide novel insights into the molecular mechanisms of antimicrobial peptides.

Harnessing pain heterogeneity and RNA transcriptome to identify blood–based pain biomarkers: a novel correlational study design and bioinformatics approach in a graded chronic constriction injury model

PubMed Central

Grace, Peter M.; Hurley, Daniel; Barratt, Daniel T.; Tsykin, Anna; Watkins, Linda R.; Rolan, Paul E.; Hutchinson, Mark R.

2017-01-01

A quantitative, peripherally accessible biomarker for neuropathic pain has great potential to improve clinical outcomes. Based on the premise that peripheral and central immunity contribute to neuropathic pain mechanisms, we hypothesized that biomarkers could be identified from the whole blood of adult male rats, by integrating graded chronic constriction injury (CCI), ipsilateral lumbar dorsal quadrant (iLDQ) and whole blood transcriptomes, and pathway analysis with pain behavior. Correlational bioinformatics identified a range of putative biomarker genes for allodynia intensity, many encoding for proteins with a recognized role in immune/nociceptive mechanisms. A selection of these genes was validated in a separate replication study. Pathway analysis of the iLDQ transcriptome identified Fcγ and Fcε signaling pathways, among others. This study is the first to employ the whole blood transcriptome to identify pain biomarker panels. The novel correlational bioinformatics, developed here, selected such putative biomarkers based on a correlation with pain behavior and formation of signaling pathways with iLDQ genes. Future studies may demonstrate the predictive ability of these biomarker genes across other models and additional variables. PMID:22697386

Characterization of biomarkers in stroke based on ego-networks and pathways.

PubMed

Li, Haixia; Guo, Qianqian

2017-12-01

To explore potential biomarkers in stroke based on ego-networks and pathways. EgoNet method was applied to search for the underlying biomarkers in stroke using transcription profiling of E-GEOD-58294 and protein-protein interaction (PPI) data. Eight ego-genes were identified from PPI network according to the degree characteristics at the criteria of top 5% ranked z-sore and degree >1. Eight candidate ego-networks with classification accuracy ≥0.9 were selected. After performed randomization test, seven significant ego-networks with adjusted p value < 0.05 were identified. Pathway enrichment analysis was then conducted with these ego-networks to search for the significant pathways. Finally, two significant pathways were identified, and six of seven ego-networks were enriched to "3'-UTR-mediated translational regulation" pathway, indicating that this pathway performs an important role in the development of stroke. Seven ego-networks were constructed using EgoNet and two significant enriched by pathways were identified. These may provide new insights into the potential biomarkers for the development of stroke.

Inhalation toxicity of indoor air pollutants in Drosophila melanogaster using integrated transcriptomics and computational behavior analyses

NASA Astrophysics Data System (ADS)

Eom, Hyun-Jeong; Liu, Yuedan; Kwak, Gyu-Suk; Heo, Muyoung; Song, Kyung Seuk; Chung, Yun Doo; Chon, Tae-Soo; Choi, Jinhee

2017-06-01

We conducted an inhalation toxicity test on the alternative animal model, Drosophila melanogaster, to investigate potential hazards of indoor air pollution. The inhalation toxicity of toluene and formaldehyde was investigated using comprehensive transcriptomics and computational behavior analyses. The ingenuity pathway analysis (IPA) based on microarray data suggests the involvement of pathways related to immune response, stress response, and metabolism in formaldehyde and toluene exposure based on hub molecules. We conducted a toxicity test using mutants of the representative genes in these pathways to explore the toxicological consequences of alterations of these pathways. Furthermore, extensive computational behavior analysis showed that exposure to either toluene or formaldehyde reduced most of the behavioral parameters of both wild-type and mutants. Interestingly, behavioral alteration caused by toluene or formaldehyde exposure was most severe in the p38b mutant, suggesting that the defects in the p38 pathway underlie behavioral alteration. Overall, the results indicate that exposure to toluene and formaldehyde via inhalation causes severe toxicity in Drosophila, by inducing significant alterations in gene expression and behavior, suggesting that Drosophila can be used as a potential alternative model in inhalation toxicity screening.

FMM: a web server for metabolic pathway reconstruction and comparative analysis.

PubMed

Chou, Chih-Hung; Chang, Wen-Chi; Chiu, Chih-Min; Huang, Chih-Chang; Huang, Hsien-Da

2009-07-01

Synthetic Biology, a multidisciplinary field, is growing rapidly. Improving the understanding of biological systems through mimicry and producing bio-orthogonal systems with new functions are two complementary pursuits in this field. A web server called FMM (From Metabolite to Metabolite) was developed for this purpose. FMM can reconstruct metabolic pathways form one metabolite to another metabolite among different species, based mainly on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and other integrated biological databases. Novel presentation for connecting different KEGG maps is newly provided. Both local and global graphical views of the metabolic pathways are designed. FMM has many applications in Synthetic Biology and Metabolic Engineering. For example, the reconstruction of metabolic pathways to produce valuable metabolites or secondary metabolites in bacteria or yeast is a promising strategy for drug production. FMM provides a highly effective way to elucidate the genes from which species should be cloned into those microorganisms based on FMM pathway comparative analysis. Consequently, FMM is an effective tool for applications in synthetic biology to produce both drugs and biofuels. This novel and innovative resource is now freely available at http://FMM.mbc.nctu.edu.tw/.

Inhalation toxicity of indoor air pollutants in Drosophila melanogaster using integrated transcriptomics and computational behavior analyses

PubMed Central

Eom, Hyun-Jeong; Liu, Yuedan; Kwak, Gyu-Suk; Heo, Muyoung; Song, Kyung Seuk; Chung, Yun Doo; Chon, Tae-Soo; Choi, Jinhee

2017-01-01

We conducted an inhalation toxicity test on the alternative animal model, Drosophila melanogaster, to investigate potential hazards of indoor air pollution. The inhalation toxicity of toluene and formaldehyde was investigated using comprehensive transcriptomics and computational behavior analyses. The ingenuity pathway analysis (IPA) based on microarray data suggests the involvement of pathways related to immune response, stress response, and metabolism in formaldehyde and toluene exposure based on hub molecules. We conducted a toxicity test using mutants of the representative genes in these pathways to explore the toxicological consequences of alterations of these pathways. Furthermore, extensive computational behavior analysis showed that exposure to either toluene or formaldehyde reduced most of the behavioral parameters of both wild-type and mutants. Interestingly, behavioral alteration caused by toluene or formaldehyde exposure was most severe in the p38b mutant, suggesting that the defects in the p38 pathway underlie behavioral alteration. Overall, the results indicate that exposure to toluene and formaldehyde via inhalation causes severe toxicity in Drosophila, by inducing significant alterations in gene expression and behavior, suggesting that Drosophila can be used as a potential alternative model in inhalation toxicity screening. PMID:28621308

twzPEA: A Topology and Working Zone Based Pathway Enrichment Analysis Framework

USDA-ARS?s Scientific Manuscript database

Sensitive detection of involvement and adaptation of key signaling, regulatory, and metabolic pathways holds the key to deciphering molecular mechanisms such as those in the biomass-to-biofuel conversion process in yeast. Typical gene set enrichment analyses often do not use topology information in...

Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis.

PubMed

Kammen, Alexandra; Law, Meng; Tjan, Bosco S; Toga, Arthur W; Shi, Yonggang

2016-01-15

Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

Disentangling the multigenic and pleiotropic nature of molecular function

PubMed Central

2015-01-01

Background Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. Results We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. Conclusions Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. PMID:26678917

Topological analysis of metabolic control.

PubMed

Sen, A K

1990-12-01

A topological approach is presented for the analysis of control and regulation in metabolic pathways. In this approach, the control structure of a metabolic pathway is represented by a weighted directed graph. From an inspection of the topology of the graph, the control coefficients of the enzymes are evaluated in a heuristic manner in terms of the enzyme elasticities. The major advantage of the topological approach is that it provides a visual framework for (1) calculating the control coefficients of the enzymes, (2) analyzing the cause-effect relationships of the individual enzymes, (3) assessing the relative importance of the enzymes in metabolic regulation, and (4) simplifying the structure of a given pathway, from a regulatory viewpoint. Results are obtained for (a) an unbranched pathway in the absence of feedback the feedforward regulation and (b) an unbranched pathway with feedback inhibition. Our formulation is based on the metabolic control theory of Kacser and Burns (1973) and Heinrich and Rapoport (1974).

A non-canonical RNA degradation pathway suppresses RNAi-dependent epimutations in the human fungal pathogen Mucor circinelloides.

PubMed

Calo, Silvia; Nicolás, Francisco E; Lee, Soo Chan; Vila, Ana; Cervantes, Maria; Torres-Martinez, Santiago; Ruiz-Vazquez, Rosa M; Cardenas, Maria E; Heitman, Joseph

2017-03-01

Mucorales are a group of basal fungi that includes the casual agents of the human emerging disease mucormycosis. Recent studies revealed that these pathogens activate an RNAi-based pathway to rapidly generate drug-resistant epimutant strains when exposed to stressful compounds such as the antifungal drug FK506. To elucidate the molecular mechanism of this epimutation pathway, we performed a genetic analysis in Mucor circinelloides that revealed an inhibitory role for the non-canonical RdRP-dependent Dicer-independent silencing pathway, which is an RNAi-based mechanism involved in mRNA degradation that was recently identified. Thus, mutations that specifically block the mRNA degradation pathway, such as those in the genes r3b2 and rdrp3, enhance the production of drug resistant epimutants, similar to the phenotype previously described for mutation of the gene rdrp1. Our genetic analysis also revealed two new specific components of the epimutation pathway related to the quelling induced protein (qip) and a Sad-3-like helicase (rnhA), as mutations in these genes prevented formation of drug-resistant epimutants. Remarkably, drug-resistant epimutant production was notably increased in M. circinelloides f. circinelloides isolates from humans or other animal hosts. The host-pathogen interaction could be a stressful environment in which the phenotypic plasticity provided by the epimutant pathway might provide an advantage for these strains. These results evoke a model whereby balanced regulation of two different RNAi pathways is determined by the activation of the RNAi-dependent epimutant pathway under stress conditions, or its repression when the regular maintenance of the mRNA degradation pathway operates under non-stress conditions.

Inter-species pathway perturbation prediction via data-driven detection of functional homology.

PubMed

Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Bonneau, Richard; Tarca, Adi L

2015-02-15

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h. christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Metabolomic Analysis and Visualization Engine for LC–MS Data

PubMed Central

Melamud, Eugene; Vastag, Livia; Rabinowitz, Joshua D.

2017-01-01

Metabolomic analysis by liquid chromatography–high-resolution mass spectrometry results in data sets with thousands of features arising from metabolites, fragments, isotopes, and adducts. Here we describe a software package, Metabolomic Analysis and Visualization ENgine (MAVEN), designed for efficient interactive analysis of LC–MS data, including in the presence of isotope labeling. The software contains tools for all aspects of the data analysis process, from feature extraction to pathway-based graphical data display. To facilitate data validation, a machine learning algorithm automatically assesses peak quality. Users interact with raw data primarily in the form of extracted ion chromatograms, which are displayed with overlaid circles indicating peak quality, and bar graphs of peak intensities for both unlabeled and isotope-labeled metabolite forms. Click-based navigation leads to additional information, such as raw data for specific isotopic forms or for metabolites changing significantly between conditions. Fast data processing algorithms result in nearly delay-free browsing. Drop-down menus provide tools for the overlay of data onto pathway maps. These tools enable animating series of pathway graphs, e.g., to show propagation of labeled forms through a metabolic network. MAVEN is released under an open source license at http://maven.princeton.edu. PMID:21049934

Transition paths of Met-enkephalin from Markov state modeling of a molecular dynamics trajectory.

PubMed

Banerjee, Rahul; Cukier, Robert I

2014-03-20

Conformational states and their interconversion pathways of the zwitterionic form of the pentapeptide Met-enkephalin (MetEnk) are identified. An explicit solvent molecular dynamics (MD) trajectory is used to construct a Markov state model (MSM) based on dihedral space clustering of the trajectory, and transition path theory (TPT) is applied to identify pathways between open and closed conformers. In the MD trajectory, only four of the eight backbone dihedrals exhibit bistable behavior. Defining a conformer as the string XXXX with X = "+" or "-" denoting, respectively, positive or negative values of a given dihedral angle and obtaining the populations of these conformers shows that only four conformers are highly populated, implying a strong correlation among these dihedrals. Clustering in dihedral space to construct the MSM finds the same four bistable dihedral angles. These state populations are very similar to those found directly from the MD trajectory. TPT is used to obtain pathways, parametrized by committor values, in dihedral state space that are followed in transitioning from closed to open states. Pathway costs are estimated by introducing a kinetics-based procedure that orders pathways from least (shortest) to greater cost paths. The least costly pathways in dihedral space are found to only involve the same XXXX set of dihedral angles, and the conformers accessed in the closed to open transition pathways are identified. For these major pathways, a correlation between reaction path progress (committors) and the end-to-end distance is identified. A dihedral space principal component analysis of the MD trajectory shows that the first three modes capture most of the overall fluctuation, and pick out the same four dihedrals having essentially all the weight in those modes. A MSM based on root-mean-square backbone clustering was also carried out, with good agreement found with dihedral clustering for the static information, but with results that differ significantly for the pathway analysis.

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Shotgun metabolomic approach based on mass spectrometry for hepatic mitochondria of mice under arsenic exposure.

PubMed

García-Sevillano, M A; García-Barrera, T; Navarro, F; Montero-Lobato, Z; Gómez-Ariza, J L

2015-04-01

Mass spectrometry (MS)-based toxicometabolomics requires analytical approaches for obtaining unbiased metabolic profiles. The present work explores the general application of direct infusion MS using a high mass resolution analyzer (a hybrid systems triple quadrupole-time-of-flight) and a complementary gas chromatography-MS analysis to mitochondria extracts from mouse hepatic cells, emphasizing on mitochondria isolation from hepatic cells with a commercial kit, sample treatment after cell lysis, comprehensive metabolomic analysis and pattern recognition from metabolic profiles. Finally, the metabolomic platform was successfully checked on a case-study based on the exposure experiment of mice Mus musculus to inorganic arsenic during 12 days. Endogenous metabolites alterations were recognized by partial least squares-discriminant analysis. Subsequently, metabolites were identified by combining MS/MS analysis and metabolomics databases. This work reports for the first time the effects of As-exposure on hepatic mitochondria metabolic pathways based on MS, and reveals disturbances in Krebs cycle, β-oxidation pathway, amino acids degradation and perturbations in creatine levels. This non-target analysis provides extensive metabolic information from mitochondrial organelle, which could be applied to toxicology, pharmacology and clinical studies.

The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data.

PubMed

Zhang, Chaoyang; Peng, Li; Zhang, Yaqin; Liu, Zhaoyang; Li, Wenling; Chen, Shilian; Li, Guancheng

2017-06-01

Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R was used for significance analysis of differentially expressed genes (DEGs) between liver cancer samples and normal samples. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, based on R software, were applied for the identification of pathways in which DEGs significantly enriched. Cytoscape software was for the construction of protein-protein interaction (PPI) network and module analysis to find the hub genes and key pathways. Finally, weighted correlation network analysis (WGCNA) was conducted to further screen critical gene modules with similar expression pattern and explore their biological significance. Significance analysis identified 1230 DEGs with fold change >2, including 632 significantly down-regulated DEGs and 598 significantly up-regulated DEGs. GO term enrichment analysis suggested that up-regulated DEG significantly enriched in immune response, cell adhesion, cell migration, type I interferon signaling pathway, and cell proliferation, and the down-regulated DEG mainly enriched in response to endoplasmic reticulum stress and endoplasmic reticulum unfolded protein response. KEGG pathway analysis found DEGs significantly enriched in five pathways including complement and coagulation cascades, focal adhesion, ECM-receptor interaction, antigen processing and presentation, and protein processing in endoplasmic reticulum. The top 10 hub genes in HCC were separately GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, and CD8A, which resulted from PPI network. The top 3 gene interaction modules in PPI network enriched in immune response, organ development, and response to other organism, respectively. WGCNA revealed that the confirmed eight gene modules significantly enriched in monooxygenase and oxidoreductase activity, response to endoplasmic reticulum stress, type I interferon signaling pathway, processing, presentation and binding of peptide antigen, cellular response to cadmium and zinc ion, cell locomotion and differentiation, ribonucleoprotein complex and RNA processing, and immune system process, respectively. In conclusion, we identified some key genes and pathways closely related with HCC initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying HCC occurrence and progression, holding promise for acting as biomarkers and potential therapeutic targets.

Pathway-based analysis of GWAs data identifies association of sex determination genes with susceptibility to testicular germ cell tumors.

PubMed

Koster, Roelof; Mitra, Nandita; D'Andrea, Kurt; Vardhanabhuti, Saran; Chung, Charles C; Wang, Zhaoming; Loren Erickson, R; Vaughn, David J; Litchfield, Kevin; Rahman, Nazneen; Greene, Mark H; McGlynn, Katherine A; Turnbull, Clare; Chanock, Stephen J; Nathanson, Katherine L; Kanetsky, Peter A

2014-11-15

Genome-wide association (GWA) studies of testicular germ cell tumor (TGCT) have identified 18 susceptibility loci, some containing genes encoding proteins important in male germ cell development. Deletions of one of these genes, DMRT1, lead to male-to-female sex reversal and are associated with development of gonadoblastoma. To further explore genetic association with TGCT, we undertook a pathway-based analysis of SNP marker associations in the Penn GWAs (349 TGCT cases and 919 controls). We analyzed a custom-built sex determination gene set consisting of 32 genes using three different methods of pathway-based analysis. The sex determination gene set ranked highly compared with canonical gene sets, and it was associated with TGCT (FDRG = 2.28 × 10(-5), FDRM = 0.014 and FDRI = 0.008 for Gene Set Analysis-SNP (GSA-SNP), Meta-Analysis Gene Set Enrichment of Variant Associations (MAGENTA) and Improved Gene Set Enrichment Analysis for Genome-wide Association Study (i-GSEA4GWAS) analysis, respectively). The association remained after removal of DMRT1 from the gene set (FDRG = 0.0002, FDRM = 0.055 and FDRI = 0.009). Using data from the NCI GWA scan (582 TGCT cases and 1056 controls) and UK scan (986 TGCT cases and 4946 controls), we replicated these findings (NCI: FDRG = 0.006, FDRM = 0.014, FDRI = 0.033, and UK: FDRG = 1.04 × 10(-6), FDRM = 0.016, FDRI = 0.025). After removal of DMRT1 from the gene set, the sex determination gene set remains associated with TGCT in the NCI (FDRG = 0.039, FDRM = 0.050 and FDRI = 0.055) and UK scans (FDRG = 3.00 × 10(-5), FDRM = 0.056 and FDRI = 0.044). With the exception of DMRT1, genes in the sex determination gene set have not previously been identified as TGCT susceptibility loci in these GWA scans, demonstrating the complementary nature of a pathway-based approach for genome-wide analysis of TGCT. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A peer review process as part of the implementation of clinical pathways in radiation oncology: Does it improve compliance?

PubMed

Gebhardt, Brian J; Heron, Dwight E; Beriwal, Sushil

Clinical pathways are patient management plans that standardize evidence-based practices to ensure high-quality and cost-effective medical care. Implementation of a pathway is a collaborative process in our network, requiring the active involvement of physicians. This approach promotes acceptance of pathway recommendations, although a peer review process is necessary to ensure compliance and to capture and approve off-pathway selections. We investigated the peer review process and factors associated with time to completion of peer review. Our cancer center implemented radiation oncology pathways for every disease site throughout a large, integrated network. Recommendations are written based upon national guidelines, published literature, and institutional experience with evidence evaluated hierarchically in order of efficacy, toxicity, and then cost. Physicians enter decisions into an online, menu-driven decision support tool that integrates with medical records. Data were collected from the support tool and included the rate of on- and off-pathway selections, peer review decisions performed by disease site directors, and time to complete peer review. A total of 6965 treatment decisions were entered in 2015, and 605 (8.7%) were made off-pathway and were subject to peer review. The median time to peer review decision was 2 days (interquartile range, 0.2-6.8). Factors associated with time to peer review decision >48 hours on univariate analysis include disease site (P < .0001) with a trend toward significance (P = .066) for radiation therapy modality. There was no difference between recurrent and non-recurrent disease (P = .267). Multivariable analysis revealed disease site was associated with time to peer review (P < .001), with lymphoma and skin/sarcoma most strongly influencing decision time >48 hours. Clinical pathways are an integral tool for standardizing evidence-based care throughout our large, integrated network, with 91.3% of all treatment decisions being made as per pathway. The peer review process was feasible, with <1% selections ultimately rejected, suggesting that awareness of peer review of treatment decisions encourages compliance with clinical pathway recommendations. Copyright © 2017 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Integrative pathway knowledge bases as a tool for systems molecular medicine.

PubMed

Liang, Mingyu

2007-08-20

There exists a sense of urgency to begin to generate a cohesive assembly of biomedical knowledge as the pace of knowledge accumulation accelerates. The urgency is in part driven by the emergence of systems molecular medicine that emphasizes the combination of systems analysis and molecular dissection in the future of medical practice and research. A potentially powerful approach is to build integrative pathway knowledge bases that link organ systems function with molecules.

Convergent and divergent pathways decoding hierarchical additive mechanisms in treating cerebral ischemia-reperfusion injury.

PubMed

Zhang, Ying-Ying; Li, Hai-Xia; Chen, Yin-Ying; Fang, Hong; Yu, Ya-Nan; Liu, Jun; Jing, Zhi-Wei; Wang, Zhong; Wang, Yong-Yan

2014-03-01

Cerebral ischemia is considered to be a highly complex disease resulting from the complicated interplay of multiple pathways. Disappointedly, most of the previous studies were limited to a single gene or a single pathway. The extent to which all involved pathways are translated into fusing mechanisms of a combination therapy is of fundamental importance. We report an integrative strategy to reveal the additive mechanism that a combination (BJ) of compound baicalin (BA) and jasminoidin (JA) fights against cerebral ischemia based on variation of pathways and functional communities. We identified six pathways of BJ group that shared diverse additive index from 0.09 to 1, which assembled broad cross talks from seven pathways of BA and 16 pathways of JA both at horizontal and vertical levels. Besides a total of 60 overlapping functions as a robust integration background among the three groups based on significantly differential subnetworks, additive mechanism with strong confidence by networks altered functions. These results provide strong evidence that the additive mechanism is more complex than previously appreciated, and an integrative analysis of pathways may suggest an important paradigm for revealing pharmacological mechanisms underlying drug combinations. © 2013 John Wiley & Sons Ltd.

Integrated analysis of miRNA and mRNA expression data identifies multiple miRNAs regulatory networks for the tumorigenesis of colorectal cancer.

PubMed

Xu, Peng; Wang, Junhua; Sun, Bo; Xiao, Zhongdang

2018-06-15

Investigating the potential biological function of differential changed genes through integrating multiple omics data including miRNA and mRNA expression profiles, is always hot topic. However, how to evaluate the repression effect on target genes integrating miRNA and mRNA expression profiles are not fully solved. In this study, we provide an analyzing method by integrating both miRNAs and mRNAs expression data simultaneously. Difference analysis was adopted based on the repression score, then significantly repressed mRNAs were screened out by DEGseq. Pathway analysis for the significantly repressed mRNAs shows that multiple pathways such as MAPK signaling pathway, TGF-beta signaling pathway and so on, may correlated to the colorectal cancer(CRC). Focusing on the MAPK signaling pathway, a miRNA-mRNA network that centering the cell fate genes was constructed. Finally, the miRNA-mRNAs that potentially important in the CRC carcinogenesis were screened out and scored by impact index. Copyright © 2018 Elsevier B.V. All rights reserved.

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Time course of gene expression during mouse skeletal muscle hypertrophy

PubMed Central

Lee, Jonah D.; England, Jonathan H.; Esser, Karyn A.; McCarthy, John J.

2013-01-01

The purpose of this study was to perform a comprehensive transcriptome analysis during skeletal muscle hypertrophy to identify signaling pathways that are operative throughout the hypertrophic response. Global gene expression patterns were determined from microarray results on days 1, 3, 5, 7, 10, and 14 during plantaris muscle hypertrophy induced by synergist ablation in adult mice. Principal component analysis and the number of differentially expressed genes (cutoffs ≥2-fold increase or ≥50% decrease compared with control muscle) revealed three gene expression patterns during overload-induced hypertrophy: early (1 day), intermediate (3, 5, and 7 days), and late (10 and 14 days) patterns. Based on the robust changes in total RNA content and in the number of differentially expressed genes, we focused our attention on the intermediate gene expression pattern. Ingenuity Pathway Analysis revealed a downregulation of genes encoding components of the branched-chain amino acid degradation pathway during hypertrophy. Among these genes, five were predicted by Ingenuity Pathway Analysis or previously shown to be regulated by the transcription factor Kruppel-like factor-15, which was also downregulated during hypertrophy. Moreover, the integrin-linked kinase signaling pathway was activated during hypertrophy, and the downregulation of muscle-specific micro-RNA-1 correlated with the upregulation of five predicted targets associated with the integrin-linked kinase pathway. In conclusion, we identified two novel pathways that may be involved in muscle hypertrophy, as well as two upstream regulators (Kruppel-like factor-15 and micro-RNA-1) that provide targets for future studies investigating the importance of these pathways in muscle hypertrophy. PMID:23869057

Time course of gene expression during mouse skeletal muscle hypertrophy.

PubMed

Chaillou, Thomas; Lee, Jonah D; England, Jonathan H; Esser, Karyn A; McCarthy, John J

2013-10-01

The purpose of this study was to perform a comprehensive transcriptome analysis during skeletal muscle hypertrophy to identify signaling pathways that are operative throughout the hypertrophic response. Global gene expression patterns were determined from microarray results on days 1, 3, 5, 7, 10, and 14 during plantaris muscle hypertrophy induced by synergist ablation in adult mice. Principal component analysis and the number of differentially expressed genes (cutoffs ≥2-fold increase or ≥50% decrease compared with control muscle) revealed three gene expression patterns during overload-induced hypertrophy: early (1 day), intermediate (3, 5, and 7 days), and late (10 and 14 days) patterns. Based on the robust changes in total RNA content and in the number of differentially expressed genes, we focused our attention on the intermediate gene expression pattern. Ingenuity Pathway Analysis revealed a downregulation of genes encoding components of the branched-chain amino acid degradation pathway during hypertrophy. Among these genes, five were predicted by Ingenuity Pathway Analysis or previously shown to be regulated by the transcription factor Kruppel-like factor-15, which was also downregulated during hypertrophy. Moreover, the integrin-linked kinase signaling pathway was activated during hypertrophy, and the downregulation of muscle-specific micro-RNA-1 correlated with the upregulation of five predicted targets associated with the integrin-linked kinase pathway. In conclusion, we identified two novel pathways that may be involved in muscle hypertrophy, as well as two upstream regulators (Kruppel-like factor-15 and micro-RNA-1) that provide targets for future studies investigating the importance of these pathways in muscle hypertrophy.

Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las.

PubMed

Aghdasi, Nava; Whipple, Mark; Humphreys, Ian M; Moe, Kris S; Hannaford, Blake; Bly, Randall A

2018-06-01

Successful multidisciplinary treatment of skull base pathology requires precise preoperative planning. Current surgical approach (pathway) selection for these complex procedures depends on an individual surgeon's experiences and background training. Because of anatomical variation in both normal tissue and pathology (eg, tumor), a successful surgical pathway used on one patient is not necessarily the best approach on another patient. The question is how to define and obtain optimized patient-specific surgical approach pathways? In this article, we demonstrate that the surgeon's knowledge and decision making in preoperative planning can be modeled by a multiobjective cost function in a retrospective analysis of actual complex skull base cases. Two different approaches- weighted-sum approach and Pareto optimality-were used with a defined cost function to derive optimized surgical pathways based on preoperative computed tomography (CT) scans and manually designated pathology. With the first method, surgeon's preferences were input as a set of weights for each objective before the search. In the second approach, the surgeon's preferences were used to select a surgical pathway from the computed Pareto optimal set. Using preoperative CT and magnetic resonance imaging, the patient-specific surgical pathways derived by these methods were similar (85% agreement) to the actual approaches performed on patients. In one case where the actual surgical approach was different, revision surgery was required and was performed utilizing the computationally derived approach pathway.

Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.

PubMed

Li, Chunquan; Han, Junwei; Yao, Qianlan; Zou, Chendan; Xu, Yanjun; Zhang, Chunlong; Shang, Desi; Zhou, Lingyun; Zou, Chaoxia; Sun, Zeguo; Li, Jing; Zhang, Yunpeng; Yang, Haixiu; Gao, Xu; Li, Xia

2013-05-01

Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome

PubMed Central

2013-01-01

Background Decades of research strongly suggest that the genetic etiology of autism spectrum disorders (ASDs) is heterogeneous. However, most published studies focus on group differences between cases and controls. In contrast, we hypothesized that the heterogeneity of the disorder could be characterized by identifying pathways for which individuals are outliers rather than pathways representative of shared group differences of the ASD diagnosis. Methods Two previously published blood gene expression data sets – the Translational Genetics Research Institute (TGen) dataset (70 cases and 60 unrelated controls) and the Simons Simplex Consortium (Simons) dataset (221 probands and 191 unaffected family members) – were analyzed. All individuals of each dataset were projected to biological pathways, and each sample’s Mahalanobis distance from a pooled centroid was calculated to compare the number of case and control outliers for each pathway. Results Analysis of a set of blood gene expression profiles from 70 ASD and 60 unrelated controls revealed three pathways whose outliers were significantly overrepresented in the ASD cases: neuron development including axonogenesis and neurite development (29% of ASD, 3% of control), nitric oxide signaling (29%, 3%), and skeletal development (27%, 3%). Overall, 50% of cases and 8% of controls were outliers in one of these three pathways, which could not be identified using group comparison or gene-level outlier methods. In an independently collected data set consisting of 221 ASD and 191 unaffected family members, outliers in the neurogenesis pathway were heavily biased towards cases (20.8% of ASD, 12.0% of control). Interestingly, neurogenesis outliers were more common among unaffected family members (Simons) than unrelated controls (TGen), but the statistical significance of this effect was marginal (Chi squared P < 0.09). Conclusions Unlike group difference approaches, our analysis identified the samples within the case and control groups that manifested each expression signal, and showed that outlier groups were distinct for each implicated pathway. Moreover, our results suggest that by seeking heterogeneity, pathway-based outlier analysis can reveal expression signals that are not apparent when considering only shared group differences. PMID:24063311

Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies.

PubMed

Cannistraci, Carlo V; Ogorevc, Jernej; Zorc, Minja; Ravasi, Timothy; Dovc, Peter; Kunej, Tanja

2013-02-14

Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent. Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0). The developed cryptorchidism gene atlas includes 217 candidate loci (genes, regions involved in chromosomal mutations, and copy number variations) identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies. The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The collected data will further facilitate development of novel genetic markers and could be of interest for functional studies in animals and human. The proposed network-based systems biology approach elucidates molecular mechanisms underlying co-presence of cryptorchidism and cardiomyopathy in RASopathies. Such approach could also aid in molecular explanation of co-presence of diverse and apparently unrelated clinical manifestations in other syndromes.

The researchers' role in knowledge translation: a realist evaluation of the development and implementation of diagnostic pathways for cancer in two United Kingdom localities.

PubMed

Banks, Jon; Wye, Lesley; Hall, Nicola; Rooney, James; Walter, Fiona M; Hamilton, Willie; Gjini, Ardiana; Rubin, Greg

2017-12-13

In examining an initiative to develop and implement new cancer diagnostic pathways in two English localities, this paper evaluates 'what works' and examines the role of researchers in facilitating knowledge translation amongst teams of local clinicians and policy-makers. Using realist evaluation with a mixed methods case study approach, we conducted documentary analysis of meeting minutes and pathway iterations to map pathway development. We interviewed 14 participants to identify the contexts, mechanisms and outcomes (CMOs) that led to successful pathway development and implementation. Interviews were analysed thematically and four CMO configurations were developed. One site produced three fully implemented pathways, while the other produced two that were partly implemented. In explaining the differences, we found that a respected, independent, well-connected leader modelling partnership working and who facilitates a local, stable group that agree about the legitimacy of the data and project (context) can empower local teams to become sufficiently autonomous (mechanism) to develop and implement research-based pathways (outcome). Although both teams designed relevant, research-based cancer pathways, in the site where the pathways were successfully implemented the research team merely assisted, while, in the other, the research team drove the initiative. Based on our study findings, local stakeholders can apply local and research knowledge to develop and implement research-based pathways. However, success will depend on how academics empower local teams to create autonomy. Crucially, after re-packaging and translating research for local circumstances, identifying fertile environments with the right elements for implementation and developing collaborative relationships with local leaders, academics must step back.

Reconstruction of the Fatty Acid Biosynthetic Pathway of Exiguobacterium antarcticum B7 Based on Genomic and Bibliomic Data.

PubMed

Kawasaki, Regiane; Baraúna, Rafael A; Silva, Artur; Carepo, Marta S P; Oliveira, Rui; Marques, Rodolfo; Ramos, Rommel T J; Schneider, Maria P C

2016-01-01

Exiguobacterium antarcticum B7 is extremophile Gram-positive bacteria able to survive in cold environments. A key factor to understanding cold adaptation processes is related to the modification of fatty acids composing the cell membranes of psychrotrophic bacteria. In our study we show the in silico reconstruction of the fatty acid biosynthesis pathway of E. antarcticum B7. To build the stoichiometric model, a semiautomatic procedure was applied, which integrates genome information using KEGG and RAST/SEED. Constraint-based methods, namely, Flux Balance Analysis (FBA) and elementary modes (EM), were applied. FBA was implemented in the sense of hexadecenoic acid production maximization. To evaluate the influence of the gene expression in the fluxome analysis, FBA was also calculated using the log2⁡FC values obtained in the transcriptome analysis at 0°C and 37°C. The fatty acid biosynthesis pathway showed a total of 13 elementary flux modes, four of which showed routes for the production of hexadecenoic acid. The reconstructed pathway demonstrated the capacity of E. antarcticum B7 to de novo produce fatty acid molecules. Under the influence of the transcriptome, the fluxome was altered, promoting the production of short-chain fatty acids. The calculated models contribute to better understanding of the bacterial adaptation at cold environments.

In their own words: Content analysis of pathways to recovery among individuals with the lived experience of homelessness and alcohol use disorders.

PubMed

Collins, Susan E; Jones, Connor B; Hoffmann, Gail; Nelson, Lonnie A; Hawes, Starlyn M; Grazioli, Véronique S; Mackelprang, Jessica L; Holttum, Jessica; Kaese, Greta; Lenert, James; Herndon, Patrick; Clifasefi, Seema L

2016-01-01

Alcohol use disorders (AUDs) are more prevalent among homeless individuals than in the general population, and homeless individuals are disproportionately affected by alcohol-related morbidity and mortality. Unfortunately, abstinence-based approaches are neither desirable to nor highly effective for most members of this population. Recent research has indicated that homeless people aspire to clinically significant recovery goals beyond alcohol abstinence, including alcohol harm reduction and quality-of-life improvement. However, no research has documented this population's preferred pathways toward self-defined recovery. Considering principles of patient-centred care, a richer understanding of this population's desired pathways to recovery may help providers better engage and support them. Participants (N=50) had lived experience of homelessness and AUDs and participated in semi-structured interviews regarding histories of homelessness, alcohol use, and abstinence-based treatment as well as suggestions for improving alcohol treatment. Conventional content analysis was used to ascertain participants' perceptions of abstinence-based treatment and mutual-help modalities, while it additionally revealed alternative pathways to recovery. Most participants reported involvement in abstinence-based modalities for reasons other than the goal of achieving long-term abstinence from alcohol (e.g., having shelter in winter months, "taking a break" from alcohol use, being among "like-minded people"). In contrast, most participants preferred alternative pathways to recovery, including fulfilling basic needs (e.g., obtaining housing), using harm reduction approaches (e.g., switching from higher to lower alcohol content beverages), engaging in meaningful activities (e.g., art, outings, spiritual/cultural activities), and making positive social connections. Most people with the lived experience of homelessness and AUDs we interviewed were uninterested in abstinence-based modalities as a means of attaining long-term alcohol abstinence. These individuals do, however, have creative ideas about alternative pathways to recovery that treatment providers may support to reduce alcohol-related harm and enhance quality of life. Copyright © 2015 Elsevier B.V. All rights reserved.

In their own words: Content analysis of pathways to recovery among individuals with the lived experience of homelessness and alcohol use disorders

PubMed Central

Collins, Susan E.; Jones, Connor B.; Hoffmann, Gail; Nelson, Lonnie A.; Hawes, Starlyn M.; Grazioli, Véronique S.; Mackelprang, Jessica L.; Holttum, Jessica; Kaese, Greta; Lenert, James; Herndon, Patrick; Clifasefi, Seema L.

2015-01-01

Background Alcohol use disorders (AUDs) are more prevalent among homeless individuals than in the general population, and homeless individuals are disproportionately affected by alcohol-related morbidity and mortality. Unfortunately, abstinence-based approaches are neither desirable to nor highly effective for most members of this population. Recent research has indicated that homeless people aspire to clinically significant recovery goals beyond alcohol abstinence, including alcohol harm reduction and quality-of-life improvement. However, no research has documented this population’s preferred pathways toward self-defined recovery. Considering principles of patient-centred care, a richer understanding of this population’s desired pathways to recovery may help providers better engage and support them. Methods Participants (N = 50) had lived experience of homelessness and AUDs and participated in semi-structured interviews regarding histories of homelessness, alcohol use, and abstinence-based treatment as well as suggestions for improving alcohol treatment. Conventional content analysis was used to ascertain participants’ perceptions of abstinence-based treatment and mutual-help modalities, while it additionally revealed alternative pathways to recovery. Results Most participants reported involvement in abstinence-based modalities for reasons other than the goal of achieving long-term abstinence from alcohol (e.g., having shelter in winter months, “taking a break” from alcohol use, being among “like-minded people”). In contrast, most participants preferred alternative pathways to recovery, including fulfilling basic needs (e.g., obtaining housing), using harm reduction approaches (e.g., switching from higher to lower alcohol content beverages), engaging in meaningful activities (e.g., art, outings, spiritual/cultural activities), and making positive social connections. Conclusions Most people with the lived experience of homelessness and AUDs we interviewed were uninterested in abstinence-based modalities as a means of attaining long-term alcohol abstinence. These individuals do, however, have creative ideas about alternative pathways to recovery that treatment providers may support to reduce alcohol-related harm and enhance quality of life. PMID:26364078

Proteomic analysis of pancreatic cancer stem cells: Functional role of fatty acid synthesis and mevalonate pathways.

PubMed

Brandi, Jessica; Dando, Ilaria; Pozza, Elisa Dalla; Biondani, Giulia; Jenkins, Rosalind; Elliott, Victoria; Park, Kevin; Fanelli, Giuseppina; Zolla, Lello; Costello, Eithne; Scarpa, Aldo; Cecconi, Daniela; Palmieri, Marta

2017-01-06

Recently, we have shown that the secretome of pancreatic cancer stem cells (CSCs) is characterized by proteins that participate in cancer differentiation, invasion, and metastasis. However, the differentially expressed intracellular proteins that lead to the specific characteristics of pancreatic CSCs have not yet been identified, and as a consequence the deranged metabolic pathways are yet to be elucidated. To identify the modulated proteins of pancreatic CSCs, iTRAQ-based proteomic analysis was performed to compare the proteome of Panc1 CSCs and Panc1 parental cells, identifying 230 modulated proteins. Pathway analysis revealed activation of glycolysis, the pentose phosphate pathway, the pyruvate-malate cycle, and lipid metabolism as well as downregulation of the Krebs cycle, the splicesome and non-homologous end joining. These findings were supported by metabolomics and immunoblotting analysis. It was also found that inhibition of fatty acid synthase by cerulenin and of mevalonate pathways by atorvastatin have a greater anti-proliferative effect on cancer stem cells than parental cells. Taken together, these results clarify some important aspects of the metabolic network signature of pancreatic cancer stem cells, shedding light on key and novel therapeutic targets and suggesting that fatty acid synthesis and mevalonate pathways play a key role in ensuring their viability. To better understand the altered metabolic pathways of pancreatic cancer stem cells (CSCs), a comprehensive proteomic analysis and metabolite profiling investigation of Panc1 and Panc1 CSCs were carried out. The findings obtained indicate that Panc1 CSCs are characterized by upregulation of glycolysis, pentose phosphate pathway, pyruvate-malate cycle, and lipid metabolism and by downregulation of Krebs cycle, spliceosome and non-homologous end joining. Moreover, fatty acid synthesis and mevalonate pathways are shown to play a critical contribution to the survival of pancreatic cancer stem cells. This study is helpful for broadening the knowledge of pancreatic cancer stem cells and could accelerate the development of novel therapeutic strategies. Copyright © 2016 Elsevier B.V. All rights reserved.

Serum metabolomics differentiating pancreatic cancer from new-onset diabetes

PubMed Central

He, Xiangyi; Zhong, Jie; Wang, Shuwei; Zhou, Yufen; Wang, Lei; Zhang, Yongping; Yuan, Yaozong

2017-01-01

To establish a screening strategy for pancreatic cancer (PC) based on new-onset diabetic mellitus (NO-DM), serum metabolomics analysis and a search for the metabolic pathways associated with PC related DM were performed. Serum samples from patients with NO-DM (n = 30) and patients with pancreatic cancer and NO-DM were examined by liquid chromatography-mass spectrometry. Data were analyzed using principal components analysis (PCA) and orthogonal projection to latent structures (OPLS) of the most significant metabolites. The diagnostic model was constructed using logistic regression analysis. Metabolic pathways were analyzed using the web-based tool MetPA. PC patients with NO-DM were older and had a lower BMI and shorter duration of DM than those with NO-DM. The metabolomic profiles of patients with PC and NO-DM were significantly different from those of patients with NO-DM in the PCA and OPLS models. Sixty two differential metabolites were identified by the OPLS model. The logistic regression model using a panel of two metabolites including N_Succinyl_L_diaminopimelic_acid and PE (18:2) had high sensitivity (93.3%) and specificity (93.1%) for PC. The top three metabolic pathways associated with PC related DM were valine, leucine and isoleucine biosynthesis and degradation, primary bile acid biosynthesis, and sphingolipid metabolism. In conclusion, screening for PC based on NO-DM using serum metabolomics in combination with clinic characteristics and CA19-9 is a potential useful strategy. Several metabolic pathways differed between PC related DM and type 2 DM. PMID:28418859

A systematic analysis of genomic changes in Tg2576 mice.

PubMed

Tan, Lu; Wang, Xiong; Ni, Zhong-Fei; Zhu, Xiuming; Wu, Wei; Zhu, Ling-Qiang; Liu, Dan

2013-06-01

Alzheimer's disease (AD) is an age-related neurodegenerative disorder characterized by intelligence decline, behavioral disorders and cognitive disability. The purpose of this study was to investigate gene expression in AD, based on published microarray data on Tg2576 mice. Hierarchical Cluster Analysis and Gene Ontology were employed to group genes together on the basis of their product characteristics and annotation data. Genes with prominent alterations were clustered into apoptosis and axon guidance pathways. Based on our findings and those of previous studies, we propose that the mitochondria-mediated apoptotic pathway plays a crucial role in the neuronal loss and synaptic dysfunction associated with AD. Furthermore, based on the findings of Positional Gene Enrichment analysis and Gene Set Enrichment analysis, we propose that the regulation of transcription of AD genes may be an important pathogenic factor in this neurodegenerative disease. Our results highlight the importance of genes that could subsequently be examined for their potential as prognostic markers for AD.

RNA-seq Analysis Reveals Unique Transcriptome Signatures in Systemic Lupus Erythematosus Patients with Distinct Autoantibody Specificities

PubMed Central

Rai, Richa; Chauhan, Sudhir Kumar; Singh, Vikas Vikram; Rai, Madhukar; Rai, Geeta

2016-01-01

Systemic lupus erythematosus (SLE) patients exhibit immense heterogeneity which is challenging from the diagnostic perspective. Emerging high throughput sequencing technologies have been proved to be a useful platform to understand the complex and dynamic disease processes. SLE patients categorised based on autoantibody specificities are reported to have differential immuno-regulatory mechanisms. Therefore, we performed RNA-seq analysis to identify transcriptomics of SLE patients with distinguished autoantibody specificities. The SLE patients were segregated into three subsets based on the type of autoantibodies present in their sera (anti-dsDNA+ group with anti-dsDNA autoantibody alone; anti-ENA+ group having autoantibodies against extractable nuclear antigens (ENA) only, and anti-dsDNA+ENA+ group having autoantibodies to both dsDNA and ENA). Global transcriptome profiling for each SLE patients subsets was performed using Illumina® Hiseq-2000 platform. The biological relevance of dysregulated transcripts in each SLE subsets was assessed by ingenuity pathway analysis (IPA) software. We observed that dysregulation in the transcriptome expression pattern was clearly distinct in each SLE patients subsets. IPA analysis of transcripts uniquely expressed in different SLE groups revealed specific biological pathways to be affected in each SLE subsets. Multiple cytokine signaling pathways were specifically dysregulated in anti-dsDNA+ patients whereas Interferon signaling was predominantly dysregulated in anti-ENA+ patients. In anti-dsDNA+ENA+ patients regulation of actin based motility by Rho pathway was significantly affected. The granulocyte gene signature was a common feature to all SLE subsets; however, anti-dsDNA+ group showed relatively predominant expression of these genes. Dysregulation of Plasma cell related transcripts were higher in anti-dsDNA+ and anti-ENA+ patients as compared to anti-dsDNA+ ENA+. Association of specific canonical pathways with the uniquely expressed transcripts in each SLE subgroup indicates that specific immunological disease mechanisms are operative in distinct SLE patients’ subsets. This ‘sub-grouping’ approach could further be useful for clinical evaluation of SLE patients and devising targeted therapeutics. PMID:27835693

Transcriptome profiling analysis reveals biomarkers in colon cancer samples of various differentiation

PubMed Central

Yu, Tonghu; Zhang, Huaping; Qi, Hong

2018-01-01

The aim of the present study was to investigate more colon cancer-related genes in different stages. Gene expression profile E-GEOD-62932 was extracted for differentially expressed gene (DEG) screening. Series test of cluster analysis was used to obtain significant trending models. Based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases, functional and pathway enrichment analysis were processed and a pathway relation network was constructed. Gene co-expression network and gene signal network were constructed for common DEGs. The DEGs with the same trend were clustered and in total, 16 clusters with statistical significance were obtained. The screened DEGs were enriched into small molecule metabolic process and metabolic pathways. The pathway relation network was constructed with 57 nodes. A total of 328 common DEGs were obtained. Gene signal network was constructed with 71 nodes. Gene co-expression network was constructed with 161 nodes and 211 edges. ABCD3, CPT2, AGL and JAM2 are potential biomarkers for the diagnosis of colon cancer. PMID:29928385

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Harnessing pain heterogeneity and RNA transcriptome to identify blood-based pain biomarkers: a novel correlational study design and bioinformatics approach in a graded chronic constriction injury model.

PubMed

Grace, Peter M; Hurley, Daniel; Barratt, Daniel T; Tsykin, Anna; Watkins, Linda R; Rolan, Paul E; Hutchinson, Mark R

2012-09-01

A quantitative, peripherally accessible biomarker for neuropathic pain has great potential to improve clinical outcomes. Based on the premise that peripheral and central immunity contribute to neuropathic pain mechanisms, we hypothesized that biomarkers could be identified from the whole blood of adult male rats, by integrating graded chronic constriction injury (CCI), ipsilateral lumbar dorsal quadrant (iLDQ) and whole blood transcriptomes, and pathway analysis with pain behavior. Correlational bioinformatics identified a range of putative biomarker genes for allodynia intensity, many encoding for proteins with a recognized role in immune/nociceptive mechanisms. A selection of these genes was validated in a separate replication study. Pathway analysis of the iLDQ transcriptome identified Fcγ and Fcε signaling pathways, among others. This study is the first to employ the whole blood transcriptome to identify pain biomarker panels. The novel correlational bioinformatics, developed here, selected such putative biomarkers based on a correlation with pain behavior and formation of signaling pathways with iLDQ genes. Future studies may demonstrate the predictive ability of these biomarker genes across other models and additional variables. © 2012 The Authors. Journal of Neurochemistry © 2012 International Society for Neurochemistry.

From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints.

PubMed

Klamt, Steffen; Regensburger, Georg; Gerstl, Matthias P; Jungreuthmayer, Christian; Schuster, Stefan; Mahadevan, Radhakrishnan; Zanghellini, Jürgen; Müller, Stefan

2017-04-01

Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [FBA]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks.

From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints

PubMed Central

Klamt, Steffen; Gerstl, Matthias P.; Jungreuthmayer, Christian; Mahadevan, Radhakrishnan; Müller, Stefan

2017-01-01

Elementary flux modes (EFMs) emerged as a formal concept to describe metabolic pathways and have become an established tool for constraint-based modeling and metabolic network analysis. EFMs are characteristic (support-minimal) vectors of the flux cone that contains all feasible steady-state flux vectors of a given metabolic network. EFMs account for (homogeneous) linear constraints arising from reaction irreversibilities and the assumption of steady state; however, other (inhomogeneous) linear constraints, such as minimal and maximal reaction rates frequently used by other constraint-based techniques (such as flux balance analysis [FBA]), cannot be directly integrated. These additional constraints further restrict the space of feasible flux vectors and turn the flux cone into a general flux polyhedron in which the concept of EFMs is not directly applicable anymore. For this reason, there has been a conceptual gap between EFM-based (pathway) analysis methods and linear optimization (FBA) techniques, as they operate on different geometric objects. One approach to overcome these limitations was proposed ten years ago and is based on the concept of elementary flux vectors (EFVs). Only recently has the community started to recognize the potential of EFVs for metabolic network analysis. In fact, EFVs exactly represent the conceptual development required to generalize the idea of EFMs from flux cones to flux polyhedra. This work aims to present a concise theoretical and practical introduction to EFVs that is accessible to a broad audience. We highlight the close relationship between EFMs and EFVs and demonstrate that almost all applications of EFMs (in flux cones) are possible for EFVs (in flux polyhedra) as well. In fact, certain properties can only be studied with EFVs. Thus, we conclude that EFVs provide a powerful and unifying framework for constraint-based modeling of metabolic networks. PMID:28406903

Application of the critical pathway and integrated case teaching method to nursing orientation.

PubMed

Goodman, D

1997-01-01

Nursing staff development programs must be responsive to current changes in healthcare. New nursing staff must be prepared to manage continuous change and to function competently in clinical practice. The orientation pathway, based on a case management model, is used as a structure for the orientation phase of staff development. The integrated case is incorporated as a teaching strategy in orientation. The integrated case method is based on discussion and analysis of patient situations with emphasis on role modeling and integration of theory and skill. The orientation pathway and integrated case teaching method provide a useful framework for orientation of new staff. Educators, preceptors and orientees find the structure provided by the orientation pathway very useful. Orientation that is developed, implemented and evaluated based on a case management model with the use of an orientation pathway and incorporation of an integrated case teaching method provides a standardized structure for orientation of new staff. This approach is designed for the adult learner, promotes conceptual reasoning, and encourages the social and contextual basis for continued learning.

Donor-Specific Indirect Pathway Analysis Reveals a B-Cell-Independent Signature Which Reflects Outcomes in Kidney Transplant Recipients

PubMed Central

Haynes, L. D.; Jankowska-Gan, E.; Sheka, A.; Keller, M. R.; Hernandez-Fuentes, M. P.; Lechler, R. I.; Seyfert-Margolis, V.; Turka, L. A.; Newell, K. A.; Burlingham, W. J.

2012-01-01

To investigate the role of donor-specific indirect pathway T cells in renal transplant tolerance, we analyzed responses in peripheral blood of 45 patients using the trans-vivo delayed-type hypersensitivity assay. Subjects were enrolled into five groups—identical twin, clinically tolerant (TOL), steroid monotherapy (MONO), standard immunosuppression (SI) and chronic rejection (CR)—based on transplant type, posttransplant immunosuppression and graft function. The indirect pathway was active in all groups except twins but distinct intergroup differences were evident, corresponding to clinical status. The antidonor indirect pathway T effector response increased across patient groups (TOL < MONO < SI < CR; p < 0.0001) whereas antidonor indirect pathway T regulatory response decreased (TOL > MONO = SI > CR; p < 0.005). This pattern differed from that seen in circulating naïve B-cell numbers and in a cross-platform biomarker analysis, where patients on monotherapy were not ranked closest to TOL patients, but rather were indistinguishable from chronically rejecting patients. Cross-sectional analysis of the indirect pathway revealed a spectrum in T-regulatory:T-effector balance, ranging from TOL patients having predominantly regulatory responses to CR patients having predominantly effector responses. Therefore, the indirect pathway measurements reflect a distinct aspect of tolerance from the recently reported elevation of circulating naïve B cells, which was apparent only in recipients off immunosuppression. PMID:22151236

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

PubMed

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy.

An integrated approach to demonstrating the ANR pathway of proanthocyanidin biosynthesis in plants.

PubMed

Peng, Qing-Zhong; Zhu, Yue; Liu, Zhong; Du, Ci; Li, Ke-Gang; Xie, De-Yu

2012-09-01

Proanthocyanidins (PAs) are oligomers or polymers of plant flavan-3-ols and are important to plant adaptation in extreme environmental conditions. The characterization of anthocyanidin reductase (ANR) and leucoanthocyanidin reductase (LAR) has demonstrated the different biogenesis of four stereo-configurations of flavan-3-ols. It is important to understand whether ANR and the ANR pathway widely occur in the plant kingdom. Here, we report an integrated approach to demonstrate the ANR pathway in plants. This includes different methods to extract native ANR from different tissues of eight angiosperm plants (Lotus corniculatus, Desmodium uncinatum, Medicago sativa, Hordeum vulgare, Vitis vinifera, Vitis bellula, Parthenocissus heterophylla, and Cerasus serrulata) and one fern plant (Dryopteris pycnopteroides), a general enzymatic analysis approach to demonstrate the ANR activity, high-performance liquid chromatography-based fingerprinting to demonstrate (-)-epicatechin and other flavan-3-ol molecules, and phytochemical analysis of PAs. Results demonstrate that in addition to leaves of M. sativa, tissues of other eight plants contain an active ANR pathway. Particularly, the leaves, flowers and pods of D. uncinatum, which is a model plant to study LAR and the LAR pathways, are demonstrated to express an active ANR pathway. This finding suggests that the ANR pathway involves PA biosynthesis in D. uncinatum. In addition, a sequence BLAST analysis reveals that ANR homologs have been sequenced in plants from both gymnosperms and angiosperms. These data show that the ANR pathway to PA biosynthesis occurs in both seed and seedless vascular plants.

Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics.

PubMed

Piao, Guanying; Wu, Jiarui

2013-11-01

To analyze the differentially expressed genes and identify featured biomarkers from prostatic carcinoma. The software "Significance Analysis of Microarray" (SAM) was used to identify the differentially coexpressed genes (DCGs). The DCGs existed in two datasets were analyzed by GO (Gene Ontology) functional annotation. A total of 389 DCGs were obtained. By GO analysis, we found these DCGs were closely related with the acinus development, TGF-β receptor and signal transduction pathways. Furthermore, five featured biomarkers were discovered by interaction analysis. These important signal pathways and oncogenes may provide potential therapeutic targets for prostatic carcinoma.

Comprehensive detection of genes causing a phenotype using phenotype sequencing and pathway analysis.

PubMed

Harper, Marc; Gronenberg, Luisa; Liao, James; Lee, Christopher

2014-01-01

Discovering all the genetic causes of a phenotype is an important goal in functional genomics. We combine an experimental design for detecting independent genetic causes of a phenotype with a high-throughput sequencing analysis that maximizes sensitivity for comprehensively identifying them. Testing this approach on a set of 24 mutant strains generated for a metabolic phenotype with many known genetic causes, we show that this pathway-based phenotype sequencing analysis greatly improves sensitivity of detection compared with previous methods, and reveals a wide range of pathways that can cause this phenotype. We demonstrate our approach on a metabolic re-engineering phenotype, the PEP/OAA metabolic node in E. coli, which is crucial to a substantial number of metabolic pathways and under renewed interest for biofuel research. Out of 2157 mutations in these strains, pathway-phenoseq discriminated just five gene groups (12 genes) as statistically significant causes of the phenotype. Experimentally, these five gene groups, and the next two high-scoring pathway-phenoseq groups, either have a clear connection to the PEP metabolite level or offer an alternative path of producing oxaloacetate (OAA), and thus clearly explain the phenotype. These high-scoring gene groups also show strong evidence of positive selection pressure, compared with strictly neutral selection in the rest of the genome.

Pathways Involved in Sasang Constitution from Genome-Wide Analysis in a Korean Population

PubMed Central

Yu, Sung-Gon; Kim, Jong-Yeol; Song, Kwang Hoon

2012-01-01

Abstract Objective Sasang constitution (SC) medicine, a branch of Korean traditional medicine, classifies the individual into one of four constitutional types (Taeum, TE; Soeum, SE; Soyang, SY; and Taeyang, TY) based on physiologic characteristics. The authors of the current article recently reported individual genetic elements associated with SC types via genome-wide association (GWA) analysis. However, to understand the biologic mechanisms underlying constitution, a comprehensive approach that combines individual genetic effects was applied. Design Genotypes of 1222 subjects of defined constitution types were measured for 341,998 genetic loci across the entire genome. The biologic pathways associated with SC types were identified via GWA analysis using three different algorithms—namely, the Z-static method, a restandardized gene set assay, and a gene set enrichment assay. Results Distinct pathways were associated (p<0.05) with each constitution type. The TE type was significantly associated with cytoskeleton-related pathways. The SE type was significantly associated with cardio- and amino-acid metabolism–related pathways. The SY type was associated with enriched melanoma-related pathways. TY subjects were excluded because of the small size of that sample. Among these functionally related pathways, core-node genes regulating multiple pathways were identified. TJP1, PTK2, and SRC were selected as core-nodes for TE; RHOA, and MAOA/MAOB for SE; and GNAO1 for SY (p<0.05), respectively. Conclusions The current authors systematically identified the biologic pathways and core-node genes associated with SC types from the GWA study; this information should provide insights regarding the molecular mechanisms inherent in constitutional pathophysiology. PMID:22889377

Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways.

PubMed

Wexler, Eric M; Rosen, Ezra; Lu, Daning; Osborn, Gregory E; Martin, Elizabeth; Raybould, Helen; Geschwind, Daniel H

2011-10-04

Wnt proteins are critical to mammalian brain development and function. The canonical Wnt signaling pathway involves the stabilization and nuclear translocation of β-catenin; however, Wnt also signals through alternative, noncanonical pathways. To gain a systems-level, genome-wide view of Wnt signaling, we analyzed Wnt1-stimulated changes in gene expression by transcriptional microarray analysis in cultured human neural progenitor (hNP) cells at multiple time points over a 72-hour time course. We observed a widespread oscillatory-like pattern of changes in gene expression, involving components of both the canonical and the noncanonical Wnt signaling pathways. A higher-order, systems-level analysis that combined independent component analysis, waveform analysis, and mutual information-based network construction revealed effects on pathways related to cell death and neurodegenerative disease. Wnt effectors were tightly clustered with presenilin1 (PSEN1) and granulin (GRN), which cause dominantly inherited forms of Alzheimer's disease and frontotemporal dementia (FTD), respectively. We further explored a potential link between Wnt1 and GRN and found that Wnt1 decreased GRN expression by hNPs. Conversely, GRN knockdown increased WNT1 expression, demonstrating that Wnt and GRN reciprocally regulate each other. Finally, we provided in vivo validation of the in vitro findings by analyzing gene expression data from individuals with FTD. These unbiased and genome-wide analyses provide evidence for a connection between Wnt signaling and the transcriptional regulation of neurodegenerative disease genes.

Racial disparity in pathophysiologic pathways of preterm birth based on genetic variants

PubMed Central

Menon, Ramkumar; Pearce, Brad; Velez, Digna R; Merialdi, Mario; Williams, Scott M; Fortunato, Stephen J; Thorsen, Poul

2009-01-01

Objective To study pathophysiologic pathways in spontaneous preterm birth and possibly the racial disparity associating with maternal and fetal genetic variations, using bioinformatics tools. Methods A large scale candidate gene association study was performed on 1442 SNPs in 130 genes in a case (preterm birth < 36 weeks) control study (term birth > 37 weeks). Both maternal and fetal DNA from Caucasians (172 cases and 198 controls) and 279 African-Americans (82 cases and 197 controls) were used. A single locus association (genotypic) analysis followed by hierarchical clustering was performed, where clustering was based on p values for significant associations within each race. Using Ingenuity Pathway Analysis (IPA) software, known pathophysiologic pathways in both races were determined. Results From all SNPs entered into the analysis, the IPA mapped genes to specific disease functions. Gene variants in Caucasians were implicated in disease functions shared with other known disorders; specifically, dermatopathy, inflammation, and hematological disorders. This may reflect abnormal cervical ripening and decidual hemorrhage. In African-Americans inflammatory pathways were the most prevalent. In Caucasians, maternal gene variants showed the most prominent role in disease functions, whereas in African Americans it was fetal variants. The IPA software was used to generate molecular interaction maps that differed between races and also between maternal and fetal genetic variants. Conclusion Differences at the genetic level revealed distinct disease functions and operational pathways in African Americans and Caucasians in spontaneous preterm birth. Differences in maternal and fetal contributions in pregnancy outcome are also different between African Americans and Caucasians. These results present a set of explicit testable hypotheses regarding genetic associations with preterm birth in African Americans and Caucasians PMID:19527514

Metabolomics and proteomics technologies to explore the herbal preparation affecting metabolic disorders using high resolution mass spectrometry.

PubMed

Zhang, Aihua; Zhou, Xiaohang; Zhao, Hongwei; Zou, Shiyu; Ma, Chung Wah; Liu, Qi; Sun, Hui; Liu, Liang; Wang, Xijun

2017-01-31

An integrative metabolomics and proteomics approach can provide novel insights in the understanding of biological systems. We have integrated proteome and metabolome data sets for a holistic view of the molecular mechanisms in disease. Using quantitative iTRAQ-LC-MS/MS proteomics coupled with UPLC-Q-TOF-HDMS based metabolomics, we determined the protein and metabolite expression changes in the kidney-yang deficiency syndrome (KYDS) rat model and further investigated the intervention effects of the Jinkui Shenqi Pill (JSP). The VIP-plot of the orthogonal PLS-DA (OPLS-DA) was used for discovering the potential biomarkers to clarify the therapeutic mechanisms of JSP in treating KYDS. The results showed that JSP can alleviate the kidney impairment induced by KYDS. Sixty potential biomarkers, including 5-l-glutamyl-taurine, phenylacetaldehyde, 4,6-dihydroxyquinoline, and xanthurenic acid etc., were definitely up- or down-regulated. The regulatory effect of JSP on the disturbed metabolic pathways was proved by the established metabonomic method. Using pathway analyses, we identified the disturbed metabolic pathways such as taurine and hypotaurine metabolism, pyrimidine metabolism, tyrosine metabolism, tryptophan metabolism, histidine metabolism, steroid hormone biosynthesis, etc. Furthermore, using iTRAQ-based quantitative proteomics analysis, seventeen differential proteins were identified and significantly altered by the JSP treatment. These proteins appear to be involved in Wnt, chemokine, PPAR, and MAPK signaling pathways, etc. Functional pathway analysis revealed that most of the proteins were found to play a key role in the regulation of metabolism pathways. Bioinformatics analysis with the IPA software found that these differentially-expressed moleculars had a strong correlation with the α-adrenergic signaling, FGF signaling, etc. Our data indicate that high-throughput metabolomics and proteomics can provide an insight on the herbal preparations affecting the metabolic disorders using high resolution mass spectrometry.

[Study on action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis based on techniques of gene expression profile and molecular fingerprint].

PubMed

Zhou, Wei; Song, Xiang-gang; Chen, Chao; Wang, Shu-mei; Liang, Sheng-wang

2015-08-01

Action mechanism and material base of compound Danshen dripping pills in treatment of carotid atherosclerosis were discussed based on gene expression profile and molecular fingerprint in this paper. First, gene expression profiles of atherosclerotic carotid artery tissues and histologically normal tissues in human body were collected, and were screened using significance analysis of microarray (SAM) to screen out differential gene expressions; then differential genes were analyzed by Gene Ontology (GO) analysis and KEGG pathway analysis; to avoid some genes with non-outstanding differential expression but biologically importance, Gene Set Enrichment Analysis (GSEA) were performed, and 7 chemical ingredients with higher negative enrichment score were obtained by Cmap method, implying that they could reversely regulate the gene expression profiles of pathological tissues; and last, based on the hypotheses that similar structures have similar activities, 336 ingredients of compound Danshen dripping pills were compared with 7 drug molecules in 2D molecular fingerprints method. The results showed that 147 differential genes including 60 up-regulated genes and 87 down regulated genes were screened out by SAM. And in GO analysis, Biological Process ( BP) is mainly concerned with biological adhesion, response to wounding and inflammatory response; Cellular Component (CC) is mainly concerned with extracellular region, extracellular space and plasma membrane; while Molecular Function (MF) is mainly concerned with antigen binding, metalloendopeptidase activity and peptide binding. KEGG pathway analysis is mainly concerned with JAK-STAT, RIG-I like receptor and PPAR signaling pathway. There were 10 compounds, such as hexadecane, with Tanimoto coefficients greater than 0.85, which implied that they may be the active ingredients (AIs) of compound Danshen dripping pills in treatment of carotid atherosclerosis (CAs). The present method can be applied to the research on material base and molecular action mechanism of TCM.

The large-scale organization of shape processing in the ventral and dorsal pathways

PubMed Central

Culham, Jody C; Plaut, David C; Behrmann, Marlene

2017-01-01

Although shape perception is considered a function of the ventral visual pathway, evidence suggests that the dorsal pathway also derives shape-based representations. In two psychophysics and neuroimaging experiments, we characterized the response properties, topographical organization and perceptual relevance of these representations. In both pathways, shape sensitivity increased from early visual cortex to extrastriate cortex but then decreased in anterior regions. Moreover, the lateral aspect of the ventral pathway and posterior regions of the dorsal pathway were sensitive to the availability of fundamental shape properties, even for unrecognizable images. This apparent representational similarity between the posterior-dorsal and lateral-ventral regions was corroborated by a multivariate analysis. Finally, as with ventral pathway, the activation profile of posterior dorsal regions was correlated with recognition performance, suggesting a possible contribution to perception. These findings challenge a strict functional dichotomy between the pathways and suggest a more distributed model of shape processing. PMID:28980938

ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework.

PubMed

Zhang, Kunlin; Chang, Suhua; Cui, Sijia; Guo, Liyuan; Zhang, Liuyan; Wang, Jing

2011-07-01

Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no web server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap between GWAS and disease mechanism study by generating hypothesis of SNP → gene → pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma.

PubMed

Li, Chaoxing; Liu, Li; Dinu, Valentin

2018-01-01

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.

An Economic Evaluation of Home Versus Laboratory-Based Diagnosis of Obstructive Sleep Apnea

PubMed Central

Kim, Richard D.; Kapur, Vishesh K.; Redline-Bruch, Julie; Rueschman, Michael; Auckley, Dennis H.; Benca, Ruth M.; Foldvary-Schafer, Nancy R.; Iber, Conrad; Zee, Phyllis C.; Rosen, Carol L.; Redline, Susan; Ramsey, Scott D.

2015-01-01

Study Objectives: We conducted an economic analysis of the HomePAP study, a multicenter randomized clinical trial that compared home-based versus laboratory-based testing for the diagnosis and management of obstructive sleep apnea (OSA). Design: A cost-minimization analysis from the payer and provider perspectives was performed, given that 3-mo clinical outcomes were equivalent. Setting: Seven academic sleep centers. Participants: There were 373 subjects at high risk for moderate to severe OSA. Interventions: Subjects were randomized to either home-based limited channel portable monitoring followed by unattended autotitration with continuous positive airway pressure (CPAP), versus a traditional pathway of in-laboratory sleep study and CPAP titration. Measurements and Results: From the payer perspective, per subject costs for the laboratory-based pathway were $1,840 (95% confidence interval [CI] $1,660, $2,015) compared to $1,575 (95% CI $1,439, $1,716) for the home-based pathway under the base case. Costs were $264 (95% CI $39, $496, P = 0.02) in favor of the home arm. From the provider perspective, per subject costs for the laboratory arm were $1,697 (95% CI $1,566, $1,826) compared to $1,736 (95% CI $1,621, $1,857) in the home arm, for a difference of $40 (95% CI −$213, $142, P = 0.66) in favor of the laboratory arm under the base case. The provider operating margin was $142 (95% CI $85, $202,P < 0.01) in the laboratory arm, compared to a loss of −$161 (95% CI −$202, −$120, P < 0.01) in the home arm. Conclusions: For payers, a home-based diagnostic pathway for obstructive sleep apnea with robust patient support incurs fewer costs than a laboratory-based pathway. For providers, costs are comparable if not higher, resulting in a negative operating margin. Clinicaltrials.gov Identifier: NCT00642486. Citation: Kim RD, Kapur VK, Redline-Bruch J, Rueschman M, Auckley DH, Benca RM, Foldvary-Schafer NR, Iber C, Zee PC, Rosen CL, Redline S, Ramsey SD. An economic evaluation of home versus laboratory-based diagnosis of obstructive sleep apnea. SLEEP 2015;38(7):1027–1037. PMID:26118558

Synergistic Modification Induced Specific Recognition between Histone and TRIM24 via Fluctuation Correlation Network Analysis

NASA Astrophysics Data System (ADS)

Zhang, Jinmai; Luo, Huajie; Liu, Hao; Ye, Wei; Luo, Ray; Chen, Hai-Feng

2016-04-01

Histone modification plays a key role in gene regulation and gene expression. TRIM24 as a histone reader can recognize histone modification. However the specific recognition mechanism between TRIM24 and histone modification is unsolved. Here, systems biology method of dynamics correlation network based on molecular dynamics simulation was used to answer the question. Our network analysis shows that the dynamics correlation network of H3K23ac is distinctly different from that of wild type and other modifications. A hypothesis of “synergistic modification induced recognition” is then proposed to link histone modification and TRIM24 binding. These observations were further confirmed from community analysis of networks with mutation and network perturbation. Finally, a possible recognition pathway is also identified based on the shortest path search for H3K23ac. Significant difference of recognition pathway was found among different systems due to methylation and acetylation modifications. The analysis presented here and other studies show that the dynamic network-based analysis might be a useful general strategy to study the biology of protein post-translational modification and associated recognition.

Parallel labeling experiments for pathway elucidation and (13)C metabolic flux analysis.

PubMed

Antoniewicz, Maciek R

2015-12-01

Metabolic pathway models provide the foundation for quantitative studies of cellular physiology through the measurement of intracellular metabolic fluxes. For model organisms metabolic models are well established, with many manually curated genome-scale model reconstructions, gene knockout studies and stable-isotope tracing studies. However, for non-model organisms a similar level of knowledge is often lacking. Compartmentation of cellular metabolism in eukaryotic systems also presents significant challenges for quantitative (13)C-metabolic flux analysis ((13)C-MFA). Recently, innovative (13)C-MFA approaches have been developed based on parallel labeling experiments, the use of multiple isotopic tracers and integrated data analysis, that allow more rigorous validation of pathway models and improved quantification of metabolic fluxes. Applications of these approaches open new research directions in metabolic engineering, biotechnology and medicine. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions.

PubMed

Roy, Raktim; Shilpa, P Phani; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level. Systems biology-Microgravity-Pathways and networks-Bacteria. Astrobiology 16, 677-689.

Transcriptome analysis supports viral infection and fluoride toxicity as contributors to chronic kidney disease of unknown etiology (CKDu) in Sri Lanka.

PubMed

Sayanthooran, Saravanabavan; Gunerathne, Lishanthe; Abeysekera, Tilak D J; Magana-Arachchi, Dhammika N

2018-05-28

Chronic kidney disease of unknown etiology (CKDu), having epidemic characteristics, is being diagnosed increasingly in certain tropical regions of the world, mainly Latin America and Sri Lanka. They have been observed primarily in farming communities and current hypotheses point toward many environmental and occupational triggers. CKDu does not have common etiologies of chronic kidney disease (CKD) such as hypertension, diabetes, or autoimmune disease. We aimed to understand the molecular processes underlying CKDu in Sri Lanka using transcriptome analysis. RNA extracted from whole blood was reverse transcribed and used for microarray analysis using the Human HT-12 v.4 array (Illumina). Pathway analysis was carried out using ingenuity pathway analysis (IPA-Qiagen). Microarray results were validated using real-time PCR of five selected genes. Pathways related to innate immune response, including interferon signaling, inflammasome signaling and TREM1 signaling had the most significant positive activation z scores, where as EIF2 signaling and mTOR signaling had the most significant negative activation z scores. Pathways previously linked to fluoride toxicity; G-protein activation, Cdc42 signaling, Rac signaling and RhoA signaling were activated in CKDu patients. The most significantly activated biological functions were cell death, cell movement and antimicrobial response. Significant toxicological functions were mitochondrial dysfunction, oxidative stress and apoptosis. Based on the molecular pathway analysis in CKDu patients and review of literature, viral infections and fluoride toxicity appear to be contributing to the molecular mechanisms underlying CKDu.

A network model of genomic hormone interactions underlying dementia and its translational validation through serendipitous off-target effect

PubMed Central

2013-01-01

Background While the majority of studies have focused on the association between sex hormones and dementia, emerging evidence supports the role of other hormone signals in increasing dementia risk. However, due to the lack of an integrated view on mechanistic interactions of hormone signaling pathways associated with dementia, molecular mechanisms through which hormones contribute to the increased risk of dementia has remained unclear and capacity of translating hormone signals to potential therapeutic and diagnostic applications in relation to dementia has been undervalued. Methods Using an integrative knowledge- and data-driven approach, a global hormone interaction network in the context of dementia was constructed, which was further filtered down to a model of convergent hormone signaling pathways. This model was evaluated for its biological and clinical relevance through pathway recovery test, evidence-based analysis, and biomarker-guided analysis. Translational validation of the model was performed using the proposed novel mechanism discovery approach based on ‘serendipitous off-target effects’. Results Our results reveal the existence of a well-connected hormone interaction network underlying dementia. Seven hormone signaling pathways converge at the core of the hormone interaction network, which are shown to be mechanistically linked to the risk of dementia. Amongst these pathways, estrogen signaling pathway takes the major part in the model and insulin signaling pathway is analyzed for its association to learning and memory functions. Validation of the model through serendipitous off-target effects suggests that hormone signaling pathways substantially contribute to the pathogenesis of dementia. Conclusions The integrated network model of hormone interactions underlying dementia may serve as an initial translational platform for identifying potential therapeutic targets and candidate biomarkers for dementia-spectrum disorders such as Alzheimer’s disease. PMID:23885764

Pathway-based discovery of genetic interactions in breast cancer

PubMed Central

Xu, Zack Z.; Boone, Charles; Lange, Carol A.

2017-01-01

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions. PMID:28957314

Rhizoma Dioscoreae extract protects against alveolar bone loss by regulating the cell cycle: A predictive study based on the protein‑protein interaction network.

PubMed

Zhang, Zhi-Guo; Song, Chang-Heng; Zhang, Fang-Zhen; Chen, Yan-Jing; Xiang, Li-Hua; Xiao, Gary Guishan; Ju, Da-Hong

2016-06-01

Rhizoma Dioscoreae extract (RDE) exhibits a protective effect on alveolar bone loss in ovariectomized (OVX) rats. The aim of this study was to predict the pathways or targets that are regulated by RDE, by re‑assessing our previously reported data and conducting a protein‑protein interaction (PPI) network analysis. In total, 383 differentially expressed genes (≥3‑fold) between alveolar bone samples from the RDE and OVX group rats were identified, and a PPI network was constructed based on these genes. Furthermore, four molecular clusters (A‑D) in the PPI network with the smallest P‑values were detected by molecular complex detection (MCODE) algorithm. Using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Ingenuity Pathway Analysis (IPA) tools, two molecular clusters (A and B) were enriched for biological process in Gene Ontology (GO). Only cluster A was associated with biological pathways in the IPA database. GO and pathway analysis results showed that cluster A, associated with cell cycle regulation, was the most important molecular cluster in the PPI network. In addition, cyclin‑dependent kinase 1 (CDK1) may be a key molecule achieving the cell‑cycle‑regulatory function of cluster A. From the PPI network analysis, it was predicted that delayed cell cycle progression in excessive alveolar bone remodeling via downregulation of CDK1 may be another mechanism underling the anti‑osteopenic effect of RDE on alveolar bone.

Transcriptomic analysis in the developing zebrafish embryo after compound exposure: Individual gene expression and pathway regulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hermsen, Sanne A.B., E-mail: Sanne.Hermsen@rivm.nl; Department of Toxicogenomics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht; Institute for Risk Assessment Sciences

2013-10-01

The zebrafish embryotoxicity test is a promising alternative assay for developmental toxicity. Classically, morphological assessment of the embryos is applied to evaluate the effects of compound exposure. However, by applying differential gene expression analysis the sensitivity and predictability of the test may be increased. For defining gene expression signatures of developmental toxicity, we explored the possibility of using gene expression signatures of compound exposures based on commonly expressed individual genes as well as based on regulated gene pathways. Four developmental toxic compounds were tested in concentration-response design, caffeine, carbamazepine, retinoic acid and valproic acid, and two non-embryotoxic compounds, D-mannitol andmore » saccharin, were included. With transcriptomic analyses we were able to identify commonly expressed genes, which were mostly development related, after exposure to the embryotoxicants. We also identified gene pathways regulated by the embryotoxicants, suggestive of their modes of action. Furthermore, whereas pathways may be regulated by all compounds, individual gene expression within these pathways can differ for each compound. Overall, the present study suggests that the use of individual gene expression signatures as well as pathway regulation may be useful starting points for defining gene biomarkers for predicting embryotoxicity. - Highlights: • The zebrafish embryotoxicity test in combination with transcriptomics was used. • We explored two approaches of defining gene biomarkers for developmental toxicity. • Four compounds in concentration-response design were tested. • We identified commonly expressed individual genes as well as regulated gene pathways. • Both approaches seem suitable starting points for defining gene biomarkers.« less

Quantitative proteomic analysis of milk fat globule membrane (MFGM) proteins in human and bovine colostrum and mature milk samples through iTRAQ labeling.

PubMed

Yang, Mei; Cong, Min; Peng, Xiuming; Wu, Junrui; Wu, Rina; Liu, Biao; Ye, Wenhui; Yue, Xiqing

2016-05-18

Milk fat globule membrane (MFGM) proteins have many functions. To explore the different proteomics of human and bovine MFGM, MFGM proteins were separated from human and bovine colostrum and mature milk, and analyzed by the iTRAQ proteomic approach. A total of 411 proteins were recognized and quantified. Among these, 232 kinds of differentially expressed proteins were identified. These differentially expressed proteins were analyzed based on multivariate analysis, gene ontology (GO) annotation and KEGG pathway. Biological processes involved were response to stimulus, localization, establishment of localization, and the immune system process. Cellular components engaged were the extracellular space, extracellular region parts, cell fractions, and vesicles. Molecular functions touched upon were protein binding, nucleotide binding, and enzyme inhibitor activity. The KEGG pathway analysis showed several pathways, including regulation of the actin cytoskeleton, focal adhesion, neurotrophin signaling pathway, leukocyte transendothelial migration, tight junction, complement and coagulation cascades, vascular endothelial growth factor signaling pathway, and adherens junction. These results enhance our understanding of different proteomes of human and bovine MFGM across different lactation phases, which could provide important information and potential directions for the infant milk powder and functional food industries.

Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

PubMed

Maes, Michael; Nowak, Gabriel; Caso, Javier R; Leza, Juan Carlos; Song, Cai; Kubera, Marta; Klein, Hans; Galecki, Piotr; Noto, Cristiano; Glaab, Enrico; Balling, Rudi; Berk, Michael

2016-07-01

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

[Transcriptome analysis of Dunaliella viridis].

PubMed

Zhu, Shuai-qi; Gong, Yi-fu; Hang, Yu-qing; Liu, Hao; Wang, He-yu

2015-08-01

In order to understand the gene information, function, haloduric pathway (glycerolipid metabolism) and related key genes for Dunaliella viridis, we used Illumina HiSeqTM 2000 high-throughput sequencing technology to sequence its transcriptome. Trinity soft was used to assemble the data to form transcripts. Based on the Clusters of Orthologous Groups (COG), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG ) databases, we carried out functional annotation and classification, pathway annotation, and the opening reading fragment (ORF) sequence prediction of transcripts. The key genes in the glycerolipid metabolism were analyzed. The results suggested that 81,593 transcripts were found, and 77,117 ORF sequences were predicted, accounting for 94.50% of all transcripts. COG classification results showed that 16,569 transcripts were assigned to 24 categories. GO classification annotated 76,436 transcripts. The number of transcripts for biologcial processes was 30,678, accounting for 40.14% of all transcripts. KEGG pathway analysis showed that 26,428 transcripts were annotated to 317 pathways, and 131 pathways were related to metabolism, accounting for 41.32% of all annotated pathways. Only one transcript was annotated as coding the key enzyme dihydroxyacetone kinase involved in the glycerolipid pathway. This enzyme could be related to glycerol biosynthesis under salt stress. This study further improved the gene information and laid the foundation of metabolic pathway research for Dunaliella viridis.

Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways

PubMed Central

Li, Chunquan; Han, Junwei; Yao, Qianlan; Zou, Chendan; Xu, Yanjun; Zhang, Chunlong; Shang, Desi; Zhou, Lingyun; Zou, Chaoxia; Sun, Zeguo; Li, Jing; Zhang, Yunpeng; Yang, Haixiu; Gao, Xu; Li, Xia

2013-01-01

Various ‘omics’ technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways. PMID:23482392

Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma

PubMed Central

Liu, Li; Dinu, Valentin

2018-01-01

Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway’s topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher’s exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level. PMID:29666752

Identification of differential pathways in papillary thyroid carcinoma utilizing pathway co-expression analysis.

PubMed

Qiu, Wei-Hai; Chen, Gui-Yan; Cui, Lu; Zhang, Ting-Ming; Wei, Feng; Yang, Yong

2016-01-01

To identify differential pathways between papillary thyroid carcinoma (PTC) patients and normal controls utilizing a novel method which combined pathway with co-expression network. The proposed method included three steps. In the first step, we conducted pretreatments for background pathways and gained representative pathways in PTC. Subsequently, a co-expression network for representative pathways was constructed using empirical Bayes (EB) approach to assign a weight value for each pathway. Finally, random model was extracted to set the thresholds of identifying differential pathways. We obtained 1267 representative pathways and their weight values based on the co-expressed pathway network, and then by meeting the criterion (Weight > 0.0296), 87 differential pathways in total across PTC patients and normal controls were identified. The top three ranked differential pathways were CREB phosphorylation, attachment of GPI anchor to urokinase plasminogen activator receptor (uPAR) and loss of function of SMAD2/3 in cancer. In conclusion, we successfully identified differential pathways (such as CREB phosphorylation, attachment of GPI anchor to uPAR and post-translational modification: synthesis of GPI-anchored proteins) for PTC using the proposed pathway co-expression method, and these pathways might be potential biomarkers for target therapy and detection of PTC.

A Systems Biology Analysis Unfolds the Molecular Pathways and Networks of Two Proteobacteria in Spaceflight and Simulated Microgravity Conditions

NASA Astrophysics Data System (ADS)

Roy, Raktim; Phani Shilpa, P.; Bagh, Sangram

2016-09-01

Bacteria are important organisms for space missions due to their increased pathogenesis in microgravity that poses risks to the health of astronauts and for projected synthetic biology applications at the space station. We understand little about the effect, at the molecular systems level, of microgravity on bacteria, despite their significant incidence. In this study, we proposed a systems biology pipeline and performed an analysis on published gene expression data sets from multiple seminal studies on Pseudomonas aeruginosa and Salmonella enterica serovar Typhimurium under spaceflight and simulated microgravity conditions. By applying gene set enrichment analysis on the global gene expression data, we directly identified a large number of new, statistically significant cellular and metabolic pathways involved in response to microgravity. Alteration of metabolic pathways in microgravity has rarely been reported before, whereas in this analysis metabolic pathways are prevalent. Several of those pathways were found to be common across studies and species, indicating a common cellular response in microgravity. We clustered genes based on their expression patterns using consensus non-negative matrix factorization. The genes from different mathematically stable clusters showed protein-protein association networks with distinct biological functions, suggesting the plausible functional or regulatory network motifs in response to microgravity. The newly identified pathways and networks showed connection with increased survival of pathogens within macrophages, virulence, and antibiotic resistance in microgravity. Our work establishes a systems biology pipeline and provides an integrated insight into the effect of microgravity at the molecular systems level.

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms.

PubMed

Jani, Saurin D; Argraves, Gary L; Barth, Jeremy L; Argraves, W Scott

2010-04-01

An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases. Here we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina. GeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG pathway diagrams and heatmaps of expression intensity values. GeneMesh is freely available online at http://proteogenomics.musc.edu/genemesh/.

Process modeling and supply chain design for advanced biofuel production based on bio-oil gasification

NASA Astrophysics Data System (ADS)

Li, Qi

As a potential substitute for petroleum-based fuel, second generation biofuels are playing an increasingly important role due to their economic, environmental, and social benefits. With the rapid development of biofuel industry, there has been an increasing literature on the techno-economic analysis and supply chain design for biofuel production based on a variety of production pathways. A recently proposed production pathway of advanced biofuel is to convert biomass to bio-oil at widely distributed small-scale fast pyrolysis plants, then gasify the bio-oil to syngas and upgrade the syngas to transportation fuels in centralized biorefinery. This thesis aims to investigate two types of assessments on this bio-oil gasification pathway: techno-economic analysis based on process modeling and literature data; supply chain design with a focus on optimal decisions for number of facilities to build, facility capacities and logistic decisions considering uncertainties. A detailed process modeling with corn stover as feedstock and liquid fuels as the final products is presented. Techno-economic analysis of the bio-oil gasification pathway is also discussed to assess the economic feasibility. Some preliminary results show a capital investment of 438 million dollar and minimum fuel selling price (MSP) of $5.6 per gallon of gasoline equivalent. The sensitivity analysis finds that MSP is most sensitive to internal rate of return (IRR), biomass feedstock cost, and fixed capital cost. A two-stage stochastic programming is formulated to solve the supply chain design problem considering uncertainties in biomass availability, technology advancement, and biofuel price. The first-stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants and the centralized biorefinery while the second-stage determines the biomass and biofuel flows. The numerical results and case study illustrate that considering uncertainties can be pivotal in this supply chain design and optimization problem. Also, farmers' participation has a significant effect on the decision making process.

Understanding alternative fluxes/effluxes through comparative metabolic pathway analysis of phylum actinobacteria using a simplified approach.

PubMed

Verma, Mansi; Lal, Devi; Saxena, Anjali; Anand, Shailly; Kaur, Jasvinder; Kaur, Jaspreet; Lal, Rup

2013-12-01

Actinobacteria are known for their diverse metabolism and physiology. Some are dreadful human pathogens whereas some constitute the natural flora for human gut. Therefore, the understanding of metabolic pathways is a key feature for targeting the pathogenic bacteria without disturbing the symbiotic ones. A big challenge faced today is multiple drug resistance by Mycobacterium and other pathogens that utilize alternative fluxes/effluxes. With the availability of genome sequence, it is now feasible to conduct the comparative in silico analysis. Here we present a simplified approach to compare metabolic pathways so that the species specific enzyme may be traced and engineered for future therapeutics. The analyses of four key carbohydrate metabolic pathways, i.e., glycolysis, pyruvate metabolism, tri carboxylic acid cycle and pentose phosphate pathway suggest the presence of alternative fluxes. It was found that the upper pathway of glycolysis was highly variable in the actinobacterial genomes whereas lower glycolytic pathway was highly conserved. Likewise, pentose phosphate pathway was well conserved in contradiction to TCA cycle, which was found to be incomplete in majority of actinobacteria. The clustering based on presence and absence of genes of these metabolic pathways clearly revealed that members of different genera shared identical pathways and, therefore, provided an easy method to identify the metabolic similarities/differences between pathogenic and symbiotic organisms. The analyses could identify isoenzymes and some key enzymes that were found to be missing in some pathogenic actinobacteria. The present work defines a simple approach to explore the effluxes in four metabolic pathways within the phylum actinobacteria. The analysis clearly reflects that actinobacteria exhibit diverse routes for metabolizing substrates. The pathway comparison can help in finding the enzymes that can be used as drug targets for pathogens without effecting symbiotic organisms within the same host. This may help to prevail over the multiple drug resistance, for designing broad spectrum drugs, in food industries and other clinical research areas. © 2013.

An integrated bioinformatics approach to improve two-color microarray quality-control: impact on biological conclusions.

PubMed

van Haaften, Rachel I M; Luceri, Cristina; van Erk, Arie; Evelo, Chris T A

2009-06-01

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

Immunomodulatory Activity of Ganoderma atrum Polysaccharide on Purified T Lymphocytes through Ca2+/CaN and Mitogen-Activated Protein Kinase Pathway Based on RNA Sequencing.

PubMed

Xiang, Quan-Dan; Yu, Qiang; Wang, Hui; Zhao, Ming-Ming; Liu, Shi-Yu; Nie, Shao-Ping; Xie, Ming-Yong

2017-07-05

Our previous study has demonstrated that Ganoderma atrum polysaccharide (PSG-1) has immunomodulatory activity on spleen lymphocytes. However, how PSG-1 exerts its effect on purified lymphocytes is still obscure. Thus, this study aimed to investigate the immunomodulatory activity of PSG-1 on purified T lymphocytes and further elucidate the underlying mechanism based on RNA sequencing (RNA-seq). Our results showed that PSG-1 promoted T lymphocytes proliferation and increased the production of IL-2, IFN-γ, and IL-12. Meanwhile, RNA-seq analysis found 394 differentially expressed genes. KEGG pathway analysis identified 20 significant canonical pathways and seven biological functions. Furthermore, PSG-1 elevated intracellular Ca 2+ concentration and calcineurin (CaN) activity and raised the p-ERK, p-JNK, and p-p38 expression levels. T lymphocytes proliferation and the production of IL-2, IFN-γ, and IL-12 were decreased by the inhibitors of calcium channel and mitogen-activated protein kinases (MAPKs). These results indicated that PSG-1 possesses immunomodulatory activity on purified T lymphocytes, in which Ca 2+ /CaN and MAPK pathways play essential roles.

Computationally efficient characterization of potential energy surfaces based on fingerprint distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schaefer, Bastian; Goedecker, Stefan, E-mail: stefan.goedecker@unibas.ch

2016-07-21

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This methodmore » allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.« less

Computer animation in teaching science: Effectiveness in teaching retrograde motion to 9th graders

NASA Astrophysics Data System (ADS)

Klenk, Kristin Elmstrom

The purpose of this study is to determine whether an instructional approach which includes computer animations is more effective than a traditional textbook-only approach in helping ninth grade students learn an abstract concept, in this case planetary retrograde motion. This investigation uses a quasi-experimental design with convenient sampling. The independent variable is the type of instruction provided to students; traditional text-based instruction (control group) compared to traditional instruction which also includes the viewing of 4 computer animations (treatment). Two conditions of the treatment examine the relative advantage of the order of the presentation of the animations and text-based instruction, as well as the quality of understanding and the retention of the learning over time. The dependent variable is student achievement which is measured using an instrument designed specifically for this study. Comparison of the independent variable to the dependent variable based upon the results from a Repeated Measure Factorial Design in ANOVA indicates that the treatment is an effective instructional technique. The posttest1 mean score of the treatment groups was significantly greater than the posttest1 mean score of the control group. Further posthoc tests indicate that there was no significant difference between the two treatments (1 and 2); read/animation versus animation/read. However, there was a significant difference in the mean score depending on the pathway, students enrolled in the A pathway achieved a significantly higher mean score after the treatment than students in the B pathway. The A pathway (n = 185) represent the larger heterogeneous population of students as compared to the B pathway (n=16) which includes students with lower cognitive abilities and special needs. When all of the students are included in the analysis the results indicate that students do not retain their understanding of the concept. However, when the students in the B pathway are removed from the data set the analysis changes, the posttest1 and posttest2 means are not significantly different. Students in the A pathway did retain their understanding of the concept and were able to demonstrate it on the assessment. A detailed item analysis of the multiple choice question suggest that students in the B pathway were much more likely to guess on the multiple choice questions than students in the A pathway who show no evidence of guessing. The outcome of this study suggests that an instructional approach with includes viewing computer animations is an effective strategy for teaching and learning an abstract concept in a ninth grade Earth Science classroom.

CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis.

PubMed

Vrahatis, Aristidis G; Dimitrakopoulou, Konstantina; Balomenos, Panos; Tsakalidis, Athanasios K; Bezerianos, Anastasios

2016-03-15

In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific 'active parts' of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical 'themes'-in the form of enriched biologically relevant microRNA-mediated subpathways-that determine the functionality of signaling networks across time. To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/ tassos.bezerianos@nus.edu.sg Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Microbial Toluene Removal in Hypoxic Model Constructed Wetlands Occurs Predominantly via the Ring Monooxygenation Pathway.

PubMed

Martínez-Lavanchy, P M; Chen, Z; Lünsmann, V; Marin-Cevada, V; Vilchez-Vargas, R; Pieper, D H; Reiche, N; Kappelmeyer, U; Imparato, V; Junca, H; Nijenhuis, I; Müller, J A; Kuschk, P; Heipieper, H J

2015-09-01

In the present study, microbial toluene degradation in controlled constructed wetland model systems, planted fixed-bed reactors (PFRs), was queried with DNA-based methods in combination with stable isotope fractionation analysis and characterization of toluene-degrading microbial isolates. Two PFR replicates were operated with toluene as the sole external carbon and electron source for 2 years. The bulk redox conditions in these systems were hypoxic to anoxic. The autochthonous bacterial communities, as analyzed by Illumina sequencing of 16S rRNA gene amplicons, were mainly comprised of the families Xanthomonadaceae, Comamonadaceae, and Burkholderiaceae, plus Rhodospirillaceae in one of the PFR replicates. DNA microarray analyses of the catabolic potentials for aromatic compound degradation suggested the presence of the ring monooxygenation pathway in both systems, as well as the anaerobic toluene pathway in the PFR replicate with a high abundance of Rhodospirillaceae. The presence of catabolic genes encoding the ring monooxygenation pathway was verified by quantitative PCR analysis, utilizing the obtained toluene-degrading isolates as references. Stable isotope fractionation analysis showed low-level of carbon fractionation and only minimal hydrogen fractionation in both PFRs, which matches the fractionation signatures of monooxygenation and dioxygenation. In combination with the results of the DNA-based analyses, this suggests that toluene degradation occurs predominantly via ring monooxygenation in the PFRs. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Reconstruction of the Fatty Acid Biosynthetic Pathway of Exiguobacterium antarcticum B7 Based on Genomic and Bibliomic Data

PubMed Central

Kawasaki, Regiane; Carepo, Marta S. P.; Oliveira, Rui; Marques, Rodolfo; Ramos, Rommel T. J.; Schneider, Maria P. C.

2016-01-01

Exiguobacterium antarcticum B7 is extremophile Gram-positive bacteria able to survive in cold environments. A key factor to understanding cold adaptation processes is related to the modification of fatty acids composing the cell membranes of psychrotrophic bacteria. In our study we show the in silico reconstruction of the fatty acid biosynthesis pathway of E. antarcticum B7. To build the stoichiometric model, a semiautomatic procedure was applied, which integrates genome information using KEGG and RAST/SEED. Constraint-based methods, namely, Flux Balance Analysis (FBA) and elementary modes (EM), were applied. FBA was implemented in the sense of hexadecenoic acid production maximization. To evaluate the influence of the gene expression in the fluxome analysis, FBA was also calculated using the log2⁡FC values obtained in the transcriptome analysis at 0°C and 37°C. The fatty acid biosynthesis pathway showed a total of 13 elementary flux modes, four of which showed routes for the production of hexadecenoic acid. The reconstructed pathway demonstrated the capacity of E. antarcticum B7 to de novo produce fatty acid molecules. Under the influence of the transcriptome, the fluxome was altered, promoting the production of short-chain fatty acids. The calculated models contribute to better understanding of the bacterial adaptation at cold environments. PMID:27595107

Understanding the mode-of-action of Cassia auriculata via in silico and in vivo studies towards validating it as a long term therapy for type II diabetes.

PubMed

Mohd Fauzi, Fazlin; John, Cini Mathew; Karunanidhi, Arunkumar; Mussa, Hamse Y; Ramasamy, Rajesh; Adam, Aishah; Bender, Andreas

2017-02-02

Cassia auriculata (CA) is used as an antidiabetic therapy in Ayurvedic and Siddha practice. This study aimed to understand the mode-of-action of CA via combined cheminformatics and in vivo biological analysis. In particular, the effect of 10 polyphenolic constituents of CA in modulating insulin and immunoprotective pathways were studied. In silico target prediction was first employed to predict the probability of the polyphenols interacting with key protein targets related to insulin signalling, based on a model trained on known bioactivity data and chemical similarity considerations. Next, CA was investigated in in vivo studies where induced type 2 diabetic rats were treated with CA for 28 days and the expression levels of genes regulating insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFN-γ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ) were determined by qPCR. In silico analysis shows that several of the top 20 enriched targets predicted for the constituents of CA are involved in insulin signalling pathways e.g. PTPN1, PCK-α, AKT2, PI3K-γ. Some of the predictions were supported by scientific literature such as the prediction of PI3K for epigallocatechin gallate. Based on the in silico and in vivo findings, we hypothesized that CA may enhance glucose uptake and glucose transporter expressions via the IRS signalling pathway. This is based on AKT2 and PI3K-γ being listed in the top 20 enriched targets. In vivo analysis shows significant increase in the expression of IRS, AKT, GLUT2 and GLUT4. CA may also affect the PPAR-γ signalling pathway. This is based on the CA-treated groups showing significant activation of PPAR-γ in the liver compared to control. PPAR-γ was predicted by the in silico target prediction with high normalisation rate although it was not in the top 20 most enriched targets. CA may also be involved in the gluconeogenesis and glycogenolysis in the liver based on the downregulation of G6PC and PCK-1 genes seen in CA-treated groups. In addition, CA-treated groups also showed decreased cholesterol, triglyceride, glucose, CRP and Hb1Ac levels, and increased insulin and C-peptide levels. These findings demonstrate the insulin secretagogue and sensitizer effect of CA. Based on both an in silico and in vivo analysis, we propose here that CA mediates glucose/lipid metabolism via the PI3K signalling pathway, and influence AKT thereby causing insulin secretion and insulin sensitivity in peripheral tissues. CA enhances glucose uptake and expression of glucose transporters in particular via the upregulation of GLUT2 and GLUT4. Thus, based on its ability to modulate immunometabolic pathways, CA appears as an attractive long term therapy for T2DM even at relatively low doses. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Embryotoxic and pharmacologic potency ranking of six azoles in the rat whole embryo culture by morphological and transcriptomic analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimopoulou, Myrto, E-mail: myrto.dimopoulou@wur.nl

Differential gene expression analysis in the rat whole embryo culture (WEC) assay provides mechanistic insight into the embryotoxicity of test compounds. In our study, we hypothesized that comparative analysis of the transcriptomes of rat embryos exposed to six azoles (flusilazole, triadimefon, ketoconazole, miconazole, difenoconazole and prothioconazole) could lead to a better mechanism-based understanding of their embryotoxicity and pharmacological action. For evaluating embryotoxicity, we applied the total morphological scoring system (TMS) in embryos exposed for 48 h. The compounds tested showed embryotoxicity in a dose-response fashion. Functional analysis of differential gene expression after 4 h exposure at the ID{sub 10} (effectivemore » dose for 10% decreased TMS), revealed the sterol biosynthesis pathway and embryonic development genes, dominated by genes in the retinoic acid (RA) pathway, albeit in a differential way. Flusilazole, ketoconazole and triadimefon were the most potent compounds affecting the RA pathway, while in terms of regulation of sterol function, difenoconazole and ketoconazole showed the most pronounced effects. Dose-dependent analysis of the effects of flusilazole revealed that the RA pathway related genes were already differentially expressed at low dose levels while the sterol pathway showed strong regulation at higher embryotoxic doses, suggesting that this pathway is less predictive for the observed embryotoxicity. A similar analysis at the 24-hour time point indicated an additional time-dependent difference in the aforementioned pathways regulated by flusilazole. In summary, the rat WEC assay in combination with transcriptomics could add a mechanistic insight into the embryotoxic potency ranking and pharmacological mode of action of the tested compounds. - Highlights: • Embryonic exposure to azoles revealed concentration-dependent malformations. • Transcriptomics could enhance the mechanistic knowledge of embryotoxicants. • Retinoic acid gene set identifies early embryotoxic responses to azoles. • Toxic versus pharmacologic potency determines functional efficacy.« less

Proteogenomic Characterization of Monocyclic Aromatic Hydrocarbon Degradation Pathways in the Aniline-Degrading Bacterium Burkholderia sp. K24.

PubMed

Lee, Sang-Yeop; Kim, Gun-Hwa; Yun, Sung Ho; Choi, Chi-Won; Yi, Yoon-Sun; Kim, Jonghyun; Chung, Young-Ho; Park, Edmond Changkyun; Kim, Seung Il

2016-01-01

Burkholderia sp. K24, formerly known as Acinetobacter lwoffii K24, is a soil bacterium capable of utilizing aniline as its sole carbon and nitrogen source. Genomic sequence analysis revealed that this bacterium possesses putative gene clusters for biodegradation of various monocyclic aromatic hydrocarbons (MAHs), including benzene, toluene, and xylene (BTX), as well as aniline. We verified the proposed MAH biodegradation pathways by dioxygenase activity assays, RT-PCR, and LC/MS-based quantitative proteomic analyses. This proteogenomic approach revealed four independent degradation pathways, all converging into the citric acid cycle. Aniline and p-hydroxybenzoate degradation pathways converged into the β-ketoadipate pathway. Benzoate and toluene were degraded through the benzoyl-CoA degradation pathway. The xylene isomers, i.e., o-, m-, and p-xylene, were degraded via the extradiol cleavage pathways. Salicylate was degraded through the gentisate degradation pathway. Our results show that Burkholderia sp. K24 possesses versatile biodegradation pathways, which may be employed for efficient bioremediation of aniline and BTX.

Proteogenomic Characterization of Monocyclic Aromatic Hydrocarbon Degradation Pathways in the Aniline-Degrading Bacterium Burkholderia sp. K24

PubMed Central

Yun, Sung Ho; Choi, Chi-Won; Yi, Yoon-Sun; Kim, Jonghyun; Chung, Young-Ho; Park, Edmond Changkyun; Kim, Seung Il

2016-01-01

Burkholderia sp. K24, formerly known as Acinetobacter lwoffii K24, is a soil bacterium capable of utilizing aniline as its sole carbon and nitrogen source. Genomic sequence analysis revealed that this bacterium possesses putative gene clusters for biodegradation of various monocyclic aromatic hydrocarbons (MAHs), including benzene, toluene, and xylene (BTX), as well as aniline. We verified the proposed MAH biodegradation pathways by dioxygenase activity assays, RT-PCR, and LC/MS-based quantitative proteomic analyses. This proteogenomic approach revealed four independent degradation pathways, all converging into the citric acid cycle. Aniline and p-hydroxybenzoate degradation pathways converged into the β-ketoadipate pathway. Benzoate and toluene were degraded through the benzoyl-CoA degradation pathway. The xylene isomers, i.e., o-, m-, and p-xylene, were degraded via the extradiol cleavage pathways. Salicylate was degraded through the gentisate degradation pathway. Our results show that Burkholderia sp. K24 possesses versatile biodegradation pathways, which may be employed for efficient bioremediation of aniline and BTX. PMID:27124467

The Cytotoxicity Mechanism of 6-Shogaol-Treated HeLa Human Cervical Cancer Cells Revealed by Label-Free Shotgun Proteomics and Bioinformatics Analysis.

PubMed

Liu, Qun; Peng, Yong-Bo; Qi, Lian-Wen; Cheng, Xiao-Lan; Xu, Xiao-Jun; Liu, Le-Le; Liu, E-Hu; Li, Ping

2012-01-01

Cervical cancer is one of the most common cancers among women in the world. 6-Shogaol is a natural compound isolated from the rhizome of ginger (Zingiber officinale). In this paper, we demonstrated that 6-shogaol induced apoptosis and G2/M phase arrest in human cervical cancer HeLa cells. Endoplasmic reticulum stress and mitochondrial pathway were involved in 6-shogaol-mediated apoptosis. Proteomic analysis based on label-free strategy by liquid chromatography chip quadrupole time-of-flight mass spectrometry was subsequently proposed to identify, in a non-target-biased manner, the molecular changes in cellular proteins in response to 6-shogaol treatment. A total of 287 proteins were differentially expressed in response to 24 h treatment with 15 μM 6-shogaol in HeLa cells. Significantly changed proteins were subjected to functional pathway analysis by multiple analyzing software. Ingenuity pathway analysis (IPA) suggested that 14-3-3 signaling is a predominant canonical pathway involved in networks which may be significantly associated with the process of apoptosis and G2/M cell cycle arrest induced by 6-shogaol. In conclusion, this work developed an unbiased protein analysis strategy by shotgun proteomics and bioinformatics analysis. Data observed provide a comprehensive analysis of the 6-shogaol-treated HeLa cell proteome and reveal protein alterations that are associated with its anticancer mechanism.

The Cytotoxicity Mechanism of 6-Shogaol-Treated HeLa Human Cervical Cancer Cells Revealed by Label-Free Shotgun Proteomics and Bioinformatics Analysis

PubMed Central

Liu, Qun; Peng, Yong-Bo; Qi, Lian-Wen; Cheng, Xiao-Lan; Xu, Xiao-Jun; Liu, Le-Le; Liu, E-Hu; Li, Ping

2012-01-01

Cervical cancer is one of the most common cancers among women in the world. 6-Shogaol is a natural compound isolated from the rhizome of ginger (Zingiber officinale). In this paper, we demonstrated that 6-shogaol induced apoptosis and G2/M phase arrest in human cervical cancer HeLa cells. Endoplasmic reticulum stress and mitochondrial pathway were involved in 6-shogaol-mediated apoptosis. Proteomic analysis based on label-free strategy by liquid chromatography chip quadrupole time-of-flight mass spectrometry was subsequently proposed to identify, in a non-target-biased manner, the molecular changes in cellular proteins in response to 6-shogaol treatment. A total of 287 proteins were differentially expressed in response to 24 h treatment with 15 μM 6-shogaol in HeLa cells. Significantly changed proteins were subjected to functional pathway analysis by multiple analyzing software. Ingenuity pathway analysis (IPA) suggested that 14-3-3 signaling is a predominant canonical pathway involved in networks which may be significantly associated with the process of apoptosis and G2/M cell cycle arrest induced by 6-shogaol. In conclusion, this work developed an unbiased protein analysis strategy by shotgun proteomics and bioinformatics analysis. Data observed provide a comprehensive analysis of the 6-shogaol-treated HeLa cell proteome and reveal protein alterations that are associated with its anticancer mechanism. PMID:23243437

Genome-wide genetic analyses highlight mitogen-activated protein kinase (MAPK) signaling in the pathogenesis of endometriosis.

PubMed

Uimari, Outi; Rahmioglu, Nilufer; Nyholt, Dale R; Vincent, Katy; Missmer, Stacey A; Becker, Christian; Morris, Andrew P; Montgomery, Grant W; Zondervan, Krina T

2017-04-01

Do genome-wide association study (GWAS) data for endometriosis provide insight into novel biological pathways associated with its pathogenesis? GWAS analysis uncovered multiple pathways that are statistically enriched for genetic association signals, analysis of Stage A disease highlighted a novel variant in MAP3K4, while top pathways significantly associated with all endometriosis and Stage A disease included several mitogen-activated protein kinase (MAPK)-related pathways. Endometriosis is a complex disease with an estimated heritability of 50%. To date, GWAS revealed 10 genomic regions associated with endometriosis, explaining <4% of heritability, while half of the heritability is estimated to be due to common risk variants. Pathway analyses combine the evidence of single variants into gene-based measures, leveraging the aggregate effect of variants in genes and uncovering biological pathways involved in disease pathogenesis. Pathway analysis was conducted utilizing the International Endogene Consortium GWAS data, comprising 3194 surgically confirmed endometriosis cases and 7060 controls of European ancestry with genotype data imputed up to 1000 Genomes Phase three reference panel. GWAS was performed for all endometriosis cases and for Stage A (revised American Fertility Society (rAFS) I/II, n = 1686) and B (rAFS III/IV, n = 1364) cases separately. The identified significant pathways were compared with pathways previously investigated in the literature through candidate association studies. The most comprehensive biological pathway databases, MSigDB (including BioCarta, KEGG, PID, SA, SIG, ST and GO) and PANTHER were utilized to test for enrichment of genetic variants associated with endometriosis. Statistical enrichment analysis was performed using the MAGENTA (Meta-Analysis Gene-set Enrichment of variaNT Associations) software. The first genome-wide association analysis for Stage A endometriosis revealed a novel locus, rs144240142 (P = 6.45 × 10-8, OR = 1.71, 95% CI = 1.23-2.37), an intronic single-nucleotide polymorphism (SNP) within MAP3K4. This SNP was not associated with Stage B disease (P = 0.086). MAP3K4 was also shown to be differentially expressed in eutopic endometrium between Stage A endometriosis cases and controls (P = 3.8 × 10-4), but not with Stage B disease (P = 0.26). A total of 14 pathways enriched with genetic endometriosis associations were identified (false discovery rate (FDR)-P < 0.05). The pathways associated with any endometriosis were Grb2-Sos provides linkage to MAPK signaling for integrins pathway (P = 2.8 × 10-5, FDR-P = 3.0 × 10-3), Wnt signaling (P = 0.026, FDR-P = 0.026) and p130Cas linkage to MAPK signaling for integrins pathway (P = 6.0 × 10-4, FDR-P = 0.029); with Stage A endometriosis: extracellular signal-regulated kinase (ERK)1 ERK2 MAPK (P = 5.0 × 10-4, FDR-P = 5.0 × 10-4) and with Stage B endometriosis: two overlapping pathways that related to extracellular matrix biology-Core matrisome (P = 1.4 × 10-3, FDR-P = 0.013) and ECM glycoproteins (P = 1.8 × 10-3, FDR-P = 7.1 × 10-3). Genes arising from endometriosis candidate gene studies performed to date were enriched for Interleukin signaling pathway (P = 2.3 × 10-12), Apoptosis signaling pathway (P = 9.7 × 10-9) and Gonadotropin releasing hormone receptor pathway (P = 1.2 × 10-6); however, these pathways did not feature in the results based on GWAS data. Not applicable. The analysis is restricted to (i) variants in/near genes that can be assigned to pathways, excluding intergenic variants; (ii) the gene-based pathway definition as registered in the databases; (iii) women of European ancestry. The top ranked pathways associated with overall and Stage A endometriosis in particular involve integrin-mediated MAPK activation and intracellular ERK/MAPK acting downstream in the MAPK cascade, both acting in the control of cell division, gene expression, cell movement and survival. Other top enriched pathways in Stage B disease include ECM glycoprotein pathways important for extracellular structure and biochemical support. The results highlight the need for increased efforts to understand the functional role of these pathways in endometriosis pathogenesis, including the investigation of the biological effects of the genetic variants on downstream molecular processes in tissue relevant to endometriosis. Additionally, our results offer further support for the hypothesis of at least partially distinct causal pathophysiology for minimal/mild (rAFS I/II) vs. moderate/severe (rAFS III/IV) endometriosis. The genome-wide association data and Wellcome Trust Case Control Consortium (WTCCC) were generated through funding from the Wellcome Trust (WT084766/Z/08/Z, 076113 and 085475) and the National Health and Medical Research Council (NHMRC) of Australia (241944, 339462, 389927, 389875, 389891, 389892, 389938, 443036, 442915, 442981, 496610, 496739, 552485 and 552498). N.R. was funded by a grant from the Medical Research Council UK (MR/K011480/1). A.P.M. is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant WT098017). All authors declare there are no conflicts of interest. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology.

Genome-wide genetic analyses highlight mitogen-activated protein kinase (MAPK) signaling in the pathogenesis of endometriosis

PubMed Central

Uimari, Outi; Rahmioglu, Nilufer; Nyholt, Dale R.; Vincent, Katy; Missmer, Stacey A.; Becker, Christian; Morris, Andrew P.; Montgomery, Grant W.

2017-01-01

Abstract STUDY QUESTION Do genome-wide association study (GWAS) data for endometriosis provide insight into novel biological pathways associated with its pathogenesis? SUMMARY ANSWER GWAS analysis uncovered multiple pathways that are statistically enriched for genetic association signals, analysis of Stage A disease highlighted a novel variant in MAP3K4, while top pathways significantly associated with all endometriosis and Stage A disease included several mitogen-activated protein kinase (MAPK)-related pathways. WHAT IS KNOWN ALREADY Endometriosis is a complex disease with an estimated heritability of 50%. To date, GWAS revealed 10 genomic regions associated with endometriosis, explaining <4% of heritability, while half of the heritability is estimated to be due to common risk variants. Pathway analyses combine the evidence of single variants into gene-based measures, leveraging the aggregate effect of variants in genes and uncovering biological pathways involved in disease pathogenesis. STUDY DESIGN, SIZE, DURATION Pathway analysis was conducted utilizing the International Endogene Consortium GWAS data, comprising 3194 surgically confirmed endometriosis cases and 7060 controls of European ancestry with genotype data imputed up to 1000 Genomes Phase three reference panel. GWAS was performed for all endometriosis cases and for Stage A (revised American Fertility Society (rAFS) I/II, n = 1686) and B (rAFS III/IV, n = 1364) cases separately. The identified significant pathways were compared with pathways previously investigated in the literature through candidate association studies. PARTICIPANTS/MATERIALS, SETTING, METHODS The most comprehensive biological pathway databases, MSigDB (including BioCarta, KEGG, PID, SA, SIG, ST and GO) and PANTHER were utilized to test for enrichment of genetic variants associated with endometriosis. Statistical enrichment analysis was performed using the MAGENTA (Meta-Analysis Gene-set Enrichment of variaNT Associations) software. MAIN RESULTS AND THE ROLE OF CHANCE The first genome-wide association analysis for Stage A endometriosis revealed a novel locus, rs144240142 (P = 6.45 × 10−8, OR = 1.71, 95% CI = 1.23–2.37), an intronic single-nucleotide polymorphism (SNP) within MAP3K4. This SNP was not associated with Stage B disease (P = 0.086). MAP3K4 was also shown to be differentially expressed in eutopic endometrium between Stage A endometriosis cases and controls (P = 3.8 × 10−4), but not with Stage B disease (P = 0.26). A total of 14 pathways enriched with genetic endometriosis associations were identified (false discovery rate (FDR)-P < 0.05). The pathways associated with any endometriosis were Grb2-Sos provides linkage to MAPK signaling for integrins pathway (P = 2.8 × 10−5, FDR-P = 3.0 × 10−3), Wnt signaling (P = 0.026, FDR-P = 0.026) and p130Cas linkage to MAPK signaling for integrins pathway (P = 6.0 × 10−4, FDR-P = 0.029); with Stage A endometriosis: extracellular signal-regulated kinase (ERK)1 ERK2 MAPK (P = 5.0 × 10−4, FDR-P = 5.0 × 10−4) and with Stage B endometriosis: two overlapping pathways that related to extracellular matrix biology—Core matrisome (P = 1.4 × 10−3, FDR-P = 0.013) and ECM glycoproteins (P = 1.8 × 10−3, FDR-P = 7.1 × 10−3). Genes arising from endometriosis candidate gene studies performed to date were enriched for Interleukin signaling pathway (P = 2.3 × 10−12), Apoptosis signaling pathway (P = 9.7 × 10−9) and Gonadotropin releasing hormone receptor pathway (P = 1.2 × 10−6); however, these pathways did not feature in the results based on GWAS data. LARGE SCALE DATA Not applicable. LIMITATIONS, REASONS FOR CAUTION The analysis is restricted to (i) variants in/near genes that can be assigned to pathways, excluding intergenic variants; (ii) the gene-based pathway definition as registered in the databases; (iii) women of European ancestry. WIDER IMPLICATIONS OF THE FINDINGS The top ranked pathways associated with overall and Stage A endometriosis in particular involve integrin-mediated MAPK activation and intracellular ERK/MAPK acting downstream in the MAPK cascade, both acting in the control of cell division, gene expression, cell movement and survival. Other top enriched pathways in Stage B disease include ECM glycoprotein pathways important for extracellular structure and biochemical support. The results highlight the need for increased efforts to understand the functional role of these pathways in endometriosis pathogenesis, including the investigation of the biological effects of the genetic variants on downstream molecular processes in tissue relevant to endometriosis. Additionally, our results offer further support for the hypothesis of at least partially distinct causal pathophysiology for minimal/mild (rAFS I/II) vs. moderate/severe (rAFS III/IV) endometriosis. STUDY FUNDING/COMPETING INTEREST(S) The genome-wide association data and Wellcome Trust Case Control Consortium (WTCCC) were generated through funding from the Wellcome Trust (WT084766/Z/08/Z, 076113 and 085475) and the National Health and Medical Research Council (NHMRC) of Australia (241944, 339462, 389927, 389875, 389891, 389892, 389938, 443036, 442915, 442981, 496610, 496739, 552485 and 552498). N.R. was funded by a grant from the Medical Research Council UK (MR/K011480/1). A.P.M. is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant WT098017). All authors declare there are no conflicts of interest. PMID:28333195

Molecular Signatures Discriminating the Male and the Female Sexual Pathways in the Pearl Oyster Pinctada margaritifera

PubMed Central

Teaniniuraitemoana, Vaihiti; Huvet, Arnaud; Levy, Peva; Gaertner-Mazouni, Nabila; Gueguen, Yannick; Le Moullac, Gilles

2015-01-01

The genomics of economically important marine bivalves is studied to provide better understanding of the molecular mechanisms underlying their different reproductive strategies. The recently available gonad transcriptome of the black-lip pearl oyster Pinctada margaritifera is a novel and powerful resource to study these mechanisms in marine mollusks displaying hermaphroditic features. In this study, RNAseq quantification data of the P. margaritifera gonad transcriptome were analyzed to identify candidate genes in histologically-characterized gonad samples to provide molecular signatures of the female and male sexual pathway in this pearl oyster. Based on the RNAseq data set, stringent expression analysis identified 1,937 contigs that were differentially expressed between the gonad histological categories. From the hierarchical clustering analysis, a new reproduction model is proposed, based on a dual histo-molecular analytical approach. Nine candidate genes were identified as markers of the sexual pathway: 7 for the female pathway and 2 for the male one. Their mRNA levels were assayed by real-time PCR on a new set of gonadic samples. A clustering method revealed four principal expression patterns based on the relative gene expression ratio. A multivariate regression tree realized on these new samples and validated on the previously analyzed RNAseq samples showed that the sexual pathway of P. margaritifera can be predicted by a 3-gene-pair expression ratio model of 4 different genes: pmarg-43476, pmarg-foxl2, pmarg-54338 and pmarg-fem1-like. This 3-gene-pair expression ratio model strongly suggests only the implication of pmarg-foxl2 and pmarg-fem1-like in the sex inversion of P. margaritifera. This work provides the first histo-molecular model of P. margaritifera reproduction and a gene expression signature of its sexual pathway discriminating the male and female pathways. These represent useful tools for understanding and studying sex inversion, sex differentiation and sex determinism in this species and other related species for aquaculture purposes such as genetic selection programs. PMID:25815473

Data driven linear algebraic methods for analysis of molecular pathways: application to disease progression in shock/trauma.

PubMed

McGuire, Mary F; Sriram Iyengar, M; Mercer, David W

2012-04-01

Although trauma is the leading cause of death for those below 45years of age, there is a dearth of information about the temporal behavior of the underlying biological mechanisms in those who survive the initial trauma only to later suffer from syndromes such as multiple organ failure. Levels of serum cytokines potentially affect the clinical outcomes of trauma; understanding how cytokine levels modulate intra-cellular signaling pathways can yield insights into molecular mechanisms of disease progression and help to identify targeted therapies. However, developing such analyses is challenging since it necessitates the integration and interpretation of large amounts of heterogeneous, quantitative and qualitative data. Here we present the Pathway Semantics Algorithm (PSA), an algebraic process of node and edge analyses of evoked biological pathways over time for in silico discovery of biomedical hypotheses, using data from a prospective controlled clinical study of the role of cytokines in multiple organ failure (MOF) at a major US trauma center. A matrix algebra approach was used in both the PSA node and PSA edge analyses with different matrix configurations and computations based on the biomedical questions to be examined. In the edge analysis, a percentage measure of crosstalk called XTALK was also developed to assess cross-pathway interference. In the node/molecular analysis of the first 24h from trauma, PSA uncovered seven molecules evoked computationally that differentiated outcomes of MOF or non-MOF (NMOF), of which three molecules had not been previously associated with any shock/trauma syndrome. In the edge/molecular interaction analysis, PSA examined four categories of functional molecular interaction relationships--activation, expression, inhibition, and transcription--and found that the interaction patterns and crosstalk changed over time and outcome. The PSA edge analysis suggests that a diagnosis, prognosis or therapy based on molecular interaction mechanisms may be most effective within a certain time period and for a specific functional relationship. Copyright © 2011 Elsevier Inc. All rights reserved.

Data driven linear algebraic methods for analysis of molecular pathways: application to disease progression in shock/trauma

PubMed Central

McGuire, Mary F.; Iyengar, M. Sriram; Mercer, David W.

2012-01-01

Motivation Although trauma is the leading cause of death for those below 45 years of age, there is a dearth of information about the temporal behavior of the underlying biological mechanisms in those who survive the initial trauma only to later suffer from syndromes such as multiple organ failure. Levels of serum cytokines potentially affect the clinical outcomes of trauma; understanding how cytokine levels modulate intra-cellular signaling pathways can yield insights into molecular mechanisms of disease progression and help to identify targeted therapies. However, developing such analyses is challenging since it necessitates the integration and interpretation of large amounts of heterogeneous, quantitative and qualitative data. Here we present the Pathway Semantics Algorithm (PSA), an algebraic process of node and edge analyses of evoked biological pathways over time for in silico discovery of biomedical hypotheses, using data from a prospective controlled clinical study of the role of cytokines in multiple organ failure (MOF) at a major US trauma center. A matrix algebra approach was used in both the PSA node and PSA edge analyses with different matrix configurations and computations based on the biomedical questions to be examined. In the edge analysis, a percentage measure of crosstalk called XTALK was also developed to assess cross-pathway interference. Results In the node/molecular analysis of the first 24 hours from trauma, PSA uncovered 7 molecules evoked computationally that differentiated outcomes of MOF or non-MOF (NMOF), of which 3 molecules had not been previously associated with any shock / trauma syndrome. In the edge/molecular interaction analysis, PSA examined four categories of functional molecular interaction relationships – activation, expression, inhibition, and transcription – and found that the interaction patterns and crosstalk changed over time and outcome. The PSA edge analysis suggests that a diagnosis, prognosis or therapy based on molecular interaction mechanisms may be most effective within a certain time period and for a specific functional relationship. PMID:22200681

Comparative analysis of taxonomic, functional, and metabolic patterns of microbiomes from 14 full-scale biogas reactors by metagenomic sequencing and radioisotopic analysis.

PubMed

Luo, Gang; Fotidis, Ioannis A; Angelidaki, Irini

2016-01-01

Biogas production is a very complex process due to the high complexity in diversity and interactions of the microorganisms mediating it, and only limited and diffuse knowledge exists about the variation of taxonomic and functional patterns of microbiomes across different biogas reactors, and their relationships with the metabolic patterns. The present study used metagenomic sequencing and radioisotopic analysis to assess the taxonomic, functional, and metabolic patterns of microbiomes from 14 full-scale biogas reactors operated under various conditions treating either sludge or manure. The results from metagenomic analysis showed that the dominant methanogenic pathway revealed by radioisotopic analysis was not always correlated with the taxonomic and functional compositions. It was found by radioisotopic experiments that the aceticlastic methanogenic pathway was dominant, while metagenomics analysis showed higher relative abundance of hydrogenotrophic methanogens. Principal coordinates analysis showed the sludge-based samples were clearly distinct from the manure-based samples for both taxonomic and functional patterns, and canonical correspondence analysis showed that the both temperature and free ammonia were crucial environmental variables shaping the taxonomic and functional patterns. The study further the overall patterns of functional genes were strongly correlated with overall patterns of taxonomic composition across different biogas reactors. The discrepancy between the metabolic patterns determined by metagenomic analysis and metabolic pathways determined by radioisotopic analysis was found. Besides, a clear correlation between taxonomic and functional patterns was demonstrated for biogas reactors, and also the environmental factors that shaping both taxonomic and functional genes patterns were identified.

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

PubMed Central

Lex, Alexander; Partl, Christian; Kalkofen, Denis; Streit, Marc; Gratzl, Samuel; Wassermann, Anne Mai; Schmalstieg, Dieter; Pfister, Hanspeter

2014-01-01

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types. Fig. 1Entourage showing the Glioma pathway in detail and contextual information of multiple related pathways. PMID:24051820

Metabolic pathway analysis and kinetic studies for production of nattokinase in Bacillus subtilis.

PubMed

Unrean, Pornkamol; Nguyen, Nhung H A

2013-01-01

We have constructed a reaction network model of Bacillus subtilis. The model was analyzed using a pathway analysis tool called elementary mode analysis (EMA). The analysis tool was used to study the network capabilities and the possible effects of altered culturing conditions on the production of a fibrinolytic enzyme, nattokinase (NK) by B. subtilis. Based on all existing metabolic pathways, the maximum theoretical yield for NK synthesis in B. subtilis under different substrates and oxygen availability was predicted and the optimal culturing condition for NK production was identified. To confirm model predictions, experiments were conducted by testing these culture conditions for their influence on NK activity. The optimal culturing conditions were then applied to batch fermentation, resulting in high NK activity. The EMA approach was also applied for engineering B. subtilis metabolism towards the most efficient pathway for NK synthesis by identifying target genes for deletion and overexpression that enable the cell to produce NK at the maximum theoretical yield. The consistency between experiments and model predictions proves the feasibility of EMA being used to rationally design culture conditions and genetic manipulations for the efficient production of desired products.

Genetic association of impulsivity in young adults: a multivariate study

PubMed Central

Khadka, S; Narayanan, B; Meda, S A; Gelernter, J; Han, S; Sawyer, B; Aslanzadeh, F; Stevens, M C; Hawkins, K A; Anticevic, A; Potenza, M N; Pearlson, G D

2014-01-01

Impulsivity is a heritable, multifaceted construct with clinically relevant links to multiple psychopathologies. We assessed impulsivity in young adult (N~2100) participants in a longitudinal study, using self-report questionnaires and computer-based behavioral tasks. Analysis was restricted to the subset (N=426) who underwent genotyping. Multivariate association between impulsivity measures and single-nucleotide polymorphism data was implemented using parallel independent component analysis (Para-ICA). Pathways associated with multiple genes in components that correlated significantly with impulsivity phenotypes were then identified using a pathway enrichment analysis. Para-ICA revealed two significantly correlated genotype–phenotype component pairs. One impulsivity component included the reward responsiveness subscale and behavioral inhibition scale of the Behavioral-Inhibition System/Behavioral-Activation System scale, and the second impulsivity component included the non-planning subscale of the Barratt Impulsiveness Scale and the Experiential Discounting Task. Pathway analysis identified processes related to neurogenesis, nervous system signal generation/amplification, neurotransmission and immune response. We identified various genes and gene regulatory pathways associated with empirically derived impulsivity components. Our study suggests that gene networks implicated previously in brain development, neurotransmission and immune response are related to impulsive tendencies and behaviors. PMID:25268255

Gene expression profiles in whole blood and associations with metabolic dysregulation in obesity.

PubMed

Cox, Amanda J; Zhang, Ping; Evans, Tiffany J; Scott, Rodney J; Cripps, Allan W; West, Nicholas P

Gene expression data provides one tool to gain further insight into the complex biological interactions linking obesity and metabolic disease. This study examined associations between blood gene expression profiles and metabolic disease in obesity. Whole blood gene expression profiles, performed using the Illumina HT-12v4 Human Expression Beadchip, were compared between (i) individuals with obesity (O) or lean (L) individuals (n=21 each), (ii) individuals with (M) or without (H) Metabolic Syndrome (n=11 each) matched on age and gender. Enrichment of differentially expressed genes (DEG) into biological pathways was assessed using Ingenuity Pathway Analysis. Association between sets of genes from biological pathways considered functionally relevant and Metabolic Syndrome were further assessed using an area under the curve (AUC) and cross-validated classification rate (CR). For OvL, only 50 genes were significantly differentially expressed based on the selected differential expression threshold (1.2-fold, p<0.05). For MvH, 582 genes were significantly differentially expressed (1.2-fold, p<0.05) and pathway analysis revealed enrichment of DEG into a diverse set of pathways including immune/inflammatory control, insulin signalling and mitochondrial function pathways. Gene sets from the mTOR signalling pathways demonstrated the strongest association with Metabolic Syndrome (p=8.1×10 -8 ; AUC: 0.909, CR: 72.7%). These results support the use of expression profiling in whole blood in the absence of more specific tissue types for investigations of metabolic disease. Using a pathway analysis approach it was possible to identify an enrichment of DEG into biological pathways that could be targeted for in vitro follow-up. Copyright © 2017 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Academic Provenance: Mapping Geoscience Students' Academic Pathways to their Career Trajectories

NASA Astrophysics Data System (ADS)

Houlton, H. R.; Gonzales, L. M.; Keane, C. M.

2011-12-01

Targeted recruitment and retention efforts for the geosciences have become increasingly important with the growing concerns about program visibility on campuses, and given that geoscience degree production remains low relative to the demand for new geoscience graduates. Furthermore, understanding the career trajectories of geoscience degree recipients is essential for proper occupational placement. A theoretical framework was developed by Houlton (2010) to focus recruitment and retention efforts. This "pathway model" explicitly maps undergraduate students' geoscience career trajectories, which can be used to refine existing methods for recruiting students into particular occupations. Houlton's (2010) framework identified three main student population groups: Natives, Immigrants or Refugees. Each student followed a unique pathway, which consisted of six pathway steps. Each pathway step was comprised of critical incidents that influenced students' overall career trajectories. An aggregate analysis of students' pathways (Academic Provenance Analysis) showed that different populations' pathways exhibited a deviation in career direction: Natives indicated intentions to pursue industry or government sectors, while Immigrants intended to pursue academic or research-based careers. We expanded on Houlton's (2010) research by conducting a follow-up study to determine if the original participants followed the career trajectories they initially indicated in the 2010 study. A voluntary, 5-question, short-answer survey was administered via email. We investigated students' current pathway steps, pathway deviations, students' goals for the near future and their ultimate career ambitions. This information may help refine Houlton's (2010) "pathway model" and may aid geoscience employers in recruiting the new generation of professionals for their respective sectors.

Pathway-based personalized analysis of cancer

PubMed Central

Drier, Yotam; Sheffer, Michal; Domany, Eytan

2013-01-01

We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm’s performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers. PMID:23547110

Comparison of different two-pathway models for describing the combined effect of DO and nitrite on the nitrous oxide production by ammonia-oxidizing bacteria.

PubMed

Lang, Longqi; Pocquet, Mathieu; Ni, Bing-Jie; Yuan, Zhiguo; Spérandio, Mathieu

2017-02-01

The aim of this work is to compare the capability of two recently proposed two-pathway models for predicting nitrous oxide (N 2 O) production by ammonia-oxidizing bacteria (AOB) for varying ranges of dissolved oxygen (DO) and nitrite. The first model includes the electron carriers whereas the second model is based on direct coupling of electron donors and acceptors. Simulations are confronted to extensive sets of experiments (43 batches) from different studies with three different microbial systems. Despite their different mathematical structures, both models could well and similarly describe the combined effect of DO and nitrite on N 2 O production rate and emission factor. The model-predicted contributions for nitrifier denitrification pathway and hydroxylamine pathway also matched well with the available isotopic measurements. Based on sensitivity analysis, calibration procedures are described and discussed for facilitating the future use of those models.

Enhanced Recovery Pathway in Microvascular Autologous Tissue-Based Breast Reconstruction: Should It Become the Standard of Care?

PubMed

Kaoutzanis, Christodoulos; Ganesh Kumar, Nishant; O'Neill, Dillon; Wormer, Blair; Winocour, Julian; Layliev, John; McEvoy, Matthew; King, Adam; Braun, Stephane A; Higdon, K Kye

2018-04-01

Enhanced recovery pathway programs have demonstrated improved perioperative care and shorter length of hospital stay in several surgical disciplines. The purpose of this study was to compare outcomes of patients undergoing autologous tissue-based breast reconstruction before and after the implementation of an enhanced recovery pathway program. The authors retrospectively reviewed consecutive patients who underwent autologous tissue-based breast reconstruction performed by two surgeons before and after the implementation of the enhanced recovery pathway at a university center over a 3-year period. Patient demographics, perioperative data, and 45-day postoperative outcomes were compared between the traditional standard of care (pre-enhanced recovery pathway) and enhanced recovery pathway patients. Multivariate logistic regression was performed to identify risk factors for length of hospital stay. Cost analysis was performed. Between April of 2014 and January of 2017, 100 consecutive women were identified, with 50 women in each group. Both groups had similar demographics, comorbidities, and reconstruction types. Postoperatively, the enhanced recovery pathway cohort used significantly less opiate and more acetaminophen compared with the traditional standard of care cohort. Median length of stay was shorter in the enhanced recovery pathway cohort, which resulted in an extrapolated $279,258 savings from freeing up inpatient beds and increase in overall contribution margins of $189,342. Participation in an enhanced recovery pathway program and lower total morphine-equivalent use were independent predictors for decreased length of hospital stay. Overall 45-day major complication rates, partial flap loss rates, emergency room visits, hospital readmissions, and unplanned reoperations were similar between the two groups. Enhanced recovery pathway program implementation should be considered as the standard approach for perioperative care in autologous tissue-based breast reconstruction because it does not affect morbidity and is associated with accelerated recovery with reduced postoperative opiate use and decreased length of hospital stay, leading to downstream health care cost savings. Therapeutic, III.

Proteomic Analysis Reveals the Contribution of TGFβ/Smad4 Signaling Pathway to Cell Differentiation During Planarian Tail Regeneration.

PubMed

Chen, Xiaoguang; Xu, Cunshuan

2017-06-01

After planarian tail is cut off, posterior end of the remaining fragment will regenerate a new tail within about 1 week. However, many details of this process remain unclear up to date. For this reason, we performed the dynamic proteomic analysis of the regenerating tail fragments at 6, 12, 24, 72, 120, and 168 h post-amputation (hpa). Using two-dimensional electrophoresis (2-DE) in combination with MALDI-TOF-TOF/MS analysis, a total of 1088 peptides were identified as significantly changed between tail-cutting groups and 0-h group, 482 of which have identifiable protein names. Of these 482 proteins, there were 111 originating from the Turbellaria. Protein functional categorization showed that these 111 proteins are mainly related to differentiation and development, transcription and translation, cell signal transduction, and cell proliferation. The screening of key protein considered the transcription factor Smad4 as important protein for planarian tail regeneration. Cell signaling pathway analysis, combined with proteomic profiling of regenerating tail fragment, showed that TGFβ/Smad4 pathway was activated during planarian tail regeneration. Based on a comprehensive analysis of 2-DE MALDI-TOF-TOF/MS and bioinformatics analyses, it could be concluded that TGFβ/Smad4 pathway perhaps plays an important role in tail regeneration via promoting cell differentiation.

A Deeply Branching Thermophilic Bacterium with an Ancient Acetyl-CoA Pathway Dominates a Subsurface Ecosystem

PubMed Central

Takami, Hideto; Noguchi, Hideki; Takaki, Yoshihiro; Uchiyama, Ikuo; Toyoda, Atsushi; Nishi, Shinro; Chee, Gab-Joo; Arai, Wataru; Nunoura, Takuro; Itoh, Takehiko; Hattori, Masahira; Takai, Ken

2012-01-01

A nearly complete genome sequence of Candidatus ‘Acetothermum autotrophicum’, a presently uncultivated bacterium in candidate division OP1, was revealed by metagenomic analysis of a subsurface thermophilic microbial mat community. Phylogenetic analysis based on the concatenated sequences of proteins common among 367 prokaryotes suggests that Ca. ‘A. autotrophicum’ is one of the earliest diverging bacterial lineages. It possesses a folate-dependent Wood-Ljungdahl (acetyl-CoA) pathway of CO2 fixation, is predicted to have an acetogenic lifestyle, and possesses the newly discovered archaeal-autotrophic type of bifunctional fructose 1,6-bisphosphate aldolase/phosphatase. A phylogenetic analysis of the core gene cluster of the acethyl-CoA pathway, shared by acetogens, methanogens, some sulfur- and iron-reducers and dechlorinators, supports the hypothesis that the core gene cluster of Ca. ‘A. autotrophicum’ is a particularly ancient bacterial pathway. The habitat, physiology and phylogenetic position of Ca. ‘A. autotrophicum’ support the view that the first bacterial and archaeal lineages were H2-dependent acetogens and methanogenes living in hydrothermal environments. PMID:22303444

System-based strategies for p53 recovery.

PubMed

Azam, Muhammad Rizwan; Fazal, Sahar; Ullah, Mukhtar; Bhatti, Aamer I

2018-06-01

The authors have proposed a systems theory-based novel drug design approach for the p53 pathway. The pathway is taken as a dynamic system represented by ordinary differential equations-based mathematical model. Using control engineering practices, the system analysis and subsequent controller design is performed for the re-activation of wild-type p53. p53 revival is discussed for both modes of operation, i.e. the sustained and oscillatory. To define the problem in control system paradigm, modification in the existing mathematical model is performed to incorporate the effect of Nutlin. Attractor point analysis is carried out to select the suitable domain of attraction. A two-loop negative feedback control strategy is devised to drag the system trajectories to the attractor point and to regulate cellular concentration of Nutlin, respectively. An integrated framework is constituted to incorporate the pharmacokinetic effects of Nutlin in the cancerous cells. Bifurcation analysis is also performed on the p53 model to see the conditions for p53 oscillation.

Motile hepatocellular carcinoma cells preferentially secret sugar metabolism regulatory proteins via exosomes.

PubMed

Zhang, Jing; Lu, Shaohua; Zhou, Ye; Meng, Kun; Chen, Zhipeng; Cui, Yizhi; Shi, Yunfeng; Wang, Tong; He, Qing-Yu

2017-07-01

Exosomes are deliverers of critically functional proteins, capable of transforming target cells in numerous cancers, including hepatocellular carcinoma (HCC). We hypothesize that the motility of HCC cells can be featured by comparative proteome of exosomes. Hence, we performed the super-SILAC-based MS analysis on the exosomes secreted by three human HCC cell lines, including the non-motile Hep3B cell, and the motile 97H and LM3 cells. More than 1400 exosomal proteins were confidently quantified in each MS analysis with highly biological reproducibility. We justified that 469 and 443 exosomal proteins represented differentially expressed proteins (DEPs) in the 97H/Hep3B and LM3/Hep3B comparisons, respectively. These DEPs focused on sugar metabolism-centric canonical pathways per ingenuity pathway analysis, which was consistent with the gene ontology analysis on biological process enrichment. These pathways included glycolysis I, gluconeogenesis I and pentose phosphate pathways; and the DEPs enriched in these pathways could form a tightly connected network. By analyzing the relative abundance of proteins and translating mRNAs, we found significantly positive correlation between exosomes and cells. The involved exosomal proteins were again focusing on sugar metabolism. In conclusion, motile HCC cells tend to preferentially export more sugar metabolism-associated proteins via exosomes that differentiate them from non-motile HCC cells. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evolution of amino acid metabolism inferred through cladistic analysis.

PubMed

Cunchillos, Chomin; Lecointre, Guillaume

2003-11-28

Because free amino acids were most probably available in primitive abiotic environments, their metabolism is likely to have provided some of the very first metabolic pathways of life. What were the first enzymatic reactions to emerge? A cladistic analysis of metabolic pathways of the 16 aliphatic amino acids and 2 portions of the Krebs cycle was performed using four criteria of homology. The analysis is not based on sequence comparisons but, rather, on coding similarities in enzyme properties. The properties used are shared specific enzymatic activity, shared enzymatic function without substrate specificity, shared coenzymes, and shared functional family. The tree shows that the earliest pathways to emerge are not portions of the Krebs cycle but metabolisms of aspartate, asparagine, glutamate, and glutamine. The views of Horowitz (Horowitz, N. H. (1945) Proc. Natl. Acad. Sci. U. S. A. 31, 153-157) and Cordón (Cordón, F. (1990) Tratado Evolucionista de Biologia, Aguilar, Madrid, Spain), according to which the upstream reactions in the catabolic pathways and the downstream reactions in the anabolic pathways are the earliest in evolution, are globally corroborated; however, with some exceptions. These are due to later opportunistic connections of pathways (actually already suggested by these authors). Earliest enzymatic functions are mostly catabolic; they were deaminations, transaminations, and decarboxylations. From the consensus tree we extracted four time spans for amino acid metabolism development. For some amino acids catabolism and biosynthesis occurred at the same time (Asp, Glu, Lys, Leu, Ala, Val, Ile, Pro, Arg). For others ultimate reactions that use amino acids as a substrate or as a product are distinct in time, with catabolism preceding anabolism for Asn, Gln, and Cys and anabolism preceding catabolism for Ser, Met, and Thr. Cladistic analysis of the structure of biochemical pathways makes hypotheses in biochemical evolution explicit and parsimonious.

Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data.

PubMed

Tang, Hongwei; Wei, Peng; Duell, Eric J; Risch, Harvey A; Olson, Sara H; Bueno-de-Mesquita, H Bas; Gallinger, Steven; Holly, Elizabeth A; Petersen, Gloria; Bracci, Paige M; McWilliams, Robert R; Jenab, Mazda; Riboli, Elio; Tjønneland, Anne; Boutron-Ruault, Marie Christine; Kaaks, Rudolph; Trichopoulos, Dimitrios; Panico, Salvatore; Sund, Malin; Peeters, Petra H M; Khaw, Kay-Tee; Amos, Christopher I; Li, Donghui

2014-05-01

Cigarette smoking is the best established modifiable risk factor for pancreatic cancer. Genetic factors that underlie smoking-related pancreatic cancer have previously not been examined at the genome-wide level. Taking advantage of the existing Genome-wide association study (GWAS) genotype and risk factor data from the Pancreatic Cancer Case Control Consortium, we conducted a discovery study in 2028 cases and 2109 controls to examine gene-smoking interactions at pathway/gene/single nucleotide polymorphism (SNP) level. Using the likelihood ratio test nested in logistic regression models and ingenuity pathway analysis (IPA), we examined 172 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, 3 manually curated gene sets, 3 nicotine dependency gene ontology pathways, 17 912 genes and 468 114 SNPs. None of the individual pathway/gene/SNP showed significant interaction with smoking after adjusting for multiple comparisons. Six KEGG pathways showed nominal interactions (P < 0.05) with smoking, and the top two are the pancreatic secretion and salivary secretion pathways (major contributing genes: RAB8A, PLCB and CTRB1). Nine genes, i.e. ZBED2, EXO1, PSG2, SLC36A1, CLSTN1, MTHFSD, FAT2, IL10RB and ATXN2 had P interaction < 0.0005. Five intergenic region SNPs and two SNPs of the EVC and KCNIP4 genes had P interaction < 0.00003. In IPA analysis of genes with nominal interactions with smoking, axonal guidance signaling $$\\left(P=2.12\\times 1{0}^{-7}\\right)$$ and α-adrenergic signaling $$\\left(P=2.52\\times 1{0}^{-5}\\right)$$ genes were significantly overrepresented canonical pathways. Genes contributing to the axon guidance signaling pathway included the SLIT/ROBO signaling genes that were frequently altered in pancreatic cancer. These observations need to be confirmed in additional data set. Once confirmed, it will open a new avenue to unveiling the etiology of smoking-associated pancreatic cancer.

Investigation of Ruthenium Dissolution in Advanced Membrane Electrode Assemblies for Direct Methanol Based Fuel Cells Stacks

NASA Technical Reports Server (NTRS)

Valdez, T. I.; Firdosy, S.; Koel, B. E.; Narayanan, S. R.

2005-01-01

This viewgraph presentation gives a detailed review of the Direct Methanol Based Fuel Cell (DMFC) stack and investigates the Ruthenium that was found at the exit of the stack. The topics include: 1) Motivation; 2) Pathways for Cell Degradation; 3) Cell Duration Testing; 4) Duration Testing, MEA Analysis; and 5) Stack Degradation Analysis.

Automated Selection of Regions of Interest for Intensity-based FRET Analysis of Transferrin Endocytic Trafficking in Normal vs. Cancer Cells

PubMed Central

Talati, Ronak; Vanderpoel, Andrew; Eladdadi, Amina; Anderson, Kate; Abe, Ken; Barroso, Margarida

2013-01-01

The overexpression of certain membrane-bound receptors is a hallmark of cancer progression and it has been suggested to affect the organization, activation, recycling and down-regulation of receptor-ligand complexes in human cancer cells. Thus, comparing receptor trafficking pathways in normal vs. cancer cells requires the ability to image cells expressing dramatically different receptor expression levels. Here, we have presented a significant technical advance to the analysis and processing of images collected using intensity based Förster resonance energy transfer (FRET) confocal microscopy. An automated Image J macro was developed to select region of interests (ROI) based on intensity and statistical-based thresholds within cellular images with reduced FRET signal. Furthermore, SSMD (strictly standardized mean differences), a statistical signal-to-noise ratio (SNR) evaluation parameter, was used to validate the quality of FRET analysis, in particular of ROI database selection. The Image J ROI selection macro together with SSMD as an evaluation parameter of SNR levels, were used to investigate the endocytic recycling of Tfn-TFR complexes at nanometer range resolution in human normal vs. breast cancer cells expressing significantly different levels of endogenous TFR. Here, the FRET-based assay demonstrates that Tfn-TFR complexes in normal epithelial vs. breast cancer cells show a significantly different E% behavior during their endocytic recycling pathway. Since E% is a relative measure of distance, we propose that these changes in E% levels represent conformational changes in Tfn-TFR complexes during endocytic pathway. Thus, our results indicate that Tfn-TFR complexes undergo different conformational changes in normal vs. cancer cells, indicating that the organization of Tfn-TFR complexes at the nanometer range is significantly altered during the endocytic recycling pathway in cancer cells. In summary, improvements in the automated selection of FRET ROI datasets allowed us to detect significant changes in E% with potential biological significance in human normal vs. cancer cells. PMID:23994873

Pathway-related modules involved in the application of sevoflurane or propofol in off-pump coronary artery bypass graft surgery.

PubMed

Bu, Xiangmei; Wang, Bo; Wang, Yaoqi; Wang, Zhigang; Gong, Chunzhi; Qi, Feng; Zhang, Caixia

2017-07-01

Off-pump coronary artery bypass graft (CABG) surgery has recently emerged as a means to avoid the sequelae of extracorporeal circulation, including the whole-body inflammatory response, coagulation disorders and multiple organ dysfunction. At present, gas anesthesia, sevoflurane and intravenous anesthesia and propofol have been widely used during the CABG. To further understand the underlying mechanisms of these anesthetics on the gene level, the present study conducted pathway-related module analysis based on a co-expression network. This was performed in order to identify significant pathways in coronary artery disease patients who had undergone off-pump CABG surgery before and after applying sevoflurane or propofol. A total of 269 and 129 differentially expressed genes were obtained in the sevoflurane and propofol groups, respectively. In total, eight and seven pathways (P<0.05) in the sevoflurane and propofol groups were separately obtained via Kyoto Encyclopedia of Genes and Genome pathway analysis. Finally, eight and seven pathway-related modules in the sevoflurane and propofol groups were obtained, respectively. Furthermore, the mean degree of complement and coagulation cascades pathway-related module in both of the groups was the highest. It was predicted that during the CABG, the anesthetics might activate the complement and coagulation systems in order to possess some cardioprotective properties.

In Silico Analysis of Putrefaction Pathways in Bacteria and Its Implication in Colorectal Cancer

PubMed Central

Kaur, Harrisham; Das, Chandrani; Mande, Sharmila S.

2017-01-01

Fermentation of undigested proteins in human gastrointestinal tract (gut) by the resident microbiota, a process called bacterial putrefaction, can sometimes disrupt the gut homeostasis. In this process, essential amino acids (e.g., histidine, tryptophan, etc.) that are required by the host may be utilized by the gut microbes. In addition, some of the products of putrefaction, like ammonia, putrescine, cresol, indole, phenol, etc., have been implicated in the disease pathogenesis of colorectal cancer (CRC). We have investigated bacterial putrefaction pathways that are known to be associated with such metabolites. Results of the comprehensive in silico analysis of the selected putrefaction pathways across bacterial genomes revealed presence of these pathways in limited bacterial groups. Majority of these bacteria are commonly found in human gut. These include Bacillus, Clostridium, Enterobacter, Escherichia, Fusobacterium, Salmonella, etc. Interestingly, while pathogens utilize almost all the analyzed pathways, commensals prefer putrescine and H2S production pathways for metabolizing the undigested proteins. Further, comparison of the putrefaction pathways in the gut microbiomes of healthy, carcinoma and adenoma datasets indicate higher abundances of putrefying bacteria in the carcinoma stage of CRC. The insights obtained from the present study indicate utilization of possible microbiome-based therapies to minimize the adverse effects of gut microbiome in enteric diseases. PMID:29163445

Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological Viewpoints

PubMed

Mohammadi Majd, Tahereh; Kalantari, Shiva; Raeisi Shahraki, Hadi; Nafar, Mohsen; Almasi, Afshin; Samavat, Shiva; Parvin, Mahmoud; Hashemian, Amirhossein

2018-03-10

IgA nephropathy (IgAN) is the most common primary glomerulonephritis diagnosed based on renal biopsy. Mesangial IgA deposits along with the proliferation of mesangial cells are the histologic hallmark of IgAN. Non-invasive diagnostic tools may help to prompt diagnosis and therapy. The discovery of potential and reliable urinary biomarkers for diagnosis of IgAN depends on applying robust and suitable models. Applying two multivariate modeling methods on a urine proteomic dataset obtained from IgAN patients, and comparison of the results of these methods were the purpose of this study. Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net regression methods. A panel of selected biomarkers with the best coefficients were proposed and further analyzed for biological relevance using functional annotation and pathway analysis. Transferrin, α1-antitrypsin, and albumin fragments were the most important up-regulated biomarkers, while fibulin-5, YIP1 family member 3, prasoposin, and osteopontin were the most important down-regulated biomarkers. Pathway analysis revealed that complement and coagulation cascades and extracellular matrix-receptor interaction pathways impaired in the pathogenesis of IgAN. SLDA and elastic net had an equal importance for diagnosis of IgAN and were useful methods for exploring and processing proteomic data. In addition, the suggested biomarkers are reliable candidates for further validation to non-invasive diagnose of IgAN based on urine examination.

Estrogen pathway polymorphisms in relation to primary open angle glaucoma: An analysis accounting for gender from the United States

PubMed Central

Loomis, Stephanie J.; Weinreb, Robert N.; Kang, Jae H.; Yaspan, Brian L.; Bailey, Jessica Cooke; Gaasterland, Douglas; Gaasterland, Terry; Lee, Richard K.; Scott, William K.; Lichter, Paul R.; Budenz, Donald L.; Liu, Yutao; Realini, Tony; Friedman, David S.; McCarty, Catherine A.; Moroi, Sayoko E.; Olson, Lana; Schuman, Joel S.; Singh, Kuldev; Vollrath, Douglas; Wollstein, Gadi; Zack, Donald J.; Brilliant, Murray; Sit, Arthur J.; Christen, William G.; Fingert, John; Kraft, Peter; Zhang, Kang; Allingham, R. Rand; Pericak-Vance, Margaret A.; Richards, Julia E.; Hauser, Michael A.; Haines, Jonathan L.; Wiggs, Janey L.

2013-01-01

Purpose Circulating estrogen levels are relevant in glaucoma phenotypic traits. We assessed the association between an estrogen metabolism single nucleotide polymorphism (SNP) panel in relation to primary open angle glaucoma (POAG), accounting for gender. Methods We included 3,108 POAG cases and 3,430 controls of both genders from the Glaucoma Genes and Environment (GLAUGEN) study and the National Eye Institute Glaucoma Human Genetics Collaboration (NEIGHBOR) consortium genotyped on the Illumina 660W-Quad platform. We assessed the relation between the SNP panels representative of estrogen metabolism and POAG using pathway- and gene-based approaches with the Pathway Analysis by Randomization Incorporating Structure (PARIS) software. PARIS executes a permutation algorithm to assess statistical significance relative to the pathways and genes of comparable genetic architecture. These analyses were performed using the meta-analyzed results from the GLAUGEN and NEIGHBOR data sets. We evaluated POAG overall as well as two subtypes of POAG defined as intraocular pressure (IOP) ≥22 mmHg (high-pressure glaucoma [HPG]) or IOP <22 mmHg (normal pressure glaucoma [NPG]) at diagnosis. We conducted these analyses for each gender separately and then jointly in men and women. Results Among women, the estrogen SNP pathway was associated with POAG overall (permuted p=0.006) and HPG (permuted p<0.001) but not NPG (permuted p=0.09). Interestingly, there was no relation between the estrogen SNP pathway and POAG when men were considered alone (permuted p>0.99). Among women, gene-based analyses revealed that the catechol-O-methyltransferase gene showed strong associations with HTG (permuted gene p≤0.001) and NPG (permuted gene p=0.01). Conclusions The estrogen SNP pathway was associated with POAG among women. PMID:23869166

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Lack of evidence and standardization in care pathway documents for patients with ST-elevated myocardial infarction.

PubMed

Aeyels, Daan; Van Vugt, Stijn; Sinnaeve, Peter R; Panella, Massimiliano; Van Zelm, Ruben; Sermeus, Walter; Vanhaecht, Kris

2016-04-01

Clinical practice variation and the subsequent burden on health care quality has been documented for patients with ST-elevated myocardial infarction (STEMI). Reduction of clinical practice variation is possible by increasing guideline adherence. Care pathway documents can increase guideline adherence by implementing evidence-based key interventions and quality indicators in daily practice. This study aims to examine guideline adherence of care pathway documents for patients with STEMI. Lay-out, size and timeframe of submitted care pathways documents were analysed. Two independent reviewers used a checklist to systematically assess the guideline adherence of care pathway documents. The checklist comprised a set of key interventions and quality indicators extracted from evidence and international guidelines. The checklist distinguished the evidence level for each item and was validated by expert consensus. Results were verified by inviting participating hospitals to provide feedback. Fifteen out of 25 invited hospitals submitted care pathway documents for STEMI. The care pathway documents differed in timeframe, lay-out and size. Analysis of the care pathway documents showed important variation in formalizing adherence to evidence: between hospitals, inclusion of 24 key interventions in care pathway documents varied from 13 to 97%. Inclusion of 11 essential quality indicators varied from 0 to 40%. Care pathway documents for patients with STEMI differ considerably in lay-out, timeframe and size. This study showed variation in, and suboptimal inclusion of, evidence-based key interventions and quality indicators in care pathway documents. The use of these care pathway documents might result in suboptimal quality of care for STEMI patients. © The European Society of Cardiology 2015.

Personalized identification of differentially expressed pathways in pediatric sepsis.

PubMed

Li, Binjie; Zeng, Qiyi

2017-10-01

Sepsis is a leading killer of children worldwide with numerous differentially expressed genes reported to be associated with sepsis. Identifying core pathways in an individual is important for understanding septic mechanisms and for the future application of custom therapeutic decisions. Samples used in the study were from a control group (n=18) and pediatric sepsis group (n=52). Based on Kauffman's attractor theory, differentially expressed pathways associated with pediatric sepsis were detected as attractors. When the distribution results of attractors are consistent with the distribution of total data assessed using support vector machine, the individualized pathway aberrance score (iPAS) was calculated to distinguish differences. Through attractor and Kyoto Encyclopedia of Genes and Genomes functional analysis, 277 enriched pathways were identified as attractors. There were 81 pathways with P<0.05 and 59 pathways with P<0.01. Distribution outcomes of screened attractors were mostly consistent with the total data demonstrated by the six classifying parameters, which suggested the efficiency of attractors. Cluster analysis of pediatric sepsis using the iPAS method identified seven pathway clusters and four sample clusters. Thus, in the majority pediatric sepsis samples, core pathways can be detected as different from accumulated normal samples. In conclusion, a novel procedure that identified the dysregulated attractors in individuals with pediatric sepsis was constructed. Attractors can be markers to identify pathways involved in pediatric sepsis. iPAS may provide a correlation score for each of the signaling pathways present in an individual patient. This process may improve the personalized interpretation of disease mechanisms and may be useful in the forthcoming era of personalized medicine.

Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing

PubMed Central

de Anda-Jáuregui, Guillermo; Guo, Kai; McGregor, Brett A.; Hur, Junguk

2018-01-01

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space. PMID:29545755

Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing.

PubMed

de Anda-Jáuregui, Guillermo; Guo, Kai; McGregor, Brett A; Hur, Junguk

2018-01-01

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.

Detecting discordance enrichment among a series of two-sample genome-wide expression data sets.

PubMed

Lai, Yinglei; Zhang, Fanni; Nayak, Tapan K; Modarres, Reza; Lee, Norman H; McCaffrey, Timothy A

2017-01-25

With the current microarray and RNA-seq technologies, two-sample genome-wide expression data have been widely collected in biological and medical studies. The related differential expression analysis and gene set enrichment analysis have been frequently conducted. Integrative analysis can be conducted when multiple data sets are available. In practice, discordant molecular behaviors among a series of data sets can be of biological and clinical interest. In this study, a statistical method is proposed for detecting discordance gene set enrichment. Our method is based on a two-level multivariate normal mixture model. It is statistically efficient with linearly increased parameter space when the number of data sets is increased. The model-based probability of discordance enrichment can be calculated for gene set detection. We apply our method to a microarray expression data set collected from forty-five matched tumor/non-tumor pairs of tissues for studying pancreatic cancer. We divided the data set into a series of non-overlapping subsets according to the tumor/non-tumor paired expression ratio of gene PNLIP (pancreatic lipase, recently shown it association with pancreatic cancer). The log-ratio ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). Our purpose is to understand whether any gene sets are enriched in discordant behaviors among these subsets (when the log-ratio is increased from negative to positive). We focus on KEGG pathways. The detected pathways will be useful for our further understanding of the role of gene PNLIP in pancreatic cancer research. Among the top list of detected pathways, the neuroactive ligand receptor interaction and olfactory transduction pathways are the most significant two. Then, we consider gene TP53 that is well-known for its role as tumor suppressor in cancer research. The log-ratio also ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). We divided the microarray data set again according to the expression ratio of gene TP53. After the discordance enrichment analysis, we observed overall similar results and the above two pathways are still the most significant detections. More interestingly, only these two pathways have been identified for their association with pancreatic cancer in a pathway analysis of genome-wide association study (GWAS) data. This study illustrates that some disease-related pathways can be enriched in discordant molecular behaviors when an important disease-related gene changes its expression. Our proposed statistical method is useful in the detection of these pathways. Furthermore, our method can also be applied to genome-wide expression data collected by the recent RNA-seq technology.

LENS: web-based lens for enrichment and network studies of human proteins

PubMed Central

2015-01-01

Background Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition. Results We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia. Conclusion LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS. PMID:26680011

Imaging of size-dependent uptake and identification of novel pathways in mouse Peyer's patches using fluorescent organosilica particles.

PubMed

Awaad, Aziz; Nakamura, Michihiro; Ishimura, Kazunori

2012-07-01

We investigated size-dependent uptake of fluorescent thiol-organosilica particles by Peyer's patches (PPs). We performed an oral single-particle administration (95, 130, 200, 340, 695 and 1050 nm) and a simultaneous dual-particle administration using 2 kinds of particles. Histological imaging and quantitative analysis revealed that particles taken up by the PP subepithelial dome were size dependent, and there was an optimal size range for higher uptake. Quantitative analysis of simultaneous dual-particle administration revealed that the percentage of fluorescence areas for 95, 130, 200, 340, 695 and 1050 nm with respect to 110 nm area was 124.0, 89.1, 73.8, 20.2, 9.2 and 0.5%, respectively. Additionally, imaging using fluorescent thiol-organosilica particles could detect 2 novel pathways through mouse PP epithelium: the transcellular pathway and the paracellular pathway. The uptake of nanoparticles based on an optimal size range and 2 novel pathways could indicate a new approach for vaccine delivery and nanomedicine development. Studying various sizes of fluorescent organosilica particles and their uptake in Peyer's patches, this team of authors determined the optimal size range of administration. Two novel pathways through mouse Peyer's patch epithelium were detected, i.e., the transcellular pathway and the paracellular pathway. This observation may have important applications in future vaccine delivery and nano-drug delivery. Copyright © 2012 Elsevier Inc. All rights reserved.

Whole-exome sequencing in obsessive-compulsive disorder identifies rare mutations in immunological and neurodevelopmental pathways

PubMed Central

Cappi, C; Brentani, H; Lima, L; Sanders, S J; Zai, G; Diniz, B J; Reis, V N S; Hounie, A G; Conceição do Rosário, M; Mariani, D; Requena, G L; Puga, R; Souza-Duran, F L; Shavitt, R G; Pauls, D L; Miguel, E C; Fernandez, T V

2016-01-01

Studies of rare genetic variation have identified molecular pathways conferring risk for developmental neuropsychiatric disorders. To date, no published whole-exome sequencing studies have been reported in obsessive-compulsive disorder (OCD). We sequenced all the genome coding regions in 20 sporadic OCD cases and their unaffected parents to identify rare de novo (DN) single-nucleotide variants (SNVs). The primary aim of this pilot study was to determine whether DN variation contributes to OCD risk. To this aim, we evaluated whether there is an elevated rate of DN mutations in OCD, which would justify this approach toward gene discovery in larger studies of the disorder. Furthermore, to explore functional molecular correlations among genes with nonsynonymous DN SNVs in OCD probands, a protein–protein interaction (PPI) network was generated based on databases of direct molecular interactions. We applied Degree-Aware Disease Gene Prioritization (DADA) to rank the PPI network genes based on their relatedness to a set of OCD candidate genes from two OCD genome-wide association studies (Stewart et al., 2013; Mattheisen et al., 2014). In addition, we performed a pathway analysis with genes from the PPI network. The rate of DN SNVs in OCD was 2.51 × 10−8 per base per generation, significantly higher than a previous estimated rate in unaffected subjects using the same sequencing platform and analytic pipeline. Several genes harboring DN SNVs in OCD were highly interconnected in the PPI network and ranked high in the DADA analysis. Nearly all the DN SNVs in this study are in genes expressed in the human brain, and a pathway analysis revealed enrichment in immunological and central nervous system functioning and development. The results of this pilot study indicate that further investigation of DN variation in larger OCD cohorts is warranted to identify specific risk genes and to confirm our preliminary finding with regard to PPI network enrichment for particular biological pathways and functions. PMID:27023170

Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

PubMed

Lee, Hyeonjeong; Shin, Miyoung

2017-01-01

The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data. The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively. Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into distinctive associations between pathway activities in case and control samples.

Groups: knowledge spreadsheets for symbolic biocomputing.

PubMed

Travers, Michael; Paley, Suzanne M; Shrager, Jeff; Holland, Timothy A; Karp, Peter D

2013-01-01

Knowledge spreadsheets (KSs) are a visual tool for interactive data analysis and exploration. They differ from traditional spreadsheets in that rather than being oriented toward numeric data, they work with symbolic knowledge representation structures and provide operations that take into account the semantics of the application domain. 'Groups' is an implementation of KSs within the Pathway Tools system. Groups allows Pathway Tools users to define a group of objects (e.g. groups of genes or metabolites) from a Pathway/Genome Database. Groups can be transformed (e.g. by transforming a metabolite group to the group of pathways in which those metabolites are substrates); combined through set operations; analysed (e.g. through enrichment analysis); and visualized (e.g. by painting onto a metabolic map diagram). Users of the Pathway Tools-based BioCyc.org website have made extensive use of Groups, and an informal survey of Groups users suggests that Groups has achieved the goal of allowing biologists themselves to perform some data manipulations that previously would have required the assistance of a programmer. Database URL: BioCyc.org.

Exploring metabolic pathways in genome-scale networks via generating flux modes.

PubMed

Rezola, A; de Figueiredo, L F; Brock, M; Pey, J; Podhorski, A; Wittmann, C; Schuster, S; Bockmayr, A; Planes, F J

2011-02-15

The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. The source code in C++ is available upon request.

Application of artifical intelligence principles to the analysis of "crazy" speech.

PubMed

Garfield, D A; Rapp, C

1994-04-01

Artificial intelligence computer simulation methods can be used to investigate psychotic or "crazy" speech. Here, symbolic reasoning algorithms establish semantic networks that schematize speech. These semantic networks consist of two main structures: case frames and object taxonomies. Node-based reasoning rules apply to object taxonomies and pathway-based reasoning rules apply to case frames. Normal listeners may recognize speech as "crazy talk" based on violations of node- and pathway-based reasoning rules. In this article, three separate segments of schizophrenic speech illustrate violations of these rules. This artificial intelligence approach is compared and contrasted with other neurolinguistic approaches and is discussed as a conceptual link between neurobiological and psychodynamic understandings of psychopathology.

Enhanced recovery pathways in abdominal gynecologic surgery: a systematic review and meta-analysis.

PubMed

de Groot, Jeanny J A; Ament, Stephanie M C; Maessen, José M C; Dejong, Cornelis H C; Kleijnen, Jos M P; Slangen, Brigitte F M

2016-04-01

Enhanced recovery pathways have been widely accepted and implemented for different types of surgery. Their overall effect in abdominal gynecologic surgery is still underdetermined. A systematic review and meta-analysis were performed to provide an overview of current evidence and to examine their effect on postoperative outcomes in women undergoing open gynecologic surgery. Searches were conducted using Embase, Medline, CINAHL, and the Cochrane Library up to 27 June 2014. Reference lists were screened to identify additional studies. Studies were included if at least four individual items of an enhanced recovery pathway were described. Outcomes included length of hospital stay, complication rates, readmissions, and mortality. Quantitative analysis was limited to comparative studies. Effect sizes were presented as relative risks or as mean differences (MD) with 95% confidence intervals (CI). Thirty-one records, involving 16 observational studies, were included. Diversity in reported elements within studies was observed. Preoperative education, early oral intake, and early mobilization were included in all pathways. Five studies, with a high risk of bias, were eligible for quantitative analysis. Enhanced recovery pathways reduced primary (MD -1.57 days, 95% CI CI -2.94 to -0.20) and total (MD -3.05 days, 95% CI -4.87 to -1.23) length of hospital stay compared with traditional perioperative care, without an increase in complications, mortality or readmission rates. The available evidence based on a broad range of non-randomized studies at high risk of bias suggests that enhanced recovery pathways may reduce length of postoperative hospital stay in abdominal gynecologic surgery. © 2015 Nordic Federation of Societies of Obstetrics and Gynecology.

A Cross-Cancer Genetic Association Analysis of the DNA Repair and DNA Damage Signaling Pathways for Lung, Ovary, Prostate, Breast, and Colorectal Cancer.

PubMed

Scarbrough, Peter M; Weber, Rachel Palmieri; Iversen, Edwin S; Brhane, Yonathan; Amos, Christopher I; Kraft, Peter; Hung, Rayjean J; Sellers, Thomas A; Witte, John S; Pharoah, Paul; Henderson, Brian E; Gruber, Stephen B; Hunter, David J; Garber, Judy E; Joshi, Amit D; McDonnell, Kevin; Easton, Doug F; Eeles, Ros; Kote-Jarai, Zsofia; Muir, Kenneth; Doherty, Jennifer A; Schildkraut, Joellen M

2016-01-01

DNA damage is an established mediator of carcinogenesis, although genome-wide association studies (GWAS) have identified few significant loci. This cross-cancer site, pooled analysis was performed to increase the power to detect common variants of DNA repair genes associated with cancer susceptibility. We conducted a cross-cancer analysis of 60,297 single nucleotide polymorphisms, at 229 DNA repair gene regions, using data from the NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) Network. Our analysis included data from 32 GWAS and 48,734 controls and 51,537 cases across five cancer sites (breast, colon, lung, ovary, and prostate). Because of the unavailability of individual data, data were analyzed at the aggregate level. Meta-analysis was performed using the Association analysis for SubSETs (ASSET) software. To test for genetic associations that might escape individual variant testing due to small effect sizes, pathway analysis of eight DNA repair pathways was performed using hierarchical modeling. We identified three susceptibility DNA repair genes, RAD51B (P < 5.09 × 10(-6)), MSH5 (P < 5.09 × 10(-6)), and BRCA2 (P = 5.70 × 10(-6)). Hierarchical modeling identified several pleiotropic associations with cancer risk in the base excision repair, nucleotide excision repair, mismatch repair, and homologous recombination pathways. Only three susceptibility loci were identified, which had all been previously reported. In contrast, hierarchical modeling identified several pleiotropic cancer risk associations in key DNA repair pathways. Results suggest that many common variants in DNA repair genes are likely associated with cancer susceptibility through small effect sizes that do not meet stringent significance testing criteria. ©2015 American Association for Cancer Research.

The Impact of GFP Reporter Gene Transduction and Expression on Metabolomics of Placental Mesenchymal Stem Cells Determined by UHPLC-Q/TOF-MS.

PubMed

Yang, Jinfeng; Wang, Nan; Chen, Deying; Yu, Jiong; Pan, Qiaoling; Wang, Dan; Liu, Jingqi; Shi, Xiaowei; Dong, Xiaotian; Cao, Hongcui; Li, Liang; Li, Lanjuan

2017-01-01

Green fluorescent protein (GFP) is widely used as a reporter gene in regenerative medicine research to label and track stem cells. Here, we examined whether expressing GFP gene may impact the metabolism of human placental mesenchymal stem cells (hPMSCs). The GFP gene was transduced into hPMSCs using lentiviral-based infection to establish GFP + hPMSCs. A sensitive 13 C/ 12 C-dansyl labeling LC-MS method targeting the amine/phenol submetabolome was used for in-depth cell metabolome profiling. A total of 1151 peak pairs or metabolites were detected from 12 LC-MS runs. Principal component analysis and partial least squares discriminant analysis showed poor separation, and the volcano plots demonstrated that most of the metabolites were not significantly changed when hPMSCs were tagged with GFP. Overall, 739 metabolites were positively or putatively identified. Only 11 metabolites showed significant changes. Metabolic pathway analyses indicated that three of the identified metabolites were involved in nine pathways. However, these metabolites are unlikely to have a large impact on the metabolic pathways due to their nonessential roles and limited hits in pathway analysis. This study indicated that the expression of ectopic GFP reporter gene did not significantly alter the metabolomics pathways covered by the amine/phenol submetabolome.

Prognostic significance of microsatellite instability‑associated pathways and genes in gastric cancer.

PubMed

Hang, Xiaosheng; Li, Dapeng; Wang, Jianping; Wang, Ge

2018-07-01

The aim of the present study was to reveal the potential molecular mechanisms of microsatellite instability (MSI) on the prognosis of gastric cancer (GC). The investigation was performed based on an RNAseq expression profiling dataset downloaded from The Cancer Genome Atlas, including 64 high‑level MSI (MSI‑H) GC samples, 44 low‑level MSI (MSI‑L) GC samples and 187 stable microsatellite (MSI‑S) GC samples. Differentially expressed genes (DEGs) were identified between the MSI‑H, MSI‑L and MSI‑S samples. Pathway enrichment analysis was performed for the identified DEGs and the pathway deviation scores of the significant enrichment pathways were calculated. A Multi‑Layer Perceptron (MLP) classifier, based on the different pathways associated with the MSI statuses was constructed for predicting the outcome of patients with GC, which was validated in another independent dataset. A total of 190 DEGs were selected between the MSI‑H, MSI‑L and MSI‑S samples. The MLP classifier was established based on the deviation scores of 10 significant pathways, among which antigen processing and presentation, and inflammatory bowel disease pathways were significantly enriched with HLA‑DRB5, HLA‑DMA, HLA‑DQA1 and HLA‑DRA; the measles, toxoplasmosis and herpes simplex infection pathways were significantly enriched with Janus kinase 2 (JAK2), caspase‑8 (CASP8) and Fas. The classifier performed well on an independent validation set with 100 GC samples. Taken together, the results indicated that MSI status may affect GC prognosis, partly through the antigen processing and presentation, inflammatory bowel disease, measles, toxoplasmosis and herpes simplex infection pathways. HLA‑DRB5, HLA‑DMA, HLA‑DQA1, HLA‑DRA, JAK2, CASP8 and Fas may be predictive factors for prognosis in GC.

The RCPCH care pathway for children at risk of anaphylaxis: an evidence and consensus based national approach to caring for children with life-threatening allergies.

PubMed

Clark, Andrew; Lloyd, Kate; Sheikh, Aziz; Alfaham, Mazin; East, Mandy; Ewan, Pamela; Jewkes, Fiona; King, Rosie; Leech, Susan; Maconochie, Ian; Sinnott, Louise; Sohi, Dalbir; Tomlin, Stephen; Warner, John

2011-11-01

Numerous studies have identified shortcomings in the management of children at risk of severe acute allergic reactions (anaphylaxis). The Science and Research Department at the Royal College of Paediatrics and Child Health (RCPCH) was commissioned by the Department of Health to develop competence based national care pathways for children with allergies. Anaphylaxis is the first completed pathway. The anaphylaxis pathway was developed by a multidisciplinary working group, reviewed by a broad group of stakeholders and approved by the Allergy Care Pathways Project Board and the RCPCH Clinical Standards Committee. Pathway development is described under five headings: evidence review, mapping, external review, core knowledge documents and key recommendations. The full pathway can be downloaded from www.rcpch.ac.uk/allergy/anaphylaxis. This document describes the entry points and the ideal pathway of care from self-care through to follow-up. The five key recommendations focus on: (1) prompt administration of adrenaline by intramuscular injection; (2) referral to specialists with competence in paediatric allergies; (3) risk analysis; (4) provision of a self-management plan; and (5) suggested creation of a national anaphylaxis death register. We present the first national care pathway for anaphylaxis, which is based on a critique of published evidence, expert consensus and multi-stakeholder input including patient representation via the Anaphylaxis Campaign. The Project Board urges health professionals to work together across networks to improve care for children at risk of anaphylaxis, in particular during the period after an acute reaction. Additionally, the Project Board strongly recommends the funding of a national anaphylaxis register.

Development, implementation, and compliance of treatment pathways in radiation medicine.

PubMed

Potters, Louis; Raince, Jadeep; Chou, Henry; Kapur, Ajay; Bulanowski, Daniel; Stanzione, Regina; Lee, Lucille

2013-01-01

While much emphasis on safety in the radiation oncology clinic is placed on process, there remains considerable opportunity to increase safety, enhance outcomes, and avoid ad hoc care by instituting detailed treatment pathways. The purpose of this study was to review the process of developing evidence and consensus-based, outcomes-oriented treatment pathways that standardize treatment and patient management in a large multi-center radiation oncology practice. Further, we reviewed our compliance in incorporating these directives into our day-to-day clinical practice. Using the Institute of Medicine guideline for developing treatment pathways, 87 disease specific pathways were developed and incorporated into the electronic medical system in our multi-facility radiation oncology department. Compliance in incorporating treatment pathways was assessed by mining our electronic medical records (EMR) data from January 1, 2010 through February 2012 for patients with breast and prostate cancer. This retrospective analysis of data from EMR found overall compliance to breast and prostate cancer treatment pathways to be 97 and 99%, respectively. The reason for non-compliance proved to be either a failure to complete the prescribed care based on grade II or III toxicity (n = 1 breast, 3 prostate) or patient elected discontinuance of care (n = 1 prostate) or the physician chose a higher dose for positive/close margins (n = 3 breast). This study demonstrates that consensus and evidence-based treatment pathways can be developed and implemented in a multi-center department of radiation oncology. And that for prostate and breast cancer there was a high degree of compliance using these directives. The development and implementation of these pathways serve as a key component of our safety program, most notably in our effort to facilitate consistent decision-making and reducing variation between physicians.

Decomposition of complex microbial behaviors into resource-based stress responses

PubMed Central

Carlson, Ross P.

2009-01-01

Motivation: Highly redundant metabolic networks and experimental data from cultures likely adapting simultaneously to multiple stresses can complicate the analysis of cellular behaviors. It is proposed that the explicit consideration of these factors is critical to understanding the competitive basis of microbial strategies. Results: Wide ranging, seemingly unrelated Escherichia coli physiological fluxes can be simply and accurately described as linear combinations of a few ecologically relevant stress adaptations. These strategies were identified by decomposing the central metabolism of E.coli into elementary modes (mathematically defined biochemical pathways) and assessing the resource investment cost–benefit properties for each pathway. The approach capitalizes on the inherent tradeoffs related to investing finite resources like nitrogen into different pathway enzymes when the pathways have varying metabolic efficiencies. The subset of ecologically competitive pathways represented 0.02% of the total permissible pathways. The biological relevance of the assembled strategies was tested against 10 000 randomly constructed pathway subsets. None of the randomly assembled collections were able to describe all of the considered experimental data as accurately as the cost-based subset. The results suggest these metabolic strategies are biologically significant. The current descriptions were compared with linear programming (LP)-based flux descriptions using the Euclidean distance metric. The current study's pathway subset described the experimental fluxes with better accuracy than the LP results without having to test multiple objective functions or constraints and while providing additional ecological insight into microbial behavior. The assembled pathways seem to represent a generalized set of strategies that can describe a wide range of microbial responses and hint at evolutionary processes where a handful of successful metabolic strategies are utilized simultaneously in different combinations to adapt to diverse conditions. Contact: rossc@biofilms.montana.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19008248

Identification of novel biomarker and therapeutic target candidates for acute intracerebral hemorrhage by quantitative plasma proteomics.

PubMed

Li, Guo-Chun; Zhang, Lina; Yu, Ming; Jia, Haiyu; Tian, Ting; Wang, Junqin; Wang, Fuqiang; Zhou, Ling

2017-01-01

The systematic mechanisms of acute intracerebral hemorrhage are still unknown and unverified, although many recent researches have indicated the secondary insults. This study was aimed to disclose the pathological mechanism and identify novel biomarker and therapeutic target candidates by plasma proteome. Patients with AICH (n = 8) who demographically matched healthy controls (n = 4) were prospectively enrolled, and their plasma samples were obtained. The TMT-LC-MS/MS-based proteomics approach was used to quantify the differential proteome across plasma samples, and the results were analyzed by Ingenuity Pathway Analysis to explore canonical pathways and the relationship involved in the uploaded data. Compared with healthy controls, there were 31 differentially expressed proteins in the ICH group ( P  < 0.05), of which 21 proteins increased while 10 proteins decreased in abundance. These proteins are involved in 21 canonical pathways. One network with high confidence level was selected by the function network analysis, in which 23 proteins, P38MAPK and NFκB signaling pathways participated. Upstream regulator analysis found two regulators, IL6 and TNF, with an activation z -score. Seven biomarker candidates: APCS, FGB, LBP, MGMT, IGFBP2, LYZ, and APOA4 were found. Six candidate proteins were selected to assess the validity of the results by subsequent Western blotting analysis. Our analysis provided several intriguing pathways involved in ICH, like LXR/RXR activation, acute phase response signaling, and production of NO and ROS in macrophages pathways. The three upstream regulators: IL-6, TNF, LPS, and seven biomarker candidates: APCS, APOA4, FGB, IGFBP2, LBP, LYZ, and MGMT were uncovered. LPS, APOA4, IGFBP2, LBP, LYZ, and MGMT are novel potential biomarkers in ICH development. The identified proteins and pathways provide new perspectives to the potential pathological mechanism and therapeutic targets underlying ICH.

Kinetic Analysis of Competitive Electrocatalytic Pathways: New Insights into Hydrogen Production with Nickel Electrocatalysts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiedner, Eric S.; Brown, Houston J.; Helm, Monte L.

2016-01-20

The hydrogen production electrocatalyst Ni(PPh2NPh2)22+ (1) is capable of traversing multiple electrocatalytic pathways. When using dimethylformamidium, DMF(H)+, the mechanism of formation of H2 catalyzed by 1 changes from an ECEC to an EECC mechanism as the potential approaches the Ni(I/0) couple. Two recent electrochemical methods, current-potential analysis and foot-of-the-wave analysis (FOWA), were performed on 1 to measure the detailed chemical kinetics of the competing ECEC and EECC pathways. A sensitivity analysis was performed on the electrochemical methods using digital simulations to gain a better understanding of their strengths and limitations. Notably, chemical rate constants were significantly underestimated when not accountingmore » for electron transfer kinetics, even when electron transfer was fast enough to afford a reversible non-catalytic wave. The EECC pathway of 1 was found to be faster than the ECEC pathway under all conditions studied. Using buffered DMF: DMF(H)+ mixtures led to an increase in the catalytic rate constant (kobs) of the EECC pathway, but kobs for the ECEC pathway did not change when using buffered acid. Further kinetic analysis of the ECEC path revealed that added base increases the rate of isomerization of the exo-protonated Ni(0) isomers to the catalytically active endo-isomers, but decreases the net rate of protonation of Ni(I). FOWA on 1 did not provide accurate rate constants due to incomplete reduction of the exo-protonated Ni(I) intermediate at the foot of the wave, but FOWA could be used to estimate the reduction potential of this previously undetected intermediate. This research was supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. Pacific Northwest National Laboratory is operated by Battelle for the U.S. Department of Energy.« less

Cost and Survival Analysis Before and After Implementation of Dana-Farber Clinical Pathways for Patients With Stage IV Non-Small-Cell Lung Cancer.

PubMed

Jackman, David M; Zhang, Yichen; Dalby, Carole; Nguyen, Tom; Nagle, Julia; Lydon, Christine A; Rabin, Michael S; McNiff, Kristen K; Fraile, Belen; Jacobson, Joseph O

2017-04-01

Increasing costs and medical complexity are significant challenges in modern oncology. We explored the use of clinical pathways to support clinical decision making and manage resources prospectively across our network. We created customized lung cancer pathways and partnered with a commercial vendor to provide a Web-based platform for real-time decision support and post-treatment data aggregation. Dana-Farber Cancer Institute (DFCI) Pathways for non-small cell lung cancer (NSCLC) were introduced in January 2014. We identified all DFCI patients who were diagnosed and treated for stage IV NSCLC in 2012 (before pathways) and 2014 (after pathways). Costs of care were determined for 1 year from the time of diagnosis. Pre- and postpathway cohorts included 160 and 210 patients with stage IV NSCLC, respectively. The prepathway group had more women but was otherwise similarly matched for demographic and tumor characteristics. The total 12-month cost of care (adjusted for age, sex, race, distance to DFCI, clinical trial enrollment, and EGFR and ALK status) demonstrated a $15,013 savings after the implementation of pathways ($67,050 before pathways v $52,037 after pathways). Antineoplastics were the largest source of cost savings. Clinical outcomes were not compromised, with similar median overall survival times (10.7 months before v 11.2 months after pathways; P = .08). After introduction of a clinical pathway in metastatic NSCLC, cost of care decreased significantly, with no compromise in survival. In an era where comparative outcomes analysis and value assessment are increasingly important, the implementation of clinical pathways may provide a means to coalesce and disseminate institutional expertise and track and learn from care decisions.

Gene- and pathway-based association tests for multiple traits with GWAS summary statistics.

PubMed

Kwak, Il-Youp; Pan, Wei

2017-01-01

To identify novel genetic variants associated with complex traits and to shed new insights on underlying biology, in addition to the most popular single SNP-single trait association analysis, it would be useful to explore multiple correlated (intermediate) traits at the gene- or pathway-level by mining existing single GWAS or meta-analyzed GWAS data. For this purpose, we present an adaptive gene-based test and a pathway-based test for association analysis of multiple traits with GWAS summary statistics. The proposed tests are adaptive at both the SNP- and trait-levels; that is, they account for possibly varying association patterns (e.g. signal sparsity levels) across SNPs and traits, thus maintaining high power across a wide range of situations. Furthermore, the proposed methods are general: they can be applied to mixed types of traits, and to Z-statistics or P-values as summary statistics obtained from either a single GWAS or a meta-analysis of multiple GWAS. Our numerical studies with simulated and real data demonstrated the promising performance of the proposed methods. The methods are implemented in R package aSPU, freely and publicly available at: https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment.

PubMed

Bohler, Anwesha; Eijssen, Lars M T; van Iersel, Martijn P; Leemans, Christ; Willighagen, Egon L; Kutmon, Martina; Jaillard, Magali; Evelo, Chris T

2015-08-23

Biological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers. We developed PathVisioRPC, an XMLRPC interface for the pathway analysis software PathVisio. PathVisioRPC enables creating and editing biological pathways, visualising data on pathways, performing pathway statistics, and exporting results in several image formats in multiple programming environments. We demonstrate PathVisioRPC functionalities using examples in Python. Subsequently, we analyse a publicly available NCBI GEO gene expression dataset studying tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly work together. To further support R users, we have created RPathVisio simplifying the use of PathVisioRPC in this environment. We have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that calls the PathVisioRPC interface to perform pathway analysis. This module allows users to use PathVisio functionality online without having to download and install the software and exemplifies how the PathVisioRPC interface can be used by data analysis pipelines for functional analysis of processed genomics data. PathVisioRPC enables data visualisation and pathway analysis directly from within various analytical environments used for preliminary analyses. It supports the use of existing pathways from WikiPathways or pathways created using the RPC itself. It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks. PathVisioRPC is freely available for academic and commercial use at http://projects.bigcat.unimaas.nl/pathvisiorpc.

Optimization of a cAMP response element signal pathway reporter system.

PubMed

Shan, Qiang; Storm, Daniel R

2010-08-15

A sensitive cAMP response element (CRE) reporter system is essential for studying the cAMP/protein kinase A/cAMP response element binding protein signal pathway. Here we have tested a few CRE promoters and found one with high sensitivity to external stimuli. Using this optimal CRE promoter and the enhanced green fluorescent protein as the reporter, we have established a CRE reporter cell line. This cell line can be used to study the signal pathway by fluorescent microscope, fluorescence-activated cell analysis and luciferase assay. This cell line's sensitivity to forskolin, using the technique of fluorescence-activated cell sorting, was increased to approximately seven times that of its parental HEK 293 cell line, which is currently the most commonly used cell line in the field for the signal pathway study. Therefore, this newly created cell line is potentially useful for studying the signal pathway's modulators, which generally have weaker effect than its mediators. Our research has also established a general procedure for optimizing transcription-based reporter cell lines, which might be useful in performing the same task when studying many other transcription-based signal pathways. (c) 2010 Elsevier B.V. All rights reserved.

Molecular Pathways: Extracting Medical Knowledge from High Throughput Genomic Data

PubMed Central

Goldstein, Theodore; Paull, Evan O.; Ellis, Matthew J.; Stuart, Joshua M.

2013-01-01

High-throughput genomic data that measures RNA expression, DNA copy number, mutation status and protein levels provide us with insights into the molecular pathway structure of cancer. Genomic lesions (amplifications, deletions, mutations) and epigenetic modifications disrupt biochemical cellular pathways. While the number of possible lesions is vast, different genomic alterations may result in concordant expression and pathway activities, producing common tumor subtypes that share similar phenotypic outcomes. How can these data be translated into medical knowledge that provides prognostic and predictive information? First generation mRNA expression signatures such as Genomic Health's Oncotype DX already provide prognostic information, but do not provide therapeutic guidance beyond the current standard of care – which is often inadequate in high-risk patients. Rather than building molecular signatures based on gene expression levels, evidence is growing that signatures based on higher-level quantities such as from genetic pathways may provide important prognostic and diagnostic cues. We provide examples of how activities for molecular entities can be predicted from pathway analysis and how the composite of all such activities, referred to here as the “activitome,” help connect genomic events to clinical factors in order to predict the drivers of poor outcome. PMID:23430023

Exploratory Application of Neuropharmacometabolomics in Severe Childhood Traumatic Brain Injury.

PubMed

Hagos, Fanuel T; Empey, Philip E; Wang, Pengcheng; Ma, Xiaochao; Poloyac, Samuel M; Bayır, Hülya; Kochanek, Patrick M; Bell, Michael J; Clark, Robert S B

2018-05-07

To employ metabolomics-based pathway and network analyses to evaluate the cerebrospinal fluid metabolome after severe traumatic brain injury in children and the capacity of combination therapy with probenecid and N-acetylcysteine to impact glutathione-related and other pathways and networks, relative to placebo treatment. Analysis of cerebrospinal fluid obtained from children enrolled in an Institutional Review Board-approved, randomized, placebo-controlled trial of a combination of probenecid and N-acetylcysteine after severe traumatic brain injury (Trial Registration NCT01322009). Thirty-six-bed PICU in a university-affiliated children's hospital. Twelve children 2-18 years old after severe traumatic brain injury and five age-matched control subjects. Probenecid (25 mg/kg) and N-acetylcysteine (140 mg/kg) or placebo administered via naso/orogastric tube. The cerebrospinal fluid metabolome was analyzed in samples from traumatic brain injury patients 24 hours after the first dose of drugs or placebo and control subjects. Feature detection, retention time, alignment, annotation, and principal component analysis and statistical analysis were conducted using XCMS-online. The software "mummichog" was used for pathway and network analyses. A two-component principal component analysis revealed clustering of each of the groups, with distinct metabolomics signatures. Several novel pathways with plausible mechanistic involvement in traumatic brain injury were identified. A combination of metabolomics and pathway/network analyses showed that seven glutathione-centered pathways and two networks were enriched in the cerebrospinal fluid of traumatic brain injury patients treated with probenecid and N-acetylcysteine versus placebo-treated patients. Several additional pathways/networks consisting of components that are known substrates of probenecid-inhibitable transporters were also identified, providing additional mechanistic validation. This proof-of-concept neuropharmacometabolomics assessment reveals alterations in known and previously unidentified metabolic pathways and supports therapeutic target engagement of the combination of probenecid and N-acetylcysteine treatment after severe traumatic brain injury in children.

Pathway-Based Analysis of Genome-Wide siRNA Screens Reveals the Regulatory Landscape of App Processing

PubMed Central

Camargo, Luiz Miguel; Zhang, Xiaohua Douglas; Loerch, Patrick; Caceres, Ramon Miguel; Marine, Shane D.; Uva, Paolo; Ferrer, Marc; de Rinaldis, Emanuele; Stone, David J.; Majercak, John; Ray, William J.; Yi-An, Chen; Shearman, Mark S.; Mizuguchi, Kenji

2015-01-01

The progressive aggregation of Amyloid-β (Aβ) in the brain is a major trait of Alzheimer's Disease (AD). Aβ is produced as a result of proteolytic processing of the β-amyloid precursor protein (APP). Processing of APP is mediated by multiple enzymes, resulting in the production of distinct peptide products: the non-amyloidogenic peptide sAPPα and the amyloidogenic peptides sAPPβ, Aβ40, and Aβ42. Using a pathway-based approach, we analyzed a large-scale siRNA screen that measured the production of different APP proteolytic products. Our analysis identified many of the biological processes/pathways that are known to regulate APP processing and have been implicated in AD pathogenesis, as well as revealing novel regulatory mechanisms. Furthermore, we also demonstrate that some of these processes differentially regulate APP processing, with some mechanisms favouring production of certain peptide species over others. For example, synaptic transmission having a bias towards regulating Aβ40 production over Aβ42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPβ production over sAPPα. In addition, some of the pathways identified as regulators of APP processing contain genes (CLU, BIN1, CR1, PICALM, TREM2, SORL1, MEF2C, DSG2, EPH1A) recently implicated with AD through genome wide association studies (GWAS) and associated meta-analysis. In addition, we provide supporting evidence and a deeper mechanistic understanding of the role of diabetes in AD. The identification of these processes/pathways, their differential impact on APP processing, and their relationships to each other, provide a comprehensive systems biology view of the “regulatory landscape” of APP. PMID:25723573

Pathway cross-talk network analysis identifies critical pathways in neonatal sepsis.

PubMed

Meng, Yu-Xiu; Liu, Quan-Hong; Chen, Deng-Hong; Meng, Ying

2017-06-01

Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. By integrating three kinds of data, only 6919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Connectome imaging for mapping human brain pathways

PubMed Central

Shi, Y; Toga, A W

2017-01-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research. PMID:28461700

Transcriptomic analysis of flower development in wintersweet (Chimonanthus praecox).

PubMed

Liu, Daofeng; Sui, Shunzhao; Ma, Jing; Li, Zhineng; Guo, Yulong; Luo, Dengpan; Yang, Jianfeng; Li, Mingyang

2014-01-01

Wintersweet (Chimonanthus praecox) is familiar as a garden plant and woody ornamental flower. On account of its unique flowering time and strong fragrance, it has a high ornamental and economic value. Despite a long history of human cultivation, our understanding of wintersweet genetics and molecular biology remains scant, reflecting a lack of basic genomic and transcriptomic data. In this study, we assembled three cDNA libraries, from three successive stages in flower development, designated as the flower bud with displayed petal, open flower and senescing flower stages. Using the Illumina RNA-Seq method, we obtained 21,412,928, 26,950,404, 24,912,954 qualified Illumina reads, respectively, for the three successive stages. The pooled reads from all three libraries were then assembled into 106,995 transcripts, 51,793 of which were annotated in the NCBI non-redundant protein database. Of these annotated sequences, 32,649 and 21,893 transcripts were assigned to gene ontology categories and clusters of orthologous groups, respectively. We could map 15,587 transcripts onto 312 pathways using the Kyoto Encyclopedia of Genes and Genomes pathway database. Based on these transcriptomic data, we obtained a large number of candidate genes that were differentially expressed at the open flower and senescing flower stages. An analysis of differentially expressed genes involved in plant hormone signal transduction pathways indicated that although flower opening and senescence may be independent of the ethylene signaling pathway in wintersweet, salicylic acid may be involved in the regulation of flower senescence. We also succeeded in isolating key genes of floral scent biosynthesis and proposed a biosynthetic pathway for monoterpenes and sesquiterpenes in wintersweet flowers, based on the annotated sequences. This comprehensive transcriptomic analysis presents fundamental information on the genes and pathways which are involved in flower development in wintersweet. And our data provided a useful database for further research of wintersweet and other Calycanthaceae family plants.

Transcriptomic Analysis of Flower Development in Wintersweet (Chimonanthus praecox)

PubMed Central

Liu, Daofeng; Sui, Shunzhao; Ma, Jing; Li, Zhineng; Guo, Yulong; Luo, Dengpan; Yang, Jianfeng; Li, Mingyang

2014-01-01

Wintersweet (Chimonanthus praecox) is familiar as a garden plant and woody ornamental flower. On account of its unique flowering time and strong fragrance, it has a high ornamental and economic value. Despite a long history of human cultivation, our understanding of wintersweet genetics and molecular biology remains scant, reflecting a lack of basic genomic and transcriptomic data. In this study, we assembled three cDNA libraries, from three successive stages in flower development, designated as the flower bud with displayed petal, open flower and senescing flower stages. Using the Illumina RNA-Seq method, we obtained 21,412,928, 26,950,404, 24,912,954 qualified Illumina reads, respectively, for the three successive stages. The pooled reads from all three libraries were then assembled into 106,995 transcripts, 51,793 of which were annotated in the NCBI non-redundant protein database. Of these annotated sequences, 32,649 and 21,893 transcripts were assigned to gene ontology categories and clusters of orthologous groups, respectively. We could map 15,587 transcripts onto 312 pathways using the Kyoto Encyclopedia of Genes and Genomes pathway database. Based on these transcriptomic data, we obtained a large number of candidate genes that were differentially expressed at the open flower and senescing flower stages. An analysis of differentially expressed genes involved in plant hormone signal transduction pathways indicated that although flower opening and senescence may be independent of the ethylene signaling pathway in wintersweet, salicylic acid may be involved in the regulation of flower senescence. We also succeeded in isolating key genes of floral scent biosynthesis and proposed a biosynthetic pathway for monoterpenes and sesquiterpenes in wintersweet flowers, based on the annotated sequences. This comprehensive transcriptomic analysis presents fundamental information on the genes and pathways which are involved in flower development in wintersweet. And our data provided a useful database for further research of wintersweet and other Calycanthaceae family plants. PMID:24489818

EDdb: a web resource for eating disorder and its application to identify an extended adipocytokine signaling pathway related to eating disorder.

PubMed

Zhao, Min; Li, XiaoMo; Qu, Hong

2013-12-01

Eating disorder is a group of physiological and psychological disorders affecting approximately 1% of the female population worldwide. Although the genetic epidemiology of eating disorder is becoming increasingly clear with accumulated studies, the underlying molecular mechanisms are still unclear. Recently, integration of various high-throughput data expanded the range of candidate genes and started to generate hypotheses for understanding potential pathogenesis in complex diseases. This article presents EDdb (Eating Disorder database), the first evidence-based gene resource for eating disorder. Fifty-nine experimentally validated genes from the literature in relation to eating disorder were collected as the core dataset. Another four datasets with 2824 candidate genes across 601 genome regions were expanded based on the core dataset using different criteria (e.g., protein-protein interactions, shared cytobands, and related complex diseases). Based on human protein-protein interaction data, we reconstructed a potential molecular sub-network related to eating disorder. Furthermore, with an integrative pathway enrichment analysis of genes in EDdb, we identified an extended adipocytokine signaling pathway in eating disorder. Three genes in EDdb (ADIPO (adiponectin), TNF (tumor necrosis factor) and NR3C1 (nuclear receptor subfamily 3, group C, member 1)) link the KEGG (Kyoto Encyclopedia of Genes and Genomes) "adipocytokine signaling pathway" with the BioCarta "visceral fat deposits and the metabolic syndrome" pathway to form a joint pathway. In total, the joint pathway contains 43 genes, among which 39 genes are related to eating disorder. As the first comprehensive gene resource for eating disorder, EDdb ( http://eddb.cbi.pku.edu.cn ) enables the exploration of gene-disease relationships and cross-talk mechanisms between related disorders. Through pathway statistical studies, we revealed that abnormal body weight caused by eating disorder and obesity may both be related to dysregulation of the novel joint pathway of adipocytokine signaling. In addition, this joint pathway may be the common pathway for body weight regulation in complex human diseases related to unhealthy lifestyle.

Circadian pathway genetic variation and cancer risk: evidence from genome-wide association studies.

PubMed

Mocellin, Simone; Tropea, Saveria; Benna, Clara; Rossi, Carlo Riccardo

2018-02-19

Dysfunction of the circadian clock and single polymorphisms of some circadian genes have been linked to cancer susceptibility, although data are scarce and findings inconsistent. We aimed to investigate the association between circadian pathway genetic variation and risk of developing common cancers based on the findings of genome-wide association studies (GWASs). Single nucleotide polymorphisms (SNPs) of 17 circadian genes reported by three GWAS meta-analyses dedicated to breast (Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Consortium; cases, n = 15,748; controls, n = 18,084), prostate (Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) Consortium; cases, n = 14,160; controls, n = 12,724) and lung carcinoma (Transdisciplinary Research In Cancer of the Lung (TRICL) Consortium; cases, n = 12,160; controls, n = 16,838) in patients of European ancestry were utilized to perform pathway analysis by means of the adaptive rank truncated product (ARTP) method. Data were also available for the following subgroups: estrogen receptor negative breast cancer, aggressive prostate cancer, squamous lung carcinoma and lung adenocarcinoma. We found a highly significant statistical association between circadian pathway genetic variation and the risk of breast (pathway P value = 1.9 × 10 -6 ; top gene RORA, gene P value = 0.0003), prostate (pathway P value = 4.1 × 10 -6 ; top gene ARNTL, gene P value = 0.0002) and lung cancer (pathway P value = 6.9 × 10 -7 ; top gene RORA, gene P value = 2.0 × 10 -6 ), as well as all their subgroups. Out of 17 genes investigated, 15 were found to be significantly associated with the risk of cancer: four genes were shared by all three malignancies (ARNTL, CLOCK, RORA and RORB), two by breast and lung cancer (CRY1 and CRY2) and three by prostate and lung cancer (NPAS2, NR1D1 and PER3), whereas four genes were specific for lung cancer (ARNTL2, CSNK1E, NR1D2 and PER2) and two for breast cancer (PER1, RORC). Our findings, based on the largest series ever utilized for ARTP-based gene and pathway analysis, support the hypothesis that circadian pathway genetic variation is involved in cancer predisposition.

Transcriptomic analysis of Siberian ginseng (Eleutherococcus senticosus) to discover genes involved in saponin biosynthesis.

PubMed

Hwang, Hwan-Su; Lee, Hyoshin; Choi, Yong Eui

2015-03-14

Eleutherococcus senticosus, Siberian ginseng, is a highly valued woody medicinal plant belonging to the family Araliaceae. E. senticosus produces a rich variety of saponins such as oleanane-type, noroleanane-type, 29-hydroxyoleanan-type, and lupane-type saponins. Genomic or transcriptomic approaches have not been used to investigate the saponin biosynthetic pathway in this plant. In this study, de novo sequencing was performed to select candidate genes involved in the saponin biosynthetic pathway. A half-plate 454 pyrosequencing run produced 627,923 high-quality reads with an average sequence length of 422 bases. De novo assembly generated 72,811 unique sequences, including 15,217 contigs and 57,594 singletons. Approximately 48,300 (66.3%) unique sequences were annotated using BLAST similarity searches. All of the mevalonate pathway genes for saponin biosynthesis starting from acetyl-CoA were isolated. Moreover, 206 reads of cytochrome P450 (CYP) and 145 reads of uridine diphosphate glycosyltransferase (UGT) sequences were isolated. Based on methyl jasmonate (MeJA) treatment and real-time PCR (qPCR) analysis, 3 CYPs and 3 UGTs were finally selected as candidate genes involved in the saponin biosynthetic pathway. The identified sequences associated with saponin biosynthesis will facilitate the study of the functional genomics of saponin biosynthesis and genetic engineering of E. senticosus.

Field protocol and GIS analysis of connectivity in semiarid headwaters: metrics and evidences from Carcavo Basin (SE Spain)

NASA Astrophysics Data System (ADS)

Marchamalo, Miguel; Hooke, Janet; Gonzalez-Rodrigo, Beatriz; Sandercock, Peter

2017-04-01

Soil erosion and land degradation are severe problems in headwaters of ephemeral streams in semiarid Mediterranean regions, particularly in marginal upland areas over erodible parent material. Field-based information is required about the main pathways of sediment movement, the identification of sources and sinks and the influence of relevant factors. The EU-funded project RECONDES approached this reality by monitoring connectivity pathways of water and sediment movement in the landscape with the aim of identifying hotspots that could then be strategically targeted to reduce soil erosion and off-site effects. A protocol including field work and GIS analysis was developed and applied to a set of microcatchments in Carcavo Basin (Spain). The philosophy of the protocol was based on the repeated mapping after rainfall events so that frequency of activity of pathways could be evaluated. Connectivity was evaluated for each site and event using specific metrics: maximum mapped connectivity (corresponding to the largest recorded event), density of connected pathway links (m/ha) and frequency of activity (times active/total). Repeated connectivity mapping allowed identifying hotspots of erosion. The effect of structural and functional factors on connectivity was investigated. Field data is also valuable for validating future connectivity models in semiarid landscapes under highly variable and unpredictable conditions.

Biosynthetic Pathway for the Epipolythiodioxopiperazine Acetylaranotin in Aspergillus terreus Revealed by Genome-based Deletion Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Chun-Jun; Yeh, Hsu-Hua; Chiang, Yi Ming

2013-04-15

Abstract Epipolythiodioxopiperazines (ETPs) are a class of fungal secondary metabolites derived from cyclic peptides. Acetylaranotin belongs to one structural subgroup of ETPs characterized by the presence of a seven-membered dihydrooxepine ring. Defining the genes involved in acetylaranotin biosynthesis should provide a means to increase production of these compounds and facilitate the engineering of second-generation molecules. The filamentous fungus Aspergillus terreus produces acetylaranotin and related natural products. Using targeted gene deletions, we have identified a cluster of 9 genes including one nonribosomal peptide synthase gene, ataP, that is required for acetylaranotin biosynthesis. Chemical analysis of the wild type and mutant strainsmore » enabled us to isolate seventeen natural products that are either intermediates in the normal biosynthetic pathway or shunt products that are produced when the pathway is interrupted through mutation. Nine of the compounds identified in this study are novel natural products. Our data allow us to propose a complete biosynthetic pathway for acetylaranotin and related natural products.« less

Pathway profiles based on gene-set enrichment analysis in the honey bee Apis mellifera under brood rearing-suppressed conditions.

PubMed

Kim, Kyungmun; Kim, Ju Hyeon; Kim, Young Ho; Hong, Seong-Eui; Lee, Si Hyeock

2018-01-01

Perturbation of normal behaviors in honey bee colonies by any external factor can immediately reduce the colony's capacity for brood rearing, which can eventually lead to colony collapse. To investigate the effects of brood-rearing suppression on the biology of honey bee workers, gene-set enrichment analysis of the transcriptomes of worker bees with or without suppressed brood rearing was performed. When brood rearing was suppressed, pathways associated with both protein degradation and synthesis were simultaneously over-represented in both nurses and foragers, and their overall pathway representation profiles resembled those of normal foragers and nurses, respectively. Thus, obstruction of normal labor induced over-representation in pathways related with reshaping of worker bee physiology, suggesting that transition of labor is physiologically reversible. In addition, some genes associated with the regulation of neuronal excitability, cellular and nutritional stress and aggressiveness were over-expressed under brood rearing suppression perhaps to manage in-hive stress under unfavorable conditions. Copyright © 2017 Elsevier Inc. All rights reserved.

SILAC-Based Quantitative Proteomic Analysis of Human Lung Cell Response to Copper Oxide Nanoparticles

PubMed Central

Edelmann, Mariola J.; Shack, Leslie A.; Naske, Caitlin D.; Walters, Keisha B.; Nanduri, Bindu

2014-01-01

Copper (II) oxide (CuO) nanoparticles (NP) are widely used in industry and medicine. In our study we evaluated the response of BEAS-2B human lung cells to CuO NP, using Stable isotope labeling by amino acids in cell culture (SILAC)-based proteomics and phosphoproteomics. Pathway modeling of the protein differential expression showed that CuO NP affect proteins relevant in cellular function and maintenance, protein synthesis, cell death and survival, cell cycle and cell morphology. Some of the signaling pathways represented by BEAS-2B proteins responsive to the NP included mTOR signaling, protein ubiquitination pathway, actin cytoskeleton signaling and epithelial adherens junction signaling. Follow-up experiments showed that CuO NP altered actin cytoskeleton, protein phosphorylation and protein ubiquitination level. PMID:25470785

Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA.

PubMed

Yin, Li; Cai, Zhihui; Zhu, Baoan; Xu, Cunshuan

2018-02-14

Hepatocellular carcinoma (HCC) is a devastating disease worldwide. Though many efforts have been made to elucidate the process of HCC, its molecular mechanisms of development remain elusive due to its complexity. To explore the stepwise carcinogenic process from pre-neoplastic lesions to the end stage of HCC, we employed weighted gene co-expression network analysis (WGCNA) which has been proved to be an effective method in many diseases to detect co-expressed modules and hub genes using eight pathological stages including normal, cirrhosis without HCC, cirrhosis, low-grade dysplastic, high-grade dysplastic, very early and early, advanced HCC and very advanced HCC. Among the eight consecutive pathological stages, five representative modules are selected to perform canonical pathway enrichment and upstream regulator analysis by using ingenuity pathway analysis (IPA) software. We found that cell cycle related biological processes were activated at four neoplastic stages, and the degree of activation of the cell cycle corresponded to the deterioration degree of HCC. The orange and yellow modules enriched in energy metabolism, especially oxidative metabolism, and the expression value of the genes decreased only at four neoplastic stages. The brown module, enriched in protein ubiquitination and ephrin receptor signaling pathways, correlated mainly with the very early stage of HCC. The darkred module, enriched in hepatic fibrosis/hepatic stellate cell activation, correlated with the cirrhotic stage only. The high degree hub genes were identified based on the protein-protein interaction (PPI) network and were verified by Kaplan-Meier survival analysis. The novel five high degree hub genes signature that was identified in our study may shed light on future prognostic and therapeutic approaches. Our study brings a new perspective to the understanding of the key pathways and genes in the dynamic changes of HCC progression. These findings shed light on further investigations.

Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

PubMed Central

2010-01-01

Background Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities. Methods We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform. Results Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion. Conclusions This profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention. PMID:20860831

Transcriptome Analysis of Barbarea vulgaris Infested with Diamondback Moth (Plutella xylostella) Larvae

PubMed Central

Shen, Di; Wang, Haiping; Wu, Qingjun; Lu, Peng; Qiu, Yang; Song, Jiangping; Zhang, Youjun; Li, Xixiang

2013-01-01

Background The diamondback moth (DBM, Plutella xylostella) is a crucifer-specific pest that causes significant crop losses worldwide. Barbarea vulgaris (Brassicaceae) can resist DBM and other herbivorous insects by producing feeding-deterrent triterpenoid saponins. Plant breeders have long aimed to transfer this insect resistance to other crops. However, a lack of knowledge on the biosynthetic pathways and regulatory networks of these insecticidal saponins has hindered their practical application. A pyrosequencing-based transcriptome analysis of B. vulgaris during DBM larval feeding was performed to identify genes and gene networks responsible for saponin biosynthesis and its regulation at the genome level. Principal Findings Approximately 1.22, 1.19, 1.16, 1.23, 1.16, 1.20, and 2.39 giga base pairs of clean nucleotides were generated from B. vulgaris transcriptomes sampled 1, 4, 8, 12, 24, and 48 h after onset of P. xylostella feeding and from non-inoculated controls, respectively. De novo assembly using all data of the seven transcriptomes generated 39,531 unigenes. A total of 37,780 (95.57%) unigenes were annotated, 14,399 of which were assigned to one or more gene ontology terms and 19,620 of which were assigned to 126 known pathways. Expression profiles revealed 2,016–4,685 up-regulated and 557–5188 down-regulated transcripts. Secondary metabolic pathways, such as those of terpenoids, glucosinolates, and phenylpropanoids, and its related regulators were elevated. Candidate genes for the triterpene saponin pathway were found in the transcriptome. Orthological analysis of the transcriptome with four other crucifer transcriptomes identified 592 B. vulgaris-specific gene families with a P-value cutoff of 1e−5. Conclusion This study presents the first comprehensive transcriptome analysis of B. vulgaris subjected to a series of DBM feedings. The biosynthetic and regulatory pathways of triterpenoid saponins and other DBM deterrent metabolites in this plant were classified. The results of this study will provide useful data for future investigations on pest-resistance phytochemistry and plant breeding. PMID:23696897

Computational analysis of liquid chromatography-tandem mass spectrometric steroid profiling in NCI H295R cells following angiotensin II, forskolin and abiraterone treatment.

PubMed

Mangelis, Anastasios; Dieterich, Peter; Peitzsch, Mirko; Richter, Susan; Jühlen, Ramona; Hübner, Angela; Willenberg, Holger S; Deussen, Andreas; Lenders, Jacques W M; Eisenhofer, Graeme

2016-01-01

Adrenal steroid hormones, which regulate a plethora of physiological functions, are produced via tightly controlled pathways. Investigations of these pathways, based on experimental data, can be facilitated by computational modeling for calculations of metabolic rate alterations. We therefore used a model system, based on mass balance and mass reaction equations, to kinetically evaluate adrenal steroidogenesis in human adrenal cortex-derived NCI H295R cells. For this purpose a panel of 10 steroids was measured by liquid chromatographic-tandem mass spectrometry. Time-dependent changes in cell incubate concentrations of steroids - including cortisol, aldosterone, dehydroepiandrosterone and their precursors - were measured after incubation with angiotensin II, forskolin and abiraterone. Model parameters were estimated based on experimental data using weighted least square fitting. Time-dependent angiotensin II- and forskolin-induced changes were observed for incubate concentrations of precursor steroids with peaks that preceded maximal increases in aldosterone and cortisol. Inhibition of 17-alpha-hydroxylase/17,20-lyase with abiraterone resulted in increases in upstream precursor steroids and decreases in downstream products. Derived model parameters, including rate constants of enzymatic processes, appropriately quantified observed and expected changes in metabolic pathways at multiple conversion steps. Our data demonstrate limitations of single time point measurements and the importance of assessing pathway dynamics in studies of adrenal cortical cell line steroidogenesis. Our analysis provides a framework for evaluation of steroidogenesis in adrenal cortical cell culture systems and demonstrates that computational modeling-derived estimates of kinetic parameters are an effective tool for describing perturbations in associated metabolic pathways. Copyright © 2015 Elsevier Ltd. All rights reserved.

Identification and quantitative analysis of cellular proteins affected by treatment with withaferin a using a SILAC-based proteomics approach.

PubMed

Narayan, Malathi; Seeley, Kent W; Jinwal, Umesh K

2015-12-04

Withaferin A (WA) is a major bioactive compound isolated from the medicinal plant Withania somnifera Dunal, also known as "Ashwagandha". A number of published reports suggest various uses for WA including its function as an anti-inflammatory and anti-angiogenic drug molecule. The effects of WA at the molecular level in a cellular environment are not well understood. Knowledge of the molecular mechanism of action of WA could enhance its therapeutic value and may reveal novel pathways it may modulate. In order to identify and characterize proteins affected by treatment with WA, we used SILAC- based proteomics analysis on a mouse microglial cell line (N9), which replicates phenotypic characteristics of primary microglial cells. Using stable isotope labeling of amino acids in cell culture (SILAC) and mass spectrometry (MS), a total of 2300 unique protein groups were identified from three biological replicates, with significant expression changes in 32 non-redundant proteins. The top biological functions associated with these differentially expressed proteins include cell death and survival, free radical scavenging, and carbohydrate metabolism. Specifically, several heat shock proteins (Hsps) were found to be upregulated, which suggests that the chaperonic machinery might be regulated by WA. Furthermore, our study revealed several novel protein molecules that were not previously reported to be affected by WA. Among them, annexin A1, a key anti-inflammatory molecule in microglial cells was found to be downregulated. Hsc70, Hsp90α and Hsp105 were found to be upregulated. We also found sequestosome1/p62 (p62) to be upregulated. We performed Ingenuity Pathway Analysis (IPA) and found a number of pathways that were affected by WA treatment. SILAC-based proteomics analysis of a microglial cell model revealed several novel proteins whose expression is regulated by WA and probable pathways regulated by WA. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Image-based compound profiling reveals a dual inhibitor of tyrosine kinase and microtubule polymerization.

PubMed

Tanabe, Kenji

2016-04-27

Small-molecule compounds are widely used as biological research tools and therapeutic drugs. Therefore, uncovering novel targets of these compounds should provide insights that are valuable in both basic and clinical studies. I developed a method for image-based compound profiling by quantitating the effects of compounds on signal transduction and vesicle trafficking of epidermal growth factor receptor (EGFR). Using six signal transduction molecules and two markers of vesicle trafficking, 570 image features were obtained and subjected to multivariate analysis. Fourteen compounds that affected EGFR or its pathways were classified into four clusters, based on their phenotypic features. Surprisingly, one EGFR inhibitor (CAS 879127-07-8) was classified into the same cluster as nocodazole, a microtubule depolymerizer. In fact, this compound directly depolymerized microtubules. These results indicate that CAS 879127-07-8 could be used as a chemical probe to investigate both the EGFR pathway and microtubule dynamics. The image-based multivariate analysis developed herein has potential as a powerful tool for discovering unexpected drug properties.

Analysis of Geothermal Pathway in the Metamorphic Area, Northeastern Taiwan

NASA Astrophysics Data System (ADS)

Wang, C.; Wu, M. Y.; Song, S. R.; Lo, W.

2016-12-01

A quantitative measure by play fairway analysis in geothermal energy development is an important tool that can present the probability map of potential resources through the uncertainty studies in geology for early phase decision making purpose in the related industries. While source, pathway, and fluid are the three main geologic factors in traditional geothermal systems, identifying the heat paths is critical to reduce drilling cost. Taiwan is in East Asia and the western edge of Pacific Ocean, locating on the convergent boundary of Eurasian Plate and Philippine Sea Plate with many earthquake activities. This study chooses a metamorphic area in the western corner of Yi-Lan plain in northeastern Taiwan with high geothermal potential and several existing exploration sites. Having high subsurface temperature gradient from the mountain belts, and plenty hydrologic systems through thousands of millimeters annual precipitation that would bring up heats closer to the surface, current geothermal conceptual model indicates the importance of pathway distribution which affects the possible concentration of extractable heat location. The study conducts surface lineation analysis using analytic hierarchy process to determine weights among various fracture types for their roles in geothermal pathways, based on the information of remote sensing data, published geologic maps and field work measurements, to produce regional fracture distribution probability map. The results display how the spatial distribution of pathways through various fractures could affect geothermal systems, identify the geothermal plays using statistical data analysis, and compare against the existing drilling data.

Pathways to Healing: Person-centered Responses to Complementary Services

PubMed Central

Bertrand, Sharon W.; Fermon, Barbara; Coleman, Julie Foley

2014-01-01

Objectives: This research study assessed perceived changes in quality-of-life measures related to participation in complementary services consisting of a variety of nontraditional therapies and/or programs at Pathways: A Health Crisis Resource Center in Minneapolis, Minnesota. Design: Survey data were used to assess perceived changes participants ascribed to their experience with complementary services at Pathways. Quantitative data analysis was conducted using participant demographics together with participant ratings of items from the “Self-Assessment of Change” (SAC) measure developed at the University of Arizona, Tucson. Qualitative data analysis was conducted on written responses to an additional survey question: “To what extent has your participation at Pathways influenced your healing process?” Setting/Location: Pathways offers a variety of services, including one-to-one sessions using nontraditional healing therapies, support groups, educational classes, and practice groups such as yoga and meditation for those facing serious health challenges. These services are offered free of charge through community financial support using volunteer practitioners. Participants: People (126) diagnosed with serious health challenges who used Pathways services from 2007 through 2009. Interventions: Participation in self-selected Pathways services. Measures: Responses to items on the SAC measure plus written responses to the question, “To what extent has your participation at Pathways influenced your healing process?” Results: Quantitative findings: Participants reported experiencing significant changes across all components of the SAC measure. Qualitative findings: Responses to the open-ended survey question identified perspectives on the culture of Pathways and a shift in participants' perceptions of well-being based on their experience of Pathways services. Conclusions: Participation in services provided by the Pathways organization improved perceptions of quality of life and well-being and led to more active involvement in the experience of a healing process. PMID:24753990

Ethylene Inhibits Cell Proliferation of the Arabidopsis Root Meristem1[OPEN

PubMed Central

Street, Ian H.; Aman, Sitwat; Zubo, Yan; Ramzan, Aleena; Wang, Xiaomin; Shakeel, Samina N.; Kieber, Joseph J.; Schaller, G. Eric

2015-01-01

The root system of plants plays a critical role in plant growth and survival, with root growth being dependent on both cell proliferation and cell elongation. Multiple phytohormones interact to control root growth, including ethylene, which is primarily known for its role in controlling root cell elongation. We find that ethylene also negatively regulates cell proliferation at the root meristem of Arabidopsis (Arabidopsis thaliana). Genetic analysis indicates that the inhibition of cell proliferation involves two pathways operating downstream of the ethylene receptors. The major pathway is the canonical ethylene signal transduction pathway that incorporates CONSTITUTIVE TRIPLE RESPONSE1, ETHYLENE INSENSITIVE2, and the ETHYLENE INSENSITIVE3 family of transcription factors. The secondary pathway is a phosphorelay based on genetic analysis of receptor histidine kinase activity and mutants involving the type B response regulators. Analysis of ethylene-dependent gene expression and genetic analysis supports SHORT HYPOCOTYL2, a repressor of auxin signaling, as one mediator of the ethylene response and furthermore, indicates that SHORT HYPOCOTYL2 is a point of convergence for both ethylene and cytokinin in negatively regulating cell proliferation. Additional analysis indicates that ethylene signaling contributes but is not required for cytokinin to inhibit activity of the root meristem. These results identify key elements, along with points of cross talk with cytokinin and auxin, by which ethylene negatively regulates cell proliferation at the root apical meristem. PMID:26149574

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis.

PubMed

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-11-07

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s).

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis

PubMed Central

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-01-01

Background To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. Results We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. Conclusion PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s). PMID:14604444

Functional Analysis of RNA Interference-Related Soybean Pod Borer (Lepidoptera) Genes Based on Transcriptome Sequences.

PubMed

Meng, Fanli; Yang, Mingyu; Li, Yang; Li, Tianyu; Liu, Xinxin; Wang, Guoyue; Wang, Zhanchun; Jin, Xianhao; Li, Wenbin

2018-01-01

RNA interference (RNAi) is useful for controlling pests of agriculturally important crops. The soybean pod borer (SPB) is the most important soybean pest in Northeastern Asia. In an earlier study, we confirmed that the SPB could be controlled via transgenic plant-mediated RNAi. Here, the SPB transcriptome was sequenced to identify RNAi-related genes, and also to establish an RNAi-of-RNAi assay system for evaluating genes involved in the SPB systemic RNAi response. The core RNAi genes, as well as genes potentially involved in double-stranded RNA (dsRNA) uptake were identified based on SPB transcriptome sequences. A phylogenetic analysis and the characterization of these core components as well as dsRNA uptake related genes revealed that they contain conserved domains essential for the RNAi pathway. The results of the RNAi-of-RNAi assay involving Laccas e 2 (a critical cuticle pigmentation gene) as a marker showed that genes encoding the sid-like ( Sil1 ), scavenger receptor class C ( Src ), and scavenger receptor class B ( Srb3 and Srb4 ) proteins of the endocytic pathway were required for SPB cellular uptake of dsRNA. The SPB response was inferred to contain three functional small RNA pathways (i.e., miRNA, siRNA, and piRNA pathways). Additionally, the SPB systemic RNA response may rely on systemic RNA interference deficient transmembrane channel-mediated and receptor-mediated endocytic pathways. The results presented herein may be useful for developing RNAi-mediated methods to control SPB infestations in soybean.

Core Canonical Pathways Involved in Developing Human Glioblastoma Multiforme (GBM).

PubMed

Ghosh, Somiranjan; Dutta, Sisir; Thorne, Gabriel; Boston, Ava; Barfield, Alexis; Banerjee, Narendra; Walker, Rayshawn; Banerjee, Hirendra Nath

2017-02-01

Glioblastoma multiforme (GBM) is the most common and aggressive type of the primary brain tumors with pathologic hallmarks of necrosis and vascular proliferation. The diagnosis of GBM is currently mostly based on histological examination of brain tumor tissues, after radiological characterization and surgical biopsy. The ability to characterize tumors comprehensively at the molecular level raises the possibility that diagnosis can be made based on molecular profiling with or without histological examination, rather than solely on histological phenotype. The development of novel genomic and proteomic techniques will foster in the identification of such diagnostic and prognostic molecular markers. We analyzed the global differential gene expression of a GBM cell line HTB15 in comparison to normal human Astrocytes, and established a few canonical pathways that are important in determining the molecular mechanisms of cancer using global gene expression microarray, coupled with the Ingenuity Pathway Analysis ( IPA ®). Overall, we revealed a discrete gene expression profile in the experimental model that resembled progression of GBM cancer. The canonical pathway analysis showed the involvement of genes that differentially expressed in such a disease condition that included Inositol pathway, Polo like kinases, nNOS signaling , and Tetrapyrrole biosynthesis . Our findings established that the gene expression pattern of this dreaded brain cancer will probably help the cancer research community by finding out newer therapeutic strategies to combat this dreaded cancer type that leads to the identification of high-risk population in this category, with almost hundred percent mortality rate.

Functional Analysis of RNA Interference-Related Soybean Pod Borer (Lepidoptera) Genes Based on Transcriptome Sequences

PubMed Central

Meng, Fanli; Yang, Mingyu; Li, Yang; Li, Tianyu; Liu, Xinxin; Wang, Guoyue; Wang, Zhanchun; Jin, Xianhao; Li, Wenbin

2018-01-01

RNA interference (RNAi) is useful for controlling pests of agriculturally important crops. The soybean pod borer (SPB) is the most important soybean pest in Northeastern Asia. In an earlier study, we confirmed that the SPB could be controlled via transgenic plant-mediated RNAi. Here, the SPB transcriptome was sequenced to identify RNAi-related genes, and also to establish an RNAi-of-RNAi assay system for evaluating genes involved in the SPB systemic RNAi response. The core RNAi genes, as well as genes potentially involved in double-stranded RNA (dsRNA) uptake were identified based on SPB transcriptome sequences. A phylogenetic analysis and the characterization of these core components as well as dsRNA uptake related genes revealed that they contain conserved domains essential for the RNAi pathway. The results of the RNAi-of-RNAi assay involving Laccase 2 (a critical cuticle pigmentation gene) as a marker showed that genes encoding the sid-like (Sil1), scavenger receptor class C (Src), and scavenger receptor class B (Srb3 and Srb4) proteins of the endocytic pathway were required for SPB cellular uptake of dsRNA. The SPB response was inferred to contain three functional small RNA pathways (i.e., miRNA, siRNA, and piRNA pathways). Additionally, the SPB systemic RNA response may rely on systemic RNA interference deficient transmembrane channel-mediated and receptor-mediated endocytic pathways. The results presented herein may be useful for developing RNAi-mediated methods to control SPB infestations in soybean. PMID:29773992

Gene-Based Mapping and Pathway Analysis of Metabolic Traits in Dairy Cows

PubMed Central

Ha, Ngoc-Thuy; Gross, Josef Johann; van Dorland, Annette; Tetens, Jens; Thaller, Georg; Schlather, Martin; Bruckmaier, Rupert; Simianer, Henner

2015-01-01

The metabolic adaptation of dairy cows during the transition period has been studied intensively in the last decades. However, until now, only few studies have paid attention to the genetic aspects of this process. Here, we present the results of a gene-based mapping and pathway analysis with the measurements of three key metabolites, (1) non-esterified fatty acids (NEFA), (2) beta-hydroxybutyrate (BHBA) and (3) glucose, characterizing the metabolic adaptability of dairy cows before and after calving. In contrast to the conventional single-marker approach, we identify 99 significant and biologically sensible genes associated with at least one of the considered phenotypes and thus giving evidence for a genetic basis of the metabolic adaptability. Moreover, our results strongly suggest three pathways involved in the metabolism of steroids and lipids are potential candidates for the adaptive regulation of dairy cows in their early lactation. From our perspective, a closer investigation of our findings will lead to a step forward in understanding the variability in the metabolic adaptability of dairy cows in their early lactation. PMID:25789767

Use of transcriptome sequencing to understand the pistillate flowering in hickory (Carya cathayensis Sarg.).

PubMed

Huang, You-Jun; Liu, Li-Li; Huang, Jian-Qin; Wang, Zheng-Jia; Chen, Fang-Fang; Zhang, Qi-Xiang; Zheng, Bing-Song; Chen, Ming

2013-10-10

Different from herbaceous plants, the woody plants undergo a long-period vegetative stage to achieve floral transition. They then turn into seasonal plants, flowering annually. In this study, a preliminary model of gene regulations for seasonal pistillate flowering in hickory (Carya cathayensis) was proposed. The genome-wide dynamic transcriptome was characterized via the joint-approach of RNA sequencing and microarray analysis. Differential transcript abundance analysis uncovered the dynamic transcript abundance patterns of flowering correlated genes and their major functions based on Gene Ontology (GO) analysis. To explore pistillate flowering mechanism in hickory, a comprehensive flowering gene regulatory network based on Arabidopsis thaliana was constructed by additional literature mining. A total of 114 putative flowering or floral genes including 31 with differential transcript abundance were identified in hickory. The locations, functions and dynamic transcript abundances were analyzed in the gene regulatory networks. A genome-wide co-expression network for the putative flowering or floral genes shows three flowering regulatory modules corresponding to response to light abiotic stimulus, cold stress, and reproductive development process, respectively. Totally 27 potential flowering or floral genes were recruited which are meaningful to understand the hickory specific seasonal flowering mechanism better. Flowering event of pistillate flower bud in hickory is triggered by several pathways synchronously including the photoperiod, autonomous, vernalization, gibberellin, and sucrose pathway. Totally 27 potential flowering or floral genes were recruited from the genome-wide co-expression network function module analysis. Moreover, the analysis provides a potential FLC-like gene based vernalization pathway and an 'AC' model for pistillate flower development in hickory. This work provides an available framework for pistillate flower development in hickory, which is significant for insight into regulation of flowering and floral development of woody plants.

Use of transcriptome sequencing to understand the pistillate flowering in hickory (Carya cathayensis Sarg.)

PubMed Central

2013-01-01

Background Different from herbaceous plants, the woody plants undergo a long-period vegetative stage to achieve floral transition. They then turn into seasonal plants, flowering annually. In this study, a preliminary model of gene regulations for seasonal pistillate flowering in hickory (Carya cathayensis) was proposed. The genome-wide dynamic transcriptome was characterized via the joint-approach of RNA sequencing and microarray analysis. Results Differential transcript abundance analysis uncovered the dynamic transcript abundance patterns of flowering correlated genes and their major functions based on Gene Ontology (GO) analysis. To explore pistillate flowering mechanism in hickory, a comprehensive flowering gene regulatory network based on Arabidopsis thaliana was constructed by additional literature mining. A total of 114 putative flowering or floral genes including 31 with differential transcript abundance were identified in hickory. The locations, functions and dynamic transcript abundances were analyzed in the gene regulatory networks. A genome-wide co-expression network for the putative flowering or floral genes shows three flowering regulatory modules corresponding to response to light abiotic stimulus, cold stress, and reproductive development process, respectively. Totally 27 potential flowering or floral genes were recruited which are meaningful to understand the hickory specific seasonal flowering mechanism better. Conclusions Flowering event of pistillate flower bud in hickory is triggered by several pathways synchronously including the photoperiod, autonomous, vernalization, gibberellin, and sucrose pathway. Totally 27 potential flowering or floral genes were recruited from the genome-wide co-expression network function module analysis. Moreover, the analysis provides a potential FLC-like gene based vernalization pathway and an 'AC’ model for pistillate flower development in hickory. This work provides an available framework for pistillate flower development in hickory, which is significant for insight into regulation of flowering and floral development of woody plants. PMID:24106755

Network pharmacology-based identification of key pharmacological pathways of Yin-Huang-Qing-Fei capsule acting on chronic bronchitis.

PubMed

Yu, Guohua; Zhang, Yanqiong; Ren, Weiqiong; Dong, Ling; Li, Junfang; Geng, Ya; Zhang, Yi; Li, Defeng; Xu, Haiyu; Yang, Hongjun

2017-01-01

For decades in China, the Yin-Huang-Qing-Fei capsule (YHQFC) has been widely used in the treatment of chronic bronchitis, with good curative effects. Owing to the complexity of traditional Chinese herbal formulas, the pharmacological mechanism of YHQFC remains unclear. To address this problem, a network pharmacology-based strategy was proposed in this study. At first, the putative target profile of YHQFC was predicted using MedChem Studio, based on structural and functional similarities of all available YHQFC components to the known drugs obtained from the DrugBank database. Then, an interaction network was constructed using links between putative YHQFC targets and known therapeutic targets of chronic bronchitis. Following the calculation of four topological features (degree, betweenness, closeness, and coreness) of each node in the network, 475 major putative targets of YHQFC and their topological importance were identified. In addition, a pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes pathway database indicated that the major putative targets of YHQFC are significantly associated with various pathways involved in anti-inflammation processes, immune responses, and pathological changes caused by asthma. More interestingly, eight major putative targets of YHQFC (interleukin [IL]-3, IL-4, IL-5, IL-10, IL-13, FCER1G, CCL11, and EPX) were demonstrated to be associated with the inflammatory process that occurs during the progression of asthma. Finally, a molecular docking simulation was performed and the results exhibited that 17 pairs of chemical components and candidate YHQFC targets involved in asthma pathway had strong binding efficiencies. In conclusion, this network pharmacology-based investigation revealed that YHQFC may attenuate the inflammatory reaction of chronic bronchitis by regulating its candidate targets, which may be implicated in the major pathological processes of the asthma pathway.

Network pharmacology-based identification of key pharmacological pathways of Yin–Huang–Qing–Fei capsule acting on chronic bronchitis

PubMed Central

Yu, Guohua; Zhang, Yanqiong; Ren, Weiqiong; Dong, Ling; Li, Junfang; Geng, Ya; Zhang, Yi; Li, Defeng; Xu, Haiyu; Yang, Hongjun

2017-01-01

For decades in China, the Yin–Huang–Qing–Fei capsule (YHQFC) has been widely used in the treatment of chronic bronchitis, with good curative effects. Owing to the complexity of traditional Chinese herbal formulas, the pharmacological mechanism of YHQFC remains unclear. To address this problem, a network pharmacology-based strategy was proposed in this study. At first, the putative target profile of YHQFC was predicted using MedChem Studio, based on structural and functional similarities of all available YHQFC components to the known drugs obtained from the DrugBank database. Then, an interaction network was constructed using links between putative YHQFC targets and known therapeutic targets of chronic bronchitis. Following the calculation of four topological features (degree, betweenness, closeness, and coreness) of each node in the network, 475 major putative targets of YHQFC and their topological importance were identified. In addition, a pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes pathway database indicated that the major putative targets of YHQFC are significantly associated with various pathways involved in anti-inflammation processes, immune responses, and pathological changes caused by asthma. More interestingly, eight major putative targets of YHQFC (interleukin [IL]-3, IL-4, IL-5, IL-10, IL-13, FCER1G, CCL11, and EPX) were demonstrated to be associated with the inflammatory process that occurs during the progression of asthma. Finally, a molecular docking simulation was performed and the results exhibited that 17 pairs of chemical components and candidate YHQFC targets involved in asthma pathway had strong binding efficiencies. In conclusion, this network pharmacology-based investigation revealed that YHQFC may attenuate the inflammatory reaction of chronic bronchitis by regulating its candidate targets, which may be implicated in the major pathological processes of the asthma pathway. PMID:28053519

Transcriptome analysis reveals candidate genes involved in luciferin metabolism in Luciola aquatilis (Coleoptera: Lampyridae)

PubMed Central

Vongsangnak, Wanwipa; Chumnanpuen, Pramote

2016-01-01

Bioluminescence, which living organisms such as fireflies emit light, has been studied extensively for over half a century. This intriguing reaction, having its origins in nature where glowing insects can signal things such as attraction or defense, is now widely used in biotechnology with applications of bioluminescence and chemiluminescence. Luciferase, a key enzyme in this reaction, has been well characterized; however, the enzymes involved in the biosynthetic pathway of its substrate, luciferin, remains unsolved at present. To elucidate the luciferin metabolism, we performed a de novo transcriptome analysis using larvae of the firefly species, Luciola aquatilis. Here, a comparative analysis is performed with the model coleopteran insect Tribolium casteneum to elucidate the metabolic pathways in L. aquatilis. Based on a template luciferin biosynthetic pathway, combined with a range of protein and pathway databases, and various prediction tools for functional annotation, the candidate genes, enzymes, and biochemical reactions involved in luciferin metabolism are proposed for L. aquatilis. The candidate gene expression is validated in the adult L. aquatilis using reverse transcription PCR (RT-PCR). This study provides useful information on the bio-production of luciferin in the firefly and will benefit to future applications of the valuable firefly bioluminescence system. PMID:27761329

Radiogenomics: a systems biology approach to understanding genetic risk factors for radiotherapy toxicity ?

PubMed Central

Herskind, Carsten; Talbot, Christopher J.; Kerns, Sarah L.; Veldwijk, Marlon R.; Rosenstein, Barry S.; West, Catharine M. L.

2016-01-01

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review ‘omics’ approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different ‘omics’ approaches may be more efficient in identifying critical pathways than pathway analysis based on single ‘omics’ data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterized by different mechanisms. Thus ‘omics’ and functional approaches may synergize if they are integrated into radiogenomics ‘systems biology’ to facilitate the goal of individualised radiotherapy. PMID:26944314

Characterizing biomarkers in osteosarcoma metastasis based on an ego-network.

PubMed

Liu, Zhen; Song, Yan

2017-06-01

To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network. From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway. These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.

Spectrally Resolved Fiber Photometry for Multi-component Analysis of Brain Circuits.

PubMed

Meng, Chengbo; Zhou, Jingheng; Papaneri, Amy; Peddada, Teja; Xu, Karen; Cui, Guohong

2018-04-25

To achieve simultaneous measurement of multiple cellular events in molecularly defined groups of neurons in vivo, we designed a spectrometer-based fiber photometry system that allows for spectral unmixing of multiple fluorescence signals recorded from deep brain structures in behaving animals. Using green and red Ca 2+ indicators differentially expressed in striatal direct- and indirect-pathway neurons, we were able to simultaneously monitor the neural activity in these two pathways in freely moving animals. We found that the activities were highly synchronized between the direct and indirect pathways within one hemisphere and were desynchronized between the two hemispheres. We further analyzed the relationship between the movement patterns and the magnitude of activation in direct- and indirect-pathway neurons and found that the striatal direct and indirect pathways coordinately control the dynamics and fate of movement. Published by Elsevier Inc.

Variant allele frequency enrichment analysis in vitro reveals sonic hedgehog pathway to impede sustained temozolomide response in GBM.

PubMed

Biswas, Nidhan K; Chandra, Vikas; Sarkar-Roy, Neeta; Das, Tapojyoti; Bhattacharya, Rabindra N; Tripathy, Laxmi N; Basu, Sunandan K; Kumar, Shantanu; Das, Subrata; Chatterjee, Ankita; Mukherjee, Ankur; Basu, Pryiadarshi; Maitra, Arindam; Chattopadhyay, Ansuman; Basu, Analabha; Dhara, Surajit

2015-01-21

Neoplastic cells of Glioblastoma multiforme (GBM) may or may not show sustained response to temozolomide (TMZ) chemotherapy. We hypothesize that TMZ chemotherapy response in GBM is predetermined in its neoplastic clones via a specific set of mutations that alter relevant pathways. We describe exome-wide enrichment of variant allele frequencies (VAFs) in neurospheres displaying contrasting phenotypes of sustained versus reversible TMZ-responses in vitro. Enrichment of VAFs was found on genes ST5, RP6KA1 and PRKDC in cells showing sustained TMZ-effect whereas on genes FREM2, AASDH and STK36, in cells showing reversible TMZ-effect. Ingenuity pathway analysis (IPA) revealed that these genes alter cell-cycle, G2/M-checkpoint-regulation and NHEJ pathways in sustained TMZ-effect cells whereas the lysine-II&V/phenylalanine degradation and sonic hedgehog (Hh) pathways in reversible TMZ-effect cells. Next, we validated the likely involvement of the Hh-pathway in TMZ-response on additional GBM neurospheres as well as on GBM patients, by extracting RNA-sequencing-based gene expression data from the TCGA-GBM database. Finally, we demonstrated TMZ-sensitization of a TMZ non-responder neurosphere in vitro by treating them with the FDA-approved pharmacological Hh-pathway inhibitor vismodegib. Altogether, our results indicate that the Hh-pathway impedes sustained TMZ-response in GBM and could be a potential therapeutic target to enhance TMZ-response in this malignancy.

Transcriptomic basis for drought-resistance in Brassica napus L.

NASA Astrophysics Data System (ADS)

Wang, Pei; Yang, Cuiling; Chen, Hao; Song, Chunpeng; Zhang, Xiao; Wang, Daojie

2017-01-01

Based on transcriptomic data from four experimental settings with drought-resistant and drought-sensitive cultivars under drought and well-watered conditions, statistical analysis revealed three categories encompassing 169 highly differentially expressed genes (DEGs) in response to drought in Brassica napus L., including 37 drought-resistant cultivar-related genes, 35 drought-sensitive cultivar-related genes and 97 cultivar non-specific ones. We provide evidence that the identified DEGs were fairly uniformly distributed on different chromosomes and their expression patterns are variety specific. Except commonly enriched in response to various stimuli or stresses, different categories of DEGs show specific enrichment in certain biological processes or pathways, which indicated the possibility of functional differences among the three categories. Network analysis revealed relationships among the 169 DEGs, annotated biological processes and pathways. The 169 DEGs can be classified into different functional categories via preferred pathways or biological processes. Some pathways might simultaneously involve a large number of shared DEGs, and these pathways are likely to cross-talk and have overlapping biological functions. Several members of the identified DEGs fit to drought stress signal transduction pathway in Arabidopsis thaliana. Finally, quantitative real-time PCR validations confirmed the reproducibility of the RNA-seq data. These investigations are profitable for the improvement of crop varieties through transgenic engineering.

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

PubMed Central

Chen, Lei; Lu, Jing; Kong, XiangYin; Huang, Tao; Li, HaiPeng

2016-01-01

A drug’s biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives. PMID:27780226

Nanocrystalline Si pathway induced unipolar resistive switching behavior from annealed Si-rich SiNx/SiNy multilayers

NASA Astrophysics Data System (ADS)

Jiang, Xiaofan; Ma, Zhongyuan; Yang, Huafeng; Yu, Jie; Wang, Wen; Zhang, Wenping; Li, Wei; Xu, Jun; Xu, Ling; Chen, Kunji; Huang, Xinfan; Feng, Duan

2014-09-01

Adding a resistive switching functionality to a silicon microelectronic chip is a new challenge in materials research. Here, we demonstrate that unipolar and electrode-independent resistive switching effects can be realized in the annealed Si-rich SiNx/SiNy multilayers with high on/off ratio of 109. High resolution transmission electron microscopy reveals that for the high resistance state broken pathways composed of discrete nanocrystalline silicon (nc-Si) exist in the Si nitride multilayers. While for the low resistance state the discrete nc-Si regions is connected, forming continuous nc-Si pathways. Based on the analysis of the temperature dependent I-V characteristics and HRTEM photos, we found that the break-and-bridge evolution of nc-Si pathway is the origin of resistive switching memory behavior. Our findings provide insights into the mechanism of the resistive switching behavior in nc-Si films, opening a way for it to be utilized as a material in Si-based memories.

Co-factors Required for TLR7- and TLR9- dependent Innate Immune Responses

PubMed Central

Chiang, Chih-yuan; Engel, Alex; Opaluch, Amanda M.; Ramos, Irene; Maestre, Ana M.; Secundino, Ismael; De Jesus, Paul D.; Nguyen, Quy T.; Welch, Genevieve; Bonamy, Ghislain M.C.; Miraglia, Loren J.; Orth, Anthony P.; Nizet, Victor; Fernandez-Sesma, Ana; Zhou, Yingyao; Barton, Gregory M.; Chanda, Sumit K.

2012-01-01

SUMMARY Pathogens commonly utilize endocytic pathways to gain cellular access. The endosomal pattern recognition receptors TLR7 and TLR9 detect pathogen-encoded nucleic acids to initiate MyD88-dependent pro-inflammatory responses to microbial infection. Using genome-wide RNAi screening and integrative systems-based analysis we identify 190 co-factors required for TLR7- and TLR9-directed signaling responses. A set of co-factors were cross-profiled for their activities downstream of several immunoreceptors, and then functionally mapped based on the known architecture of NF-κB signaling pathways. Protein complexes and pathways involved in ubiquitin-protein ligase activities, sphingolipid metabolism, chromatin modifications, and ancient stress responses were found to modulate innate recognition of endosomal nucleic acids. Additionally, hepatocyte growth factor-regulated tyrosine kinase substrate (HRS) was characterized as necessary for ubiquitin-dependent TLR9 targeting to the endolysosome. Proteins and pathways identified here should prove useful in delineating strategies to manipulate innate responses for treatment of autoimmune disorders and microbial infection. PMID:22423970

Stress transgenerationally programs metabolic pathways linked to altered mental health.

PubMed

Kiss, Douglas; Ambeskovic, Mirela; Montina, Tony; Metz, Gerlinde A S

2016-12-01

Stress is among the primary causes of mental health disorders, which are the most common reason for disability worldwide. The ubiquity of these disorders, and the costs associated with them, lends a sense of urgency to the efforts to improve prediction and prevention. Down-stream metabolic changes are highly feasible and accessible indicators of pathophysiological processes underlying mental health disorders. Here, we show that remote and cumulative ancestral stress programs central metabolic pathways linked to mental health disorders. The studies used a rat model consisting of a multigenerational stress lineage (the great-great-grandmother and each subsequent generation experienced stress during pregnancy) and a transgenerational stress lineage (only the great-great-grandmother was stressed during pregnancy). Urine samples were collected from adult male F4 offspring and analyzed using 1 H NMR spectroscopy. The results of variable importance analysis based on random variable combination were used for unsupervised multivariate principal component analysis and hierarchical clustering analysis, as well as metabolite set enrichment analysis (MSEA) and pathway analysis. We identified distinct metabolic profiles associated with the multigenerational and transgenerational stress phenotype, with consistent upregulation of hippurate and downregulation of tyrosine, threonine, and histamine. MSEA and pathway analysis showed that these metabolites are involved in catecholamine biosynthesis, immune responses, and microbial host interactions. The identification of metabolic signatures linked to ancestral programming assists in the discovery of gene targets for future studies of epigenetic regulation in pathogenic processes. Ultimately, this research can lead to biomarker discovery for better prediction and prevention of mental health disorders.

Derivation of Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Teleost Reproductive Axis.

EPA Science Inventory

Oligonucleotide microarrays are a powerful tool for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-based analyses to detect diffe...

Transcriptional pathway and de novo network-based approaches to effects-based monitoring in the Great Lakes

EPA Science Inventory

Transcriptomics provides unique solutions for understanding the impact of complex mixtures and their components on aquatic systems. Here we describe the application of transcriptomics analysis of in situ fathead minnow exposures for assessing biological impacts of wastewater trea...

Plasma metabolic profiling analysis of nephrotoxicity induced by acyclovir using metabonomics coupled with multivariate data analysis.

PubMed

Zhang, Xiuxiu; Li, Yubo; Zhou, Huifang; Fan, Simiao; Zhang, Zhenzhu; Wang, Lei; Zhang, Yanjun

2014-08-01

Acyclovir (ACV) is an antiviral agent. However, its use is limited by adverse side effect, particularly by its nephrotoxicity. Metabonomics technology can provide essential information on the metabolic profiles of biofluids and organs upon drug administration. Therefore, in this study, mass spectrometry-based metabonomics coupled with multivariate data analysis was used to identify the plasma metabolites and metabolic pathways related to nephrotoxicity caused by intraperitoneal injection of low (50mg/kg) and high (100mg/kg) doses of acyclovir. Sixteen biomarkers were identified by metabonomics and nephrotoxicity results revealed the dose-dependent effect of acyclovir on kidney tissues. The present study showed that the top four metabolic pathways interrupted by acyclovir included the metabolisms of arachidonic acid, tryptophan, arginine and proline, and glycerophospholipid. This research proves the established metabonomic approach can provide information on changes in metabolites and metabolic pathways, which can be applied to in-depth research on the mechanism of acyclovir-induced kidney injury. Copyright © 2014 Elsevier B.V. All rights reserved.

Electronic Implementation of Integrated End-of-life Care: A Local Approach

PubMed Central

Schlieper, Daniel; Altreuther, Christiane; Schallenburger, Manuela; Neukirchen, Martin; Schmitz, Andrea

2017-01-01

Introduction: The Liverpool Care Pathway for the Dying Patient is an instrument to deliver integrated care for patients in their last hours of life. Originally a paper-based system, this study investigates the feasibility of an electronic version. Methods: An electronic Liverpool Care Pathway was implemented in a specialized palliative care unit of a German university hospital. Its use is exemplified by means of auditing and analysis of the proportion of recorded items. Results: In the years 2013 and 2014 the electronic Liverpool Care Pathway was used for the care of 159 patients. The uptake of the instrument was high (67%). Most items were recorded. Apart from a high usability, the fast data retrieval allows fast analysis for auditing and research. Conclusions and discussion: The electronic instrument is feasible in a computerized ward and has strong advantages for retrospective analysis. Trial registration: Internal Clinical Trial Register of the Medical Faculty, Heinrich Heine University Düsseldorf, No. 2015124683 (7 December 2015). PMID:28970746

Dissociative adsorption of O2 on unreconstructed metal (100) surfaces: Pathways, energetics, and sticking kinetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Da-Jiang; Evans, James W.

An accurate description of oxygen dissociation pathways and kinetics for various local adlayer environments is key for an understanding not just of the coverage dependence of oxygen sticking, but also of reactive steady states in oxidation reactions. Density functional theory analysis for M(100) surfaces with M=Pd, Rh, and Ni, where O prefers the fourfold hollow adsorption site, does not support the traditional Brundle-Behm-Barker picture of dissociative adsorption onto second-nearest-neighbor hollow sites with an additional blocking constraint. Rather adsorption via neighboring vicinal bridge sites dominates, although other pathways can be active. The same conclusion also applies for M=Pt and Ir, wheremore » oxygen prefers the bridge adsorption site. Statistical mechanical analysis is performed based on kinetic Monte Carlo simulation of a multisite lattice-gas model consistent with our revised picture of adsorption. This analysis determines the coverage and temperature dependence of sticking for a realistic treatment of the oxygen adlayer structure.« less

Drug-Path: a database for drug-induced pathways

PubMed Central

Zeng, Hui; Cui, Qinghua

2015-01-01

Some databases for drug-associated pathways have been built and are publicly available. However, the pathways curated in most of these databases are drug-action or drug-metabolism pathways. In recent years, high-throughput technologies such as microarray and RNA-sequencing have produced lots of drug-induced gene expression profiles. Interestingly, drug-induced gene expression profile frequently show distinct patterns, indicating that drugs normally induce the activation or repression of distinct pathways. Therefore, these pathways contribute to study the mechanisms of drugs and drug-repurposing. Here, we present Drug-Path, a database of drug-induced pathways, which was generated by KEGG pathway enrichment analysis for drug-induced upregulated genes and downregulated genes based on drug-induced gene expression datasets in Connectivity Map. Drug-Path provides user-friendly interfaces to retrieve, visualize and download the drug-induced pathway data in the database. In addition, the genes deregulated by a given drug are highlighted in the pathways. All data were organized using SQLite. The web site was implemented using Django, a Python web framework. Finally, we believe that this database will be useful for related researches. Database URL: http://www.cuilab.cn/drugpath PMID:26130661

Drug-Path: a database for drug-induced pathways.

PubMed

Zeng, Hui; Qiu, Chengxiang; Cui, Qinghua

2015-01-01

Some databases for drug-associated pathways have been built and are publicly available. However, the pathways curated in most of these databases are drug-action or drug-metabolism pathways. In recent years, high-throughput technologies such as microarray and RNA-sequencing have produced lots of drug-induced gene expression profiles. Interestingly, drug-induced gene expression profile frequently show distinct patterns, indicating that drugs normally induce the activation or repression of distinct pathways. Therefore, these pathways contribute to study the mechanisms of drugs and drug-repurposing. Here, we present Drug-Path, a database of drug-induced pathways, which was generated by KEGG pathway enrichment analysis for drug-induced upregulated genes and downregulated genes based on drug-induced gene expression datasets in Connectivity Map. Drug-Path provides user-friendly interfaces to retrieve, visualize and download the drug-induced pathway data in the database. In addition, the genes deregulated by a given drug are highlighted in the pathways. All data were organized using SQLite. The web site was implemented using Django, a Python web framework. Finally, we believe that this database will be useful for related researches. © The Author(s) 2015. Published by Oxford University Press.

Analysis of differential gene expression by bead-based fiber-optic array in nonfunctioning pituitary adenomas.

PubMed

Jiang, Z; Gui, S; Zhang, Y

2011-05-01

Nonfunctioning pituitary adenomas (NFPAs) are relatively common, accounting for 30% of all pituitary adenomas; however, their pathogenesis remains enigmatic. To explore the possible pathogenesis of NFPAs, we used fiber-optic BeadArray to examine gene expression in 5 NFPAs compared with 3 normal pituitaries. 4 differentially expressed genes were chosen randomly for validation by reverse transcriptase-real time quantitative polymerase chain reaction (RT-qPCR). We then analyzed the differentially expressed gene profile with Kyoto Encyclopedia of Genes and Genomes (KEGG). The array analysis indentified significant increases in the expression of 1,402 genes and 383 expressed sequence tags (ESTs), and decreases in 1,697 genes and 113 ESTs in the NFPAs. Bioinformatic and pathway analysis showed that the genes HIGD1B, FAM5C, PMAIP1 and the pathway cell-cycle regulation may play an important role in tumorigenesis and progression of NFPAs. Our data suggest fiber-optic BeadArray combined with pathway analysis of differential gene expression profile appears to be a valid approach for investigating the pathogenesis of tumors. © Georg Thieme Verlag KG Stuttgart · New York.

CalFitter: a web server for analysis of protein thermal denaturation data.

PubMed

Mazurenko, Stanislav; Stourac, Jan; Kunka, Antonin; Nedeljkovic, Sava; Bednar, David; Prokop, Zbynek; Damborsky, Jiri

2018-05-14

Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.

Comparative transcriptomes analysis of the wing disc between two silkworm strains with different size of wings

PubMed Central

Zhang, Jing; Blessing, Danso; Wu, Chenyu; Liu, Na; Li, Juan; Qin, Sheng

2017-01-01

Wings of Bombyx mori (B. mori) develop from the primordium, and different B. mori strains have different wing types. In order to identify the key factors influencing B. mori wing development, we chose strains P50 and U11, which are typical for normal wing and minute wing phenotypes, respectively. We dissected the wing disc on the 1st-day of wandering stage (P50D1 and U11D1), 2nd-day of wandering stage (P50D2 and U11D2), and 3rd-day of wandering stage (P50D3 and U11D3). Subsequently, RNA-sequencing (RNA-Seq) was performed on both strains in order to construct their gene expression profiles. P50 exhibited 628 genes differentially expressed to U11, 324 up-regulated genes, and 304 down-regulated genes. Five enriched gene ontology (GO) terms were identified by GO enrichment analysis based on these differentially expressed genes (DEGs). KEGG enrichment analysis results showed that the DEGs were enriched in five pathways; of these, we identified three pathways related to the development of wings. The three pathways include amino sugar and nucleotide sugar metabolism pathway, proteasome signaling pathway, and the Hippo signaling pathway. The representative genes in the enrichment pathways were further verified by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). The RNA-Seq and qRT-PCR results were largely consistent with each other. Our results also revealed that the significantly different genes obtained in our study might be involved in the development of the size of B. mori wings. In addition, several KEGG enriched pathways might be involved in the regulation of the pathways of wing formation. These results provide a basis for further research of wing development in B. mori. PMID:28617839

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling

PubMed Central

Fang, H; Tong, W; Perkins, R; Shi, L; Hong, H; Cao, X; Xie, Q; Yim, SH; Ward, JM; Pitot, HC; Dragan, YP

2005-01-01

Background The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer. Results In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism. Conclusion The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation. PMID:16026603

Brain Networks Shaping Religious Belief

PubMed Central

Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P.; Grafman, Jordan Henry

2014-01-01

Abstract We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing. PMID:24279687

Brain networks shaping religious belief.

PubMed

Kapogiannis, Dimitrios; Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P; Grafman, Jordan Henry

2014-02-01

We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing.

Analysis of Metabolic Pathways and Fluxes in a Newly Discovered Thermophilic and Ethanol-Tolerant Geobacillus Strain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Yinjie J.; Sapra, Rajat; Joyner, Dominique

2009-01-20

A recently discovered thermophilic bacterium, Geobacillus thermoglucosidasius M10EXG, ferments a range of C5 (e.g., xylose) and C6 sugars (e.g., glucose) and istolerant to high ethanol concentrations (10percent, v/v). We have investigated the central metabolism of this bacterium using both in vitro enzyme assays and 13C-based flux analysis to provide insights into the physiological properties of this extremophile and explore its metabolism for bio-ethanol or other bioprocess applications. Our findings show that glucose metabolism in G. thermoglucosidasius M10EXG proceeds via glycolysis, the pentose phosphate pathway, and the TCA cycle; the Entner?Doudoroff pathway and transhydrogenase activity were not detected. Anaplerotic reactions (includingmore » the glyoxylate shunt, pyruvate carboxylase, and phosphoenolpyruvate carboxykinase) were active, but fluxes through those pathways could not be accuratelydetermined using amino acid labeling. When growth conditions were switched from aerobic to micro-aerobic conditions, fluxes (based on a normalized glucose uptake rate of 100 units (g DCW)-1 h-1) through the TCA cycle and oxidative pentose phosphate pathway were reduced from 64+-3 to 25+-2 and from 30+-2 to 19+-2, respectively. The carbon flux under micro-aerobic growth was directed formate. Under fully anerobic conditions, G. thermoglucosidasius M10EXG used a mixed acid fermentation process and exhibited a maximum ethanol yield of 0.38+-0.07 mol mol-1 glucose. In silico flux balance modeling demonstrates that lactate and acetate production from G. thermoglucosidasius M10EXG reduces the maximum ethanol yieldby approximately threefold, thus indicating that both pathways should be modified to maximize ethanol production.« less

Comprehensive analysis of miRNAs expression profiles revealed potential key miRNA/mRNAs regulating colorectal cancer stem cell self-renewal.

PubMed

Xu, Peng; Wang, Junhua; Sun, Bo; Xiao, Zhongdang

2018-05-20

Self-renewal is essential for the malignant biological behaviors of colorectal cancer stem cells. While the self-renewal molecular mechanisms of colorectal cancer stem cells are not yet fully understood. Recently, miRNAs are reported to be relevant to the self-renewal ability of cancer stem cells. In this study, we first isolated colorectal cancer stem cell from colorectal cancer cell line HCT-116 by 1% low serum culture. Then we conducted a comprehensive analysis based on the miRNAs profiles data of both colorectal cancer stem cells and normal cultured colorectal cancer cells. Pathway analysis revealed multiple pathways including Jak-STAT, TGF-beta, PI3K-Akt and MAPK signaling pathway that are correlated to colorectal cancer. Further, we constructed a miRNA-mRNA network, based on which, several miRNA/mRNA pairs were ranked according to their impact index to the self-renewal of colorectal cancer stem cells. Further biological experiment showed that up-regulation of miR-92a-3p led to cell cycle arrest and reduced colony formation. This work provides clues to find the new potential biomarkers for colorectal cancer stem cell diagnosis and select effective miRNAs for targeted therapy. Copyright © 2018 Elsevier B.V. All rights reserved.

Gene expression profile in cardiovascular disease and preeclampsia: a meta-analysis of the transcriptome based on raw data from human studies deposited in Gene Expression Omnibus.

PubMed

Sitras, V; Fenton, C; Acharya, G

2015-02-01

Cardiovascular disease (CVD) and preeclampsia (PE) share common clinical features. We aimed to identify common transcriptomic signatures involved in CVD and PE in humans. Meta-analysis of individual raw microarray data deposited in GEO, obtained from blood samples of patients with CVD versus controls and placental samples from women with PE versus healthy women with uncomplicated pregnancies. Annotation of cases versus control samples was taken directly from the microarray documentation. Genes that showed a significant differential expression in the majority of experiments were selected for subsequent analysis. Hypergeometric gene list analysis was performed using Bioconductor GOstats package. Bioinformatic analysis was performed in PANTHER. Seven studies in CVD and 5 studies in PE were eligible for meta-analysis. A total of 181 genes were found to be differentially expressed in microarray studies investigating gene expression in blood samples obtained from patients with CVD compared to controls and 925 genes were differentially expressed between preeclamptic and healthy placentas. Among these differentially expressed genes, 22 were common between CVD and PE. Bioinformatic analysis of these genes revealed oxidative stress, p-53 pathway feedback, inflammation mediated by chemokines and cytokines, interleukin signaling, B-cell activation, PDGF signaling, Wnt signaling, integrin signaling and Alzheimer disease pathways to be involved in the pathophysiology of both CVD and PE. Metabolism, development, response to stimulus, immune response and cell communication were the associated biologic processes in both conditions. Gene set enrichment analysis showed the following overlapping pathways between CVD and PE: TGF-β-signaling, apoptosis, graft-versus-host disease, allograft rejection, chemokine signaling, steroid hormone synthesis, type I and II diabetes mellitus, VEGF signaling, pathways in cancer, GNRH signaling, Huntingtons disease and Notch signaling. CVD and PE share same common traits in their gene expression profile indicating common pathways in their pathophysiology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Auditory pathways: anatomy and physiology.

PubMed

Pickles, James O

2015-01-01

This chapter outlines the anatomy and physiology of the auditory pathways. After a brief analysis of the external, middle ears, and cochlea, the responses of auditory nerve fibers are described. The central nervous system is analyzed in more detail. A scheme is provided to help understand the complex and multiple auditory pathways running through the brainstem. The multiple pathways are based on the need to preserve accurate timing while extracting complex spectral patterns in the auditory input. The auditory nerve fibers branch to give two pathways, a ventral sound-localizing stream, and a dorsal mainly pattern recognition stream, which innervate the different divisions of the cochlear nucleus. The outputs of the two streams, with their two types of analysis, are progressively combined in the inferior colliculus and onwards, to produce the representation of what can be called the "auditory objects" in the external world. The progressive extraction of critical features in the auditory stimulus in the different levels of the central auditory system, from cochlear nucleus to auditory cortex, is described. In addition, the auditory centrifugal system, running from cortex in multiple stages to the organ of Corti of the cochlea, is described. © 2015 Elsevier B.V. All rights reserved.

[Impact of care pathway on the delay for initiation of antituberculosis treatment in Conakry, Guinea].

PubMed

Camara, A; Bah-Sow, O Y; Baldé, N M; Camara, L M; Barry, I S; Bah, B; Diallo, M; Chaperon, J; Riou, F

2009-06-01

Complex care pathways can result in detrimental treatment delay particularly in tuberculosis patients. The purpose of this retrospective study was to assess the care pathways followed by tuberculosis patients prior to diagnosis and to assess impact on the delay for initiation of treatment in Conakry, Guinea. A total of 112 patients were interviewed at the time of first admission for pulmonary tuberculosis with positive bacilloscopy. Based on interview data, pathways were classified as conventional (use of health care facilities only) and mixed (use of health care facilities, self-medication, and traditional medicine). The correlation between patient characteristics and type of pathway was assessed by univariate and multivariate analysis and the two groups, i.e., conventional vs. mixed, were compared with regard to delay for initiation of treatment. The care pathway was classified as mixed in two out of three patients. Multivariate analysis showed that this type of pathway was only correlated with schooling (p=0.02). The mean delay for treatment was similar, i.e., 13.4 and 12.8 weeks for conventional and mixed pathways respectively (p<0.68). The percentage of pathways including three consultations at health care facilities was significantly higher in the conventional than mixed group (72% vs. 30%, p<0.001). The main reasons given for delayed use of health care facilities were poor knowledge of tuberculosis symptoms (26%) and high cost of care (12%). The findings of this study indicate that tuberculosis patients follow a variety of care pathways that can lead to delayed treatment. An information campaign is needed to increase awareness among the population and care providers.

Targeted exploration and analysis of large cross-platform human transcriptomic compendia

PubMed Central

Zhu, Qian; Wong, Aaron K; Krishnan, Arjun; Aure, Miriam R; Tadych, Alicja; Zhang, Ran; Corney, David C; Greene, Casey S; Bongo, Lars A; Kristensen, Vessela N; Charikar, Moses; Li, Kai; Troyanskaya, Olga G.

2016-01-01

We present SEEK (http://seek.princeton.edu), a query-based search engine across very large transcriptomic data collections, including thousands of human data sets from almost 50 microarray and next-generation sequencing platforms. SEEK uses a novel query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify query-coregulated genes, pathways, and processes. SEEK provides cross-platform handling, multi-gene query search, iterative metadata-based search refinement, and extensive visualization-based analysis options. PMID:25581801

Transcriptomics of cortical gray matter thickness decline during normal aging

PubMed Central

Kochunov, P; Charlesworth, J; Winkler, A; Hong, LE; Nichols, T; Curran, JE; Sprooten, E; Jahanshad, N; Thompson, PM; Johnson, MP; Kent, JW; Landman, BA; Mitchell, B; Cole, SA; Dyer, TD; Moses, EK; Goring, HHH; Almasy, L; Duggirala, R; Olvera, RL; Glahn, DC; Blangero, J

2013-01-01

Introduction We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathways analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging Methods Transcriptome and GMT data were availabe for 379 individuals (age range=28–85) community-dwelling members of large extended Mexican-American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800µm) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability. Results Transcripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10−6) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT. Conclusion Expression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation. PMID:23707588

Transcriptomics of cortical gray matter thickness decline during normal aging.

PubMed

Kochunov, P; Charlesworth, J; Winkler, A; Hong, L E; Nichols, T E; Curran, J E; Sprooten, E; Jahanshad, N; Thompson, P M; Johnson, M P; Kent, J W; Landman, B A; Mitchell, B; Cole, S A; Dyer, T D; Moses, E K; Goring, H H H; Almasy, L; Duggirala, R; Olvera, R L; Glahn, D C; Blangero, J

2013-11-15

We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging. Transcriptome and GMT data were available for 379 individuals (age range=28-85) community-dwelling members of large extended Mexican American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800 μm) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability. Transcripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, and HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10(-6)) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT. Expression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation. Copyright © 2013 Elsevier Inc. All rights reserved.

Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction.

PubMed

Suresh, Rahul; Li, Xing; Chiriac, Anca; Goel, Kashish; Terzic, Andre; Perez-Terzic, Carmen; Nelson, Timothy J

2014-09-01

Whole-genome gene expression analysis has been successfully utilized to diagnose, prognosticate, and identify potential therapeutic targets for high-risk cardiovascular diseases. However, the feasibility of this approach to identify outcome-related genes and dysregulated pathways following first-time myocardial infarction (AMI) remains unknown and may offer a novel strategy to detect affected expressome networks that predict long-term outcome. Whole-genome expression microarray on blood samples from normal cardiac function controls (n=21) and first-time AMI patients (n=31) within 48-hours post-MI revealed expected differential gene expression profiles enriched for inflammation and immune-response pathways. To determine molecular signatures at the time of AMI associated with long-term outcomes, transcriptional profiles from sub-groups of AMI patients with (n=5) or without (n=22) any recurrent events over an 18-month follow-up were compared. This analysis identified 559 differentially-expressed genes. Bioinformatic analysis of this differential gene-set for associated pathways revealed 1) increasing disease severity in AMI patients is associated with a decreased expression of genes involved in the developmental epithelial-to-mesenchymal transition pathway, and 2) modulation of cholesterol transport genes that include ABCA1, CETP, APOA1, and LDLR is associated with clinical outcome. Differentially regulated genes and modulated pathways were identified that were associated with recurrent cardiovascular outcomes in first-time AMI patients. This cell-based approach for risk stratification in AMI could represent a novel, non-invasive platform to anticipate modifiable pathways and therapeutic targets to optimize long-term outcome for AMI patients and warrants further study to determine the role of metabolic remodeling and regenerative processes required for optimal outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Differential proteomics analysis to identify proteins and pathways associated with male sterility of soybean using iTRAQ-based strategy.

PubMed

Li, Jiajia; Ding, Xianlong; Han, Shaohuai; He, Tingting; Zhang, Hao; Yang, Longshu; Yang, Shouping; Gai, Junyi

2016-04-14

To further elucidate the molecular mechanism of cytoplasmic male sterility (CMS) in soybean, a differential proteomic analysis was completed between the CMS line NJCMS1A and its maintainer NJCMS1B using iTRAQ-based strategy. As a result, 180 differential abundance proteins (DAPs) were identified, of which, 60 were down-regulated and 120 were up-regulated in NJCMS1A compared with NJCMS1B. Bioinformatic analysis showed that 167 DAPs were annotated in 41 Gene Ontology functional groups, 106 DAPs were classified into 20 clusters of orthologous groups of protein categories, and 128 DAPs were enrichment in 53 KEGG pathways. Fifteen differential level proteins/genes with the same expression pattern were identified in the further conjoint analysis of DAPs and the previously reported differential expression genes. Moreover, multiple reaction monitoring test, qRT-PCR analysis and enzyme activity assay validated that the iTRAQ results were reliable. Based on functional analysis of DAPs, we concluded that male sterility in NJCMS1A might be related to insufficiencies in energy supply, unbalance of protein synthesis and degradation, disruption of flavonoid synthesis, programmed cell death, abnormalities of substance metabolism, etc. These results might facilitate our understanding of the molecular mechanisms behind CMS in soybean. Soybean is an important global crop that provides protein and oil. Heterosis is a significantly potential approach to increase the yield of soybean. Cytoplasmic male sterility (CMS) plays a vital role in the production of hybrid seeds. However, the genetic and molecular mechanisms of male sterility in soybean still need to be further elucidated. In the present paper, a differential proteomic analysis was carried out and the results showed that several key proteins involved in key pathways were associated with male sterility in soybean. This work provides a new insight to understand the genetic and molecular mechanisms underlying CMS in soybean. Copyright © 2016 Elsevier B.V. All rights reserved.

Identifying miRNA-mediated signaling subpathways by integrating paired miRNA/mRNA expression data with pathway topology.

PubMed

Vrahatis, Aristidis G; Dimitrakopoulos, Georgios N; Tsakalidis, Athanasios K; Bezerianos, Anastasios

2015-01-01

In the road for network medicine the newly emerged systems-level subpathway-based analysis methods offer new disease genes, drug targets and network-based biomarkers. In parallel, paired miRNA/mRNA expression data enable simultaneously monitoring of the micronome effect upon the signaling pathways. Towards this orientation, we present a methodological pipeline for the identification of differentially expressed subpathways along with their miRNA regulators by using KEGG signaling pathway maps, miRNA-target interactions and expression profiles from paired miRNA/mRNA experiments. Our pipeline offered new biological insights on a real application of paired miRNA/mRNA expression profiles with respect to the dynamic changes from colostrum to mature milk whey; several literature supported genes and miRNAs were recontextualized through miRNA-mediated differentially expressed subpathways.

MicroRNA profiling in intraocular medulloepitheliomas.

PubMed

Edward, Deepak P; Alkatan, Hind; Rafiq, Qundeel; Eberhart, Charles; Al Mesfer, Saleh; Ghazi, Nicola; Al Safieh, Leen; Kondkar, Altaf A; Abu Amero, Khaled K

2015-01-01

To study the differential expression of microRNA (miRNA) profiles between intraocular medulloepithelioma (ME) and normal control tissue (CT). Total RNA was extracted from formalin fixed paraffin embedded (FFPE) intraocular ME (n=7) and from age matched ciliary body controls (n=8). The clinical history and phenotype was recorded. MiRNA profiles were determined using the Affymetrix GeneChip miRNA Arrays analyzed using expression console 1.3 software. Validation of significantly dysregulated miRNA was confirmed by quantitative real-time PCR. The web-based DNA Intelligent Analysis (DIANA)-miRPath v2.0 was used to perform enrichment analysis of differentially expressed (DE) miRNA gene targets in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The pathologic evaluation revealed one benign (benign non-teratoid, n=1) and six malignant tumors (malignant teratoid, n=2; malignant non-teratoid, n = 4). A total of 88 miRNAs were upregulated and 43 miRNAs were downregulated significantly (P<0.05) in the tumor specimens. Many of these significantly dysregulated miRNAs were known to play various roles in carcinogenesis and tumor behavior. RT-PCR validated three significantly upregulated miRNAs and three significantly downregulated miRNAs namely miR-217, miR-216a, miR-216b, miR-146a, miR-509-3p and miR-211. Many DE miRNAs that were significant in ME tumors showed dysregulation in retinoblastoma, glioblastoma, and precursor, normal and reactive human cartilage. Enriched pathway analysis suggested a significant association of upregulated miRNAs with 15 pathways involved in prion disease and several types of cancer. The pathways involving significantly downregulated miRNAs included the toll-like receptor (TLR) (p<4.36E-16) and Nuclear Factor kappa B (NF-κB) signaling pathways (p<9.00E-06). We report significantly dysregulated miRNAs in intraocular ME tumors, which exhibited abnormal profiles in other cancers as well such as retinoblastoma and glioblastoma. Pathway analysis of all dysregulated miRNAs shared commonalities with other cancer pathways.

Dynamic regulation of genetic pathways and targets during aging in Caenorhabditis elegans.

PubMed

He, Kan; Zhou, Tao; Shao, Jiaofang; Ren, Xiaoliang; Zhao, Zhongying; Liu, Dahai

2014-03-01

Numerous genetic targets and some individual pathways associated with aging have been identified using the worm model. However, less is known about the genetic mechanisms of aging in genome wide, particularly at the level of multiple pathways as well as the regulatory networks during aging. Here, we employed the gene expression datasets of three time points during aging in Caenorhabditis elegans (C. elegans) and performed the approach of gene set enrichment analysis (GSEA) on each dataset between adjacent stages. As a result, multiple genetic pathways and targets were identified as significantly down- or up-regulated. Among them, 5 truly aging-dependent signaling pathways including MAPK signaling pathway, mTOR signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and ErbB signaling pathway as well as 12 significantly associated genes were identified with dynamic expression pattern during aging. On the other hand, the continued declines in the regulation of several metabolic pathways have been demonstrated to display age-related changes. Furthermore, the reconstructed regulatory networks based on three of aging related Chromatin immunoprecipitation experiments followed by sequencing (ChIP-seq) datasets and the expression matrices of 154 involved genes in above signaling pathways provide new insights into aging at the multiple pathways level. The combination of multiple genetic pathways and targets needs to be taken into consideration in future studies of aging, in which the dynamic regulation would be uncovered.

Elementary Mode Analysis: A Useful Metabolic Pathway Analysis Tool for Characterizing Cellular Metabolism

PubMed Central

Trinh, Cong T.; Wlaschin, Aaron; Srienc, Friedrich

2010-01-01

Elementary Mode Analysis is a useful Metabolic Pathway Analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering. PMID:19015845

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

PubMed

Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

2006-06-06

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Hemorrhage and Subsequent Allogenic Red Blood Cell Transfusion are Associated With Characteristic Monocyte Messenger RNA Expression Patterns in Patients After Multiple Injury—A Genome Wide View

PubMed Central

Bogner, Viktoria; Baker, Henry V.; Kanz, Karl-Georg; Moldawer, L. L.; Mutschler, Wolf; Biberthaler, Peter

2014-01-01

Introduction As outcome to severe trauma is frequently affected by massive blood loss and consecutive hemorrhagic shock, replacement of red blood cell (RBC) units remains indispensable. Administration of RBC units is an independent risk factor for adverse outcome in patients with trauma. The impact of massive blood transfusion or uncrossmatched blood transfusion on the patients’ immune response in the early posttraumatic period remains unclear. Material Thirteen patients presenting with blunt multiple injuries (Injury Severity Score >16) were studied. Monocytes were obtained on admission and at 6, 12, 24, 48, and 72 hours after trauma. Biotinylated complementary RNA targets were hybridized to Affymetrix HG U 133A microarrays. The data were analyzed by a supervised analysis based on whether the patients received massive blood transfusions, and then subsequently, by hierarchical clustering, and by Ingenuity pathway analysis. Results Supervised analysis identified 224 probe sets to be differentially expressed (p < 0.001) in patients who received massive blood transfusion, when compared with those who did not. In addition, 331 probe sets were found differentially expressed (p < 0.001) in patients who received uncrossmatched RBC units in comparison with those who exclusively gained crossmatched ones. Functional pathway analysis of the respectively identified gene expression profiles suggests a contributory role by the AKT/PI3Kinase pathway, the mitogen-activated protein-kinase pathway, the Ubiquitin pathway, and the diverse inflammatory networks. Conclusion We exhibited for the first time a serial, sequential screening analysis of monocyte messenger RNA expression patterns in patients with multiple trauma indicating a strongly significant association between the patients’ genomic response in blood monocytes and massive or uncross-matched RBC substitution. PMID:19820587

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis.

PubMed

Guo, Sheng-Min; Wang, Jian-Xiong; Li, Jin; Xu, Fang-Yuan; Wei, Quan; Wang, Hai-Ming; Huang, Hou-Qiang; Zheng, Si-Lin; Xie, Yu-Jie; Zhang, Chi

2018-06-15

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA-associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease-related networks based on 21756 gene expression correlation coefficients, hub-genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits-related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA-associated genes. Moreover, 310 OA-associated genes were found, and 34 of them were among hub-genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)-receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'-kinase (PI3K)-Akt signaling pathway (PI3K-AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA. © 2018 Wiley Periodicals, Inc.

OVCAR-3 Spheroid-Derived Cells Display Distinct Metabolic Profiles

PubMed Central

Vermeersch, Kathleen A.; Wang, Lijuan; Mezencev, Roman; McDonald, John F.; Styczynski, Mark P.

2015-01-01

Introduction Recently, multicellular spheroids were isolated from a well-established epithelial ovarian cancer cell line, OVCAR-3, and were propagated in vitro. These spheroid-derived cells displayed numerous hallmarks of cancer stem cells, which are chemo- and radioresistant cells thought to be a significant cause of cancer recurrence and resultant mortality. Gene set enrichment analysis of expression data from the OVCAR-3 cells and the spheroid-derived putative cancer stem cells identified several metabolic pathways enriched in differentially expressed genes. Before this, there had been little previous knowledge or investigation of systems-scale metabolic differences between cancer cells and cancer stem cells, and no knowledge of such differences in ovarian cancer stem cells. Methods To determine if there were substantial metabolic changes corresponding with these transcriptional differences, we used two-dimensional gas chromatography coupled to mass spectrometry to measure the metabolite profiles of the two cell lines. Results These two cell lines exhibited significant metabolic differences in both intracellular and extracellular metabolite measurements. Principal components analysis, an unsupervised dimensional reduction technique, showed complete separation between the two cell types based on their metabolite profiles. Pathway analysis of intracellular metabolomics data revealed close overlap with metabolic pathways identified from gene expression data, with four out of six pathways found enriched in gene-level analysis also enriched in metabolite-level analysis. Some of those pathways contained multiple metabolites that were individually statistically significantly different between the two cell lines, with one of the most broadly and consistently different pathways, arginine and proline metabolism, suggesting an interesting hypothesis about cancerous and stem-like metabolic phenotypes in this pair of cell lines. Conclusions Overall, we demonstrate for the first time that metabolism in an ovarian cancer stem cell line is distinct from that of more differentiated isogenic cancer cells, supporting the potential importance of metabolism in the differences between cancer cells and cancer stem cells. PMID:25688563

Genome-wide gene–environment interaction analysis for asbestos exposure in lung cancer susceptibility

PubMed Central

Wei, Qingyi Wei

2012-01-01

Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene–environment interactions. To determine gene–asbestos interactions in lung cancer risk, we conducted genome-wide gene–environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P-values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P-value for the most significant SNP rs13383928 was 2.17×10–6, which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54, located on 7q32.1 (P = 8.90×10–5). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene–asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk. Abbreviations:CIconfidence intervalEenvironmentFDRfalse discovery rateGgeneGSEAgene-set-enrichment analysisGWASgenome-wide association studiesi-GSEAimproved gene-set-enrichment analysis approachORodds ratioSNPsingle nucleotide polymorphism PMID:22637743

Investigation on biochemical compositional changes during the microbial fermentation process of Fu brick tea by LC-MS based metabolomics.

PubMed

Xu, Jie; Hu, Feng-Lin; Wang, Wei; Wan, Xiao-Chun; Bao, Guan-Hu

2015-11-01

Fu brick tea (FBT) is a unique post-fermented tea product which is fermented with fungi during the manufacturing process. In this study, we investigated the biochemical compositional changes occurring during the microbial fermentation process (MFP) of FBT based on non-targeted LC-MS, which was a comprehensive and unbiased methodology. Our data analysis took a two-phase approach: (1) comparison of FBT with other tea products using PCA analysis to exhibit the characteristic effect of MFP on the formation of Fu brick tea and (2) comparison of tea samples throughout the MFP of FBT to elucidate the possible key metabolic pathways produced by the fungi. Non-targeted LC-MS analysis clearly distinguished FBT with other tea samples and highlighted some interesting metabolic pathways during the MFP including B ring fission catechin. Our study demonstrated that those fungi had a significant influence on the biochemical profiles in the FBT and consequently contributed to its unique quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Parameterizing the Transport Pathways for Cell Invasion in Complex Scaffold Architectures

PubMed Central

Ashworth, Jennifer C.; Mehr, Marco; Buxton, Paul G.; Best, Serena M.

2016-01-01

Interconnecting pathways through porous tissue engineering scaffolds play a vital role in determining nutrient supply, cell invasion, and tissue ingrowth. However, the global use of the term “interconnectivity” often fails to describe the transport characteristics of these pathways, giving no clear indication of their potential to support tissue synthesis. This article uses new experimental data to provide a critical analysis of reported methods for the description of scaffold transport pathways, ranging from qualitative image analysis to thorough structural parameterization using X-ray Micro-Computed Tomography. In the collagen scaffolds tested in this study, it was found that the proportion of pore space perceived to be accessible dramatically changed depending on the chosen method of analysis. Measurements of % interconnectivity as defined in this manner varied as a function of direction and connection size, and also showed a dependence on measurement length scale. As an alternative, a method for transport pathway parameterization was investigated, using percolation theory to calculate the diameter of the largest sphere that can travel to infinite distance through a scaffold in a specified direction. As proof of principle, this approach was used to investigate the invasion behavior of primary fibroblasts in response to independent changes in pore wall alignment and pore space accessibility, parameterized using the percolation diameter. The result was that both properties played a distinct role in determining fibroblast invasion efficiency. This example therefore demonstrates the potential of the percolation diameter as a method of transport pathway parameterization, to provide key structural criteria for application-based scaffold design. PMID:26888449

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

PubMed Central

2012-01-01

Background Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets. Results The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets. Conclusions The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods. PMID:22876834

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory.

PubMed

Jakobson, Christopher M; Tullman-Ercek, Danielle; Mangan, Niall M

2018-05-29

Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We use a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we demonstrate that the optimal design varies as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible design-space analysis we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.

Identification of Differentially Expressed Genes and Pathways for Myofiber Characteristics in Soleus Muscles between Chicken Breeds Differing in Meat Quality.

PubMed

Du, Y F; Ding, Q L; Li, Y M; Fang, W R

2017-04-03

In the modern chicken industry, fast-growing broilers have undergone strong artificial selection for muscle growth, which has led to remarkable phenotypic variations compared with slow-growing chickens. However, the molecular mechanism underlying these phenotypes differences remains unknown. In this study, a systematic identification of candidate genes and new pathways related to myofiber development and composition in chicken Soleus muscle (SOL) has been made using gene expression profiles of two distinct breeds: Qingyuan partridge (QY), a slow-growing Chinese breed possessing high meat quality and Cobb 500 (CB), a commercial fast-growing broiler line. Agilent cDNA microarray analyses were conducted to determine gene expression profiles of soleus muscle sampled at sexual maturity age of QY (112 d) and CB (42 d). The 1318 genes with at least 2-fold differences were identified (P < 0.05, FDR <0.05, FC ≥ 2) in SOL muscles of QY and CB chickens. Differentially expressed genes (DEGs) related to muscle development, energy metabolism or lipid metabolism processes were examined further in each breed based on Gene Ontology (GO) analysis, and 11 genes involved in these processes were selected for further validation studies by qRT-PCR. In addition, based on KEGG pathway analysis of DEGs in both QY and CB chickens, it was found that in addition to pathways affecting myogenic fibre-type development and differentiation (pathways for Hedgehog & Calcium signaling), energy metabolism (Phosphatidylinositol signaling system, VEGF signaling pathway, Purine metabolism, Pyrimidine metabolism) were also enriched and might form a network with pathways related to muscle metabolism to influence the development of myofibers. This study is the first stage in the understanding of molecular mechanisms underlying variations in poultry meat quality. Large scale analyses are now required to validate the role of the genes identified and ultimately to find molecular markers that can be used for selection or to optimize rearing practices.

Small RNA-Seq analysis reveals microRNA-regulation of the Imd pathway during Escherichia coli infection in Drosophila.

PubMed

Li, Shengjie; Shen, Li; Sun, Lianjie; Xu, Jiao; Jin, Ping; Chen, Liming; Ma, Fei

2017-05-01

Drosophila have served as a model for research on innate immunity for decades. However, knowledge of the post-transcriptional regulation of immune gene expression by microRNAs (miRNAs) remains rudimentary. In the present study, using small RNA-seq and bioinformatics analysis, we identified 67 differentially expressed miRNAs in Drosophila infected with Escherichia coli compared to injured flies at three time-points. Furthermore, we found that 21 of these miRNAs were potentially involved in the regulation of Imd pathway-related genes. Strikingly, based on UAS-miRNAs line screening and Dual-luciferase assay, we identified that miR-9a and miR-981 could both negatively regulate Drosophila antibacterial defenses and decrease the level of the antibacterial peptide, Diptericin. Taken together, these data support the involvement of miRNAs in the regulation of the Drosophila Imd pathway. Copyright © 2017 Elsevier Ltd. All rights reserved.

Proteomics analysis reveals a dynamic diurnal pattern of photosynthesis-related pathways in maize leaves.

PubMed

Feng, Dan; Wang, Yanwei; Lu, Tiegang; Zhang, Zhiguo; Han, Xiao

2017-01-01

Plant leaves exhibit differentiated patterns of photosynthesis rates under diurnal light regulation. Maize leaves show a single-peak pattern without photoinhibition at midday when the light intensity is maximized. This mechanism contributes to highly efficient photosynthesis in maize leaves. To understand the molecular basis of this process, an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomics analysis was performed to reveal the dynamic pattern of proteins related to photosynthetic reactions. Steady, single-peak and double-peak protein expression patterns were discovered in maize leaves, and antenna proteins in these leaves displayed a steady pattern. In contrast, the photosystem, carbon fixation and citrate pathways were highly controlled by diurnal light intensity. Most enzymes in the limiting steps of these pathways were major sites of regulation. Thus, maize leaves optimize photosynthesis and carbon fixation outside of light harvesting to adapt to the changes in diurnal light intensity at the protein level.

Mass spectrometry-based metabolomics: applications to biomarker and metabolic pathway research.

PubMed

Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

2016-01-01

Mass spectrometry-based metabolomics has become increasingly popular in molecular medicine. High-definition mass spectrometry (MS), coupled with pattern recognition methods, have been carried out to obtain comprehensive metabolite profiling and metabolic pathway of large biological datasets. This sets the scene for a new and powerful diagnostic approach. Analysis of the key metabolites in body fluids has become an important part of improving disease diagnosis. With technological advances in analytical techniques, the ability to measure low-molecular-weight metabolites in bio-samples provides a powerful platform for identifying metabolites that are uniquely correlated with a specific human disease. MS-based metabolomics can lead to enhanced understanding of disease mechanisms and to new diagnostic markers and has a strong potential to contribute to improving early diagnosis of diseases. This review will highlight the importance and benefit with certain characteristic examples of MS-metabolomics for identifying metabolic pathways and metabolites that accurately screen for potential diagnostic biomarkers of diseases. Copyright © 2015 John Wiley & Sons, Ltd.

Developing an in silico model of the modulation of base excision repair using methoxyamine for more targeted cancer therapeutics.

PubMed

Gurkan-Cavusoglu, Evren; Avadhani, Sriya; Liu, Lili; Kinsella, Timothy J; Loparo, Kenneth A

2013-04-01

Base excision repair (BER) is a major DNA repair pathway involved in the processing of exogenous non-bulky base damages from certain classes of cancer chemotherapy drugs as well as ionising radiation (IR). Methoxyamine (MX) is a small molecule chemical inhibitor of BER that is shown to enhance chemotherapy and/or IR cytotoxicity in human cancers. In this study, the authors have analysed the inhibitory effect of MX on the BER pathway kinetics using a computational model of the repair pathway. The inhibitory effect of MX depends on the BER efficiency. The authors have generated variable efficiency groups using different sets of protein concentrations generated by Latin hypercube sampling, and they have clustered simulation results into high, medium and low efficiency repair groups. From analysis of the inhibitory effect of MX on each of the three groups, it is found that the inhibition is most effective for high efficiency BER, and least effective for low efficiency repair.

Monocytic cell junction proteins serve important roles in atherosclerosis via the endoglin pathway

PubMed Central

Chen, Lina; Chen, Zhongliang; Ge, Menghua; Tang, Oushan; Cheng, Yinhong; Zhou, Haoliang; Shen, Yu; Qin, Fengming

2017-01-01

The formation of atherosclerosis is recognized to be caused by multiple factors including pathogenesis in monocytes during inflammation. The current study provided evidence that monocytic junctions were significantly altered in patients with atherosclerosis, which suggested an association between cell junctions and atherosclerosis. Claudin-1, occludin-1 and ZO-1 were significantly enhanced in atherosclerosis, indicating that the tight junction pathway was activated during the pathogenesis of atherosclerosis. In addition, the gene expression of 5 connexin members involved in the gap junction pathway were quantified, indicating that connexin 43 and 46 were significantly up-regulated in atherosclerosis. Furthermore, inflammatory factors including endoglin and SMAD were observed, suggesting that immune regulative factors were down-regulated in this pathway. Silicon-based analysis additionally identified that connexins and tight junctions were altered in association with monocytic inflammation regulations, endoglin pathway. The results imply that reduced expression of the immune regulation pathway in monocytes is correlated with the generation of gap junctions and tight junctions which serve important roles in atherosclerosis. PMID:28901429

Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes

PubMed Central

Biankin, Andrew V.; Waddell, Nicola; Kassahn, Karin S.; Gingras, Marie-Claude; Muthuswamy, Lakshmi B.; Johns, Amber L.; Miller, David K.; Wilson, Peter J.; Patch, Ann-Marie; Wu, Jianmin; Chang, David K.; Cowley, Mark J.; Gardiner, Brooke B.; Song, Sarah; Harliwong, Ivon; Idrisoglu, Senel; Nourse, Craig; Nourbakhsh, Ehsan; Manning, Suzanne; Wani, Shivangi; Gongora, Milena; Pajic, Marina; Scarlett, Christopher J.; Gill, Anthony J.; Pinho, Andreia V.; Rooman, Ilse; Anderson, Matthew; Holmes, Oliver; Leonard, Conrad; Taylor, Darrin; Wood, Scott; Xu, Qinying; Nones, Katia; Fink, J. Lynn; Christ, Angelika; Bruxner, Tim; Cloonan, Nicole; Kolle, Gabriel; Newell, Felicity; Pinese, Mark; Mead, R. Scott; Humphris, Jeremy L.; Kaplan, Warren; Jones, Marc D.; Colvin, Emily K.; Nagrial, Adnan M.; Humphrey, Emily S.; Chou, Angela; Chin, Venessa T.; Chantrill, Lorraine A.; Mawson, Amanda; Samra, Jaswinder S.; Kench, James G.; Lovell, Jessica A.; Daly, Roger J.; Merrett, Neil D.; Toon, Christopher; Epari, Krishna; Nguyen, Nam Q.; Barbour, Andrew; Zeps, Nikolajs; Kakkar, Nipun; Zhao, Fengmei; Wu, Yuan Qing; Wang, Min; Muzny, Donna M.; Fisher, William E.; Brunicardi, F. Charles; Hodges, Sally E.; Reid, Jeffrey G.; Drummond, Jennifer; Chang, Kyle; Han, Yi; Lewis, Lora R.; Dinh, Huyen; Buhay, Christian J.; Beck, Timothy; Timms, Lee; Sam, Michelle; Begley, Kimberly; Brown, Andrew; Pai, Deepa; Panchal, Ami; Buchner, Nicholas; De Borja, Richard; Denroche, Robert E.; Yung, Christina K.; Serra, Stefano; Onetto, Nicole; Mukhopadhyay, Debabrata; Tsao, Ming-Sound; Shaw, Patricia A.; Petersen, Gloria M.; Gallinger, Steven; Hruban, Ralph H.; Maitra, Anirban; Iacobuzio-Donahue, Christine A.; Schulick, Richard D.; Wolfgang, Christopher L.; Morgan, Richard A.; Lawlor, Rita T.; Capelli, Paola; Corbo, Vincenzo; Scardoni, Maria; Tortora, Giampaolo; Tempero, Margaret A.; Mann, Karen M.; Jenkins, Nancy A.; Perez-Mancera, Pedro A.; Adams, David J.; Largaespada, David A.; Wessels, Lodewyk F. A.; Rust, Alistair G.; Stein, Lincoln D.; Tuveson, David A.; Copeland, Neal G.; Musgrove, Elizabeth A.; Scarpa, Aldo; Eshleman, James R.; Hudson, Thomas J.; Sutherland, Robert L.; Wheeler, David A.; Pearson, John V.; McPherson, John D.; Gibbs, Richard A.; Grimmond, Sean M.

2012-01-01

Pancreatic cancer is a highly lethal malignancy with few effective therapies. We performed exome sequencing and copy number analysis to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (stage I and II) sporadic pancreatic ductal adenocarcinoma. Detailed analysis of 99 informative tumours identified substantial heterogeneity with 2,016 non-silent mutations and 1,628 copy-number variations. We define 16 significantly mutated genes, reaffirming known mutations (KRAS, TP53, CDKN2A, SMAD4, MLL3, TGFBR2, ARID1A and SF3B1), and uncover novel mutated genes including additional genes involved in chromatin modification (EPC1 and ARID2), DNA damage repair (ATM) and other mechanisms (ZIM2, MAP2K4, NALCN, SLC16A4 and MAGEA6). Integrative analysis with in vitro functional data and animal models provided supportive evidence for potential roles for these genetic aberrations in carcinogenesis. Pathway-based analysis of recurrently mutated genes recapitulated clustering in core signalling pathways in pancreatic ductal adenocarcinoma, and identified new mutated genes in each pathway. We also identified frequent and diverse somatic aberrations in genes described traditionally as embryonic regulators of axon guidance, particularly SLIT/ROBO signalling, which was also evident in murine Sleeping Beauty transposon-mediated somatic mutagenesis models of pancreatic cancer, providing further supportive evidence for the potential involvement of axon guidance genes in pancreatic carcinogenesis. PMID:23103869

Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes.

PubMed

Biankin, Andrew V; Waddell, Nicola; Kassahn, Karin S; Gingras, Marie-Claude; Muthuswamy, Lakshmi B; Johns, Amber L; Miller, David K; Wilson, Peter J; Patch, Ann-Marie; Wu, Jianmin; Chang, David K; Cowley, Mark J; Gardiner, Brooke B; Song, Sarah; Harliwong, Ivon; Idrisoglu, Senel; Nourse, Craig; Nourbakhsh, Ehsan; Manning, Suzanne; Wani, Shivangi; Gongora, Milena; Pajic, Marina; Scarlett, Christopher J; Gill, Anthony J; Pinho, Andreia V; Rooman, Ilse; Anderson, Matthew; Holmes, Oliver; Leonard, Conrad; Taylor, Darrin; Wood, Scott; Xu, Qinying; Nones, Katia; Fink, J Lynn; Christ, Angelika; Bruxner, Tim; Cloonan, Nicole; Kolle, Gabriel; Newell, Felicity; Pinese, Mark; Mead, R Scott; Humphris, Jeremy L; Kaplan, Warren; Jones, Marc D; Colvin, Emily K; Nagrial, Adnan M; Humphrey, Emily S; Chou, Angela; Chin, Venessa T; Chantrill, Lorraine A; Mawson, Amanda; Samra, Jaswinder S; Kench, James G; Lovell, Jessica A; Daly, Roger J; Merrett, Neil D; Toon, Christopher; Epari, Krishna; Nguyen, Nam Q; Barbour, Andrew; Zeps, Nikolajs; Kakkar, Nipun; Zhao, Fengmei; Wu, Yuan Qing; Wang, Min; Muzny, Donna M; Fisher, William E; Brunicardi, F Charles; Hodges, Sally E; Reid, Jeffrey G; Drummond, Jennifer; Chang, Kyle; Han, Yi; Lewis, Lora R; Dinh, Huyen; Buhay, Christian J; Beck, Timothy; Timms, Lee; Sam, Michelle; Begley, Kimberly; Brown, Andrew; Pai, Deepa; Panchal, Ami; Buchner, Nicholas; De Borja, Richard; Denroche, Robert E; Yung, Christina K; Serra, Stefano; Onetto, Nicole; Mukhopadhyay, Debabrata; Tsao, Ming-Sound; Shaw, Patricia A; Petersen, Gloria M; Gallinger, Steven; Hruban, Ralph H; Maitra, Anirban; Iacobuzio-Donahue, Christine A; Schulick, Richard D; Wolfgang, Christopher L; Morgan, Richard A; Lawlor, Rita T; Capelli, Paola; Corbo, Vincenzo; Scardoni, Maria; Tortora, Giampaolo; Tempero, Margaret A; Mann, Karen M; Jenkins, Nancy A; Perez-Mancera, Pedro A; Adams, David J; Largaespada, David A; Wessels, Lodewyk F A; Rust, Alistair G; Stein, Lincoln D; Tuveson, David A; Copeland, Neal G; Musgrove, Elizabeth A; Scarpa, Aldo; Eshleman, James R; Hudson, Thomas J; Sutherland, Robert L; Wheeler, David A; Pearson, John V; McPherson, John D; Gibbs, Richard A; Grimmond, Sean M

2012-11-15

Pancreatic cancer is a highly lethal malignancy with few effective therapies. We performed exome sequencing and copy number analysis to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (stage I and II) sporadic pancreatic ductal adenocarcinoma. Detailed analysis of 99 informative tumours identified substantial heterogeneity with 2,016 non-silent mutations and 1,628 copy-number variations. We define 16 significantly mutated genes, reaffirming known mutations (KRAS, TP53, CDKN2A, SMAD4, MLL3, TGFBR2, ARID1A and SF3B1), and uncover novel mutated genes including additional genes involved in chromatin modification (EPC1 and ARID2), DNA damage repair (ATM) and other mechanisms (ZIM2, MAP2K4, NALCN, SLC16A4 and MAGEA6). Integrative analysis with in vitro functional data and animal models provided supportive evidence for potential roles for these genetic aberrations in carcinogenesis. Pathway-based analysis of recurrently mutated genes recapitulated clustering in core signalling pathways in pancreatic ductal adenocarcinoma, and identified new mutated genes in each pathway. We also identified frequent and diverse somatic aberrations in genes described traditionally as embryonic regulators of axon guidance, particularly SLIT/ROBO signalling, which was also evident in murine Sleeping Beauty transposon-mediated somatic mutagenesis models of pancreatic cancer, providing further supportive evidence for the potential involvement of axon guidance genes in pancreatic carcinogenesis.

T-cell lymphomas associated gene expression signature: Bioinformatics analysis based on gene expression Omnibus.

PubMed

Zhou, Lei-Lei; Xu, Xiao-Yue; Ni, Jie; Zhao, Xia; Zhou, Jian-Wei; Feng, Ji-Feng

2018-06-01

Due to the low incidence and the heterogeneity of subtypes, the biological process of T-cell lymphomas is largely unknown. Although many genes have been detected in T-cell lymphomas, the role of these genes in biological process of T-cell lymphomas was not further analyzed. Two qualified datasets were downloaded from Gene Expression Omnibus database. The biological functions of differentially expressed genes were evaluated by gene ontology enrichment and KEGG pathway analysis. The network for intersection genes was constructed by the cytoscape v3.0 software. Kaplan-Meier survival curves and log-rank test were employed to assess the association between differentially expressed genes and clinical characters. The intersection mRNAs were proved to be associated with fundamental processes of T-cell lymphoma cells. These intersection mRNAs were involved in the activation of some cancer-related pathways, including PI3K/AKT, Ras, JAK-STAT, and NF-kappa B signaling pathway. PDGFRA, CXCL12, and CCL19 were the most significant central genes in the signal-net analysis. The results of survival analysis are not entirely credible. Our findings uncovered aberrantly expressed genes and a complex RNA signal network in T-cell lymphomas and indicated cancer-related pathways involved in disease initiation and progression, providing a new insight for biotargeted therapy in T-cell lymphomas. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes, and Cytokine Signaling Genes in Crohn's Disease

PubMed Central

Carbonetto, Peter; Stephens, Matthew

2013-01-01

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study. PMID:24098138

Investigating the different mechanisms of genotoxic and non-genotoxic carcinogens by a gene set analysis.

PubMed

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways.

Investigating the Different Mechanisms of Genotoxic and Non-Genotoxic Carcinogens by a Gene Set Analysis

PubMed Central

Lee, Won Jun; Kim, Sang Cheol; Lee, Seul Ji; Lee, Jeongmi; Park, Jeong Hill; Yu, Kyung-Sang; Lim, Johan; Kwon, Sung Won

2014-01-01

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways. PMID:24497971

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, M; Craft, D

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchicalmore » clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve cancer classification using biological pathways. Patients are classified with greater specificity and physiological relevance as compared to current gene-specific approaches. Focus now moves to utilizing PICS for pan-cancer patient-specific treatment response prediction.« less

Functional analysis of the gene controlling hydroxylation of festuclavine in the ergot alkaloid pathway of Neosartorya fumigata

PubMed Central

Bilovol, Yulia; Panaccione, Daniel G.

2016-01-01

Bioactive ergot alkaloids produced by several species of fungi are important molecules in agriculture and medicine. Much of the ergot alkaloid pathway has been elucidated, but a few steps, including the gene controlling hydroxylation of festuclavine to fumigaclavine B, remain unsolved. Festuclavine is a key intermediate in the fumigaclavine branch of the ergot alkaloid pathway of the opportunistic pathogen Neosartorya fumigata and also in the dihydrolysergic acid-based ergot alkaloid pathway of certain Claviceps species. Based on several lines of evidence, the N. fumigata gene easM is a logical candidate to encode the festuclavine-hydroxylating enzyme. To test this hypothesis we disrupted easM function by replacing part of its coding sequences with a hygromycin resistance gene and transforming N. fumigata with this construct. High pressure liquid chromatography analysis demonstrated that easM deletion mutants were blocked in the ergot alkaloid pathway at festuclavine, and downstream products were eliminated. An additional alkaloid, proposed to be a prenylated form of festuclavine on the basis of mass spectral data, also accumulated to higher concentrations in the easM knockout. Complementation with the wild-type allele of easM gene restored the ability of the fungus to produce downstream compounds. These results indicate that easM encodes an enzyme required for fumigaclavine B synthesis likely by hydroxylating festuclavine. The festuclavine-accumulating strain of N. fumigata may facilitate future investigations of the biosynthesis of dihydrolysergic acid derivatives, which are derived from festuclavine and are the basis for several important drugs. PMID:26972831

Functional analysis of the gene controlling hydroxylation of festuclavine in the ergot alkaloid pathway of Neosartorya fumigata.

PubMed

Bilovol, Yulia; Panaccione, Daniel G

2016-11-01

Bioactive ergot alkaloids produced by several species of fungi are important molecules in agriculture and medicine. Much of the ergot alkaloid pathway has been elucidated, but a few steps, including the gene controlling hydroxylation of festuclavine to fumigaclavine B, remain unsolved. Festuclavine is a key intermediate in the fumigaclavine branch of the ergot alkaloid pathway of the opportunistic pathogen Neosartorya fumigata and also in the dihydrolysergic acid-based ergot alkaloid pathway of certain Claviceps species. Based on several lines of evidence, the N. fumigata gene easM is a logical candidate to encode the festuclavine-hydroxylating enzyme. To test this hypothesis we disrupted easM function by replacing part of its coding sequences with a hygromycin resistance gene and transforming N. fumigata with this construct. High-pressure liquid chromatography analysis demonstrated that easM deletion mutants were blocked in the ergot alkaloid pathway at festuclavine, and downstream products were eliminated. An additional alkaloid, proposed to be a prenylated form of festuclavine on the basis of mass spectral data, also accumulated to higher concentrations in the easM knockout. Complementation with the wild-type allele of easM gene restored the ability of the fungus to produce downstream compounds. These results indicate that easM encodes an enzyme required for fumigaclavine B synthesis likely by hydroxylating festuclavine. The festuclavine-accumulating strain of N. fumigata may facilitate future investigations of the biosynthesis of dihydrolysergic acid derivatives, which are derived from festuclavine and are the basis for several important drugs.

The impact of care pathways for exacerbation of Chronic Obstructive Pulmonary Disease: rationale and design of a cluster randomized controlled trial

PubMed Central

2010-01-01

Background Hospital treatment of chronic obstructive pulmonary disease (COPD) frequently does not follow published evidences. This lack of adherence can contribute to the high morbidity, mortality and readmissions rates. The European Quality of Care Pathway (EQCP) study on acute exacerbations of COPD (NTC00962468) is undertaken to determine how care pathways (CP) as complex intervention for hospital treatment of COPD affects care variability, adherence to evidence based key interventions and clinical outcomes. Methods An international cluster Randomized Controlled Trial (cRCT) will be performed in Belgium, Italy, Ireland and Portugal. Based on the power analysis, a sample of 40 hospital teams and 398 patients will be included in the study. In the control arm of the study, usual care will be provided. The experimental teams will implement a CP as complex intervention which will include three active components: a formative evaluation of the quality and organization of care, a set of evidence based key interventions, and support on the development and implementation of the CP. The main outcome will be six-month readmission rate. As a secondary endpoint a set of clinical outcome and performance indicators (including care process evaluation and team functioning indicators) will be measured in both groups. Discussion The EQCP study is the first international cRCT on care pathways. The design of the EQCP project is both a research study and a quality improvement project and will include a realistic evaluation framework including process analysis to further understand why and when CP can really work. Trial Registration number NCT00962468 PMID:21092098

Integrated analysis of microRNA and gene expression profiles reveals a functional regulatory module associated with liver fibrosis.

PubMed

Chen, Wei; Zhao, Wenshan; Yang, Aiting; Xu, Anjian; Wang, Huan; Cong, Min; Liu, Tianhui; Wang, Ping; You, Hong

2017-12-15

Liver fibrosis, characterized with the excessive accumulation of extracellular matrix (ECM) proteins, represents the final common pathway of chronic liver inflammation. Ever-increasing evidence indicates microRNAs (miRNAs) dysregulation has important implications in the different stages of liver fibrosis. However, our knowledge of miRNA-gene regulation details pertaining to such disease remains unclear. The publicly available Gene Expression Omnibus (GEO) datasets of patients suffered from cirrhosis were extracted for integrated analysis. Differentially expressed miRNAs (DEMs) and genes (DEGs) were identified using GEO2R web tool. Putative target gene prediction of DEMs was carried out using the intersection of five major algorithms: DIANA-microT, TargetScan, miRanda, PICTAR5 and miRWalk. Functional miRNA-gene regulatory network (FMGRN) was constructed based on the computational target predictions at the sequence level and the inverse expression relationships between DEMs and DEGs. DAVID web server was selected to perform KEGG pathway enrichment analysis. Functional miRNA-gene regulatory module was generated based on the biological interpretation. Internal connections among genes in liver fibrosis-related module were determined using String database. MiRNA-gene regulatory modules related to liver fibrosis were experimentally verified in recombinant human TGFβ1 stimulated and specific miRNA inhibitor treated LX-2 cells. We totally identified 85 and 923 dysregulated miRNAs and genes in liver cirrhosis biopsy samples compared to their normal controls. All evident miRNA-gene pairs were identified and assembled into FMGRN which consisted of 990 regulations between 51 miRNAs and 275 genes, forming two big sub-networks that were defined as down-network and up-network, respectively. KEGG pathway enrichment analysis revealed that up-network was prominently involved in several KEGG pathways, in which "Focal adhesion", "PI3K-Akt signaling pathway" and "ECM-receptor interaction" were remarked significant (adjusted p<0.001). Genes enriched in these pathways coupled with their regulatory miRNAs formed a functional miRNA-gene regulatory module that contains 7 miRNAs, 22 genes and 42 miRNA-gene connections. Gene interaction analysis based on String database revealed that 8 out of 22 genes were highly clustered. Finally, we experimentally confirmed a functional regulatory module containing 5 miRNAs (miR-130b-3p, miR-148a-3p, miR-345-5p, miR-378a-3p, and miR-422a) and 6 genes (COL6A1, COL6A2, COL6A3, PIK3R3, COL1A1, CCND2) associated with liver fibrosis. Our integrated analysis of miRNA and gene expression profiles highlighted a functional miRNA-gene regulatory module associated with liver fibrosis, which, to some extent, may provide important clues to better understand the underlying pathogenesis of liver fibrosis. Copyright © 2017. Published by Elsevier B.V.

Reconstructing biochemical pathways from time course data.

PubMed

Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago

2007-03-01

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Colored Petri net modeling and simulation of signal transduction pathways.

PubMed

Lee, Dong-Yup; Zimmer, Ralf; Lee, Sang Yup; Park, Sunwon

2006-03-01

Presented herein is a methodology for quantitatively analyzing the complex signaling network by resorting to colored Petri nets (CPN). The mathematical as well as Petri net models for two basic reaction types were established, followed by the extension to a large signal transduction system stimulated by epidermal growth factor (EGF) in an application study. The CPN models based on the Petri net representation and the conservation and kinetic equations were used to examine the dynamic behavior of the EGF signaling pathway. The usefulness of Petri nets is demonstrated for the quantitative analysis of the signal transduction pathway. Moreover, the trade-offs between modeling capability and simulation efficiency of this pathway are explored, suggesting that the Petri net model can be invaluable in the initial stage of building a dynamic model.

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome.

PubMed

Morine, Melissa J; McMonagle, Jolene; Toomey, Sinead; Reynolds, Clare M; Moloney, Aidan P; Gormley, Isobel C; Gaora, Peadar O; Roche, Helen M

2010-10-07

Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways--selenoamino acid metabolism and steroid biosynthesis--illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect. Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease.

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

PubMed Central

2010-01-01

Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p < 0.05), followed by muscle (601 genes) and adipose (16 genes). Results from modified GSEA showed that the high-CLA beef diet affected diverse biological processes across the three tissues, and that the majority of pathway changes reached significance only with the bi-directional test. Combining the liver tissue microarray results with plasma marker data revealed 110 CLA-sensitive genes showing strong canonical correlation with one or more plasma markers of metabolic health, and 9 significantly overrepresented pathways among this set; each of these pathways was also significantly changed by the high-CLA diet. Closer inspection of two of these pathways - selenoamino acid metabolism and steroid biosynthesis - illustrated clear diet-sensitive changes in constituent genes, as well as strong correlations between gene expression and plasma markers of metabolic syndrome independent of the dietary effect. Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of analysis has the potential to generate novel transcriptome-based biomarkers of disease. PMID:20929581

Comparison of subacute effects of two types of pyrethroid insecticides using metabolomics methods.

PubMed

Miao, Jiyan; Wang, Dezhen; Yan, Jin; Wang, Yao; Teng, Miaomiao; Zhou, Zhiqiang; Zhu, Wentao

2017-11-01

In this study, 1 H NMR based metabolomics analysis, LC-MS/MS based serum metabolomics and histopathology techniques were used to investigate the toxic effects of subacute exposure to two types of pyrethroid insecticides bifenthrin and lambda-cyhalothrin in mice. Metabolomic analysis of tissues extracts and serum showed that these two types of pyrethroid insecticides resulted in alterations of metabolites in the liver, kidney and serum of mice. Based on the altered metabolites, several significant pathways were identified, which are associated with gut microbial metabolism, lipid metabolism, nucleotide catabolism, tyrosine metabolism and energy metabolism. The results showed that bifenthrin and lambda-cyhalothrin have similarities in disruption of metabolic pathways in kidney, indicating that the toxicological mechanisms of these two types of insecticides have some likeness to each other. This study may provide novel insight into revealing differences of toxicological mechanisms between these two types of pyrethroid insecticides. Copyright © 2017 Elsevier Inc. All rights reserved.

Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid Inhibitor Screening

PubMed Central

Tyner, Jeffrey W.; Yang, Wayne F.; Bankhead, Armand; Fan, Guang; Fletcher, Luke B.; Bryant, Jade; Glover, Jason M.; Chang, Bill H.; Spurgeon, Stephen E.; Fleming, William H.; Kovacsovics, Tibor; Gotlib, Jason R.; Oh, Stephen T.; Deininger, Michael W.; Zwaan, C. Michel; Den Boer, Monique L.; van den Heuvel-Eibrink, Marry M.; O’Hare, Thomas; Druker, Brian J.; Loriaux, Marc M.

2012-01-01

Kinases are dysregulated in most cancer but the frequency of specific kinase mutations is low, indicating a complex etiology in kinase dysregulation. Here we report a strategy to rapidly identify functionally important kinase targets, irrespective of the etiology of kinase pathway dysregulation, ultimately enabling a correlation of patient genetic profiles to clinically effective kinase inhibitors. Our methodology assessed the sensitivity of primary leukemia patient samples to a panel of 66 small-molecule kinase inhibitors over 3 days. Screening of 151 leukemia patient samples revealed a wide diversity of drug sensitivities, with 70% of the clinical specimens exhibiting hypersensitivity to one or more drugs. From this data set, we developed an algorithm to predict kinase pathway dependence based on analysis of inhibitor sensitivity patterns. Applying this algorithm correctly identified pathway dependence in proof-of-principle specimens with known oncogenes, including a rare FLT3 mutation outside regions covered by standard molecular diagnostic tests. Interrogation of all 151 patient specimens with this algorithm identified a diversity of gene targets and signaling pathways that could aid prioritization of deep sequencing data sets, permitting a cumulative analysis to understand kinase pathway dependence within leukemia subsets. In a proof-of-principle case, we showed that in vitro drug sensitivity could predict both a clinical response and the development of drug resistance. Taken together, our results suggested that drug target scores derived from a comprehensive kinase inhibitor panel could predict pathway dependence in cancer cells while simultaneously identifying potential therapeutic options. PMID:23087056

Alternative Sigma Factor Over-Expression Enables Heterologous Expression of a Type II Polyketide Biosynthetic Pathway in Escherichia coli

PubMed Central

Stevens, David Cole; Conway, Kyle R.; Pearce, Nelson; Villegas-Peñaranda, Luis Roberto; Garza, Anthony G.; Boddy, Christopher N.

2013-01-01

Background Heterologous expression of bacterial biosynthetic gene clusters is currently an indispensable tool for characterizing biosynthetic pathways. Development of an effective, general heterologous expression system that can be applied to bioprospecting from metagenomic DNA will enable the discovery of a wealth of new natural products. Methodology We have developed a new Escherichia coli-based heterologous expression system for polyketide biosynthetic gene clusters. We have demonstrated the over-expression of the alternative sigma factor σ54 directly and positively regulates heterologous expression of the oxytetracycline biosynthetic gene cluster in E. coli. Bioinformatics analysis indicates that σ54 promoters are present in nearly 70% of polyketide and non-ribosomal peptide biosynthetic pathways. Conclusions We have demonstrated a new mechanism for heterologous expression of the oxytetracycline polyketide biosynthetic pathway, where high-level pleiotropic sigma factors from the heterologous host directly and positively regulate transcription of the non-native biosynthetic gene cluster. Our bioinformatics analysis is consistent with the hypothesis that heterologous expression mediated by the alternative sigma factor σ54 may be a viable method for the production of additional polyketide products. PMID:23724102

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ovacik, Meric A.; Sen, Banalata; Euling, Susan Y.

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significancemore » analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.« less

Comparative techno-economic analysis and process design for indirect liquefaction pathways to distillate-range fuels via biomass-derived oxygenated intermediates upgrading

DOE PAGES

Tan, Eric C. D.; Snowden-Swan, Lesley J.; Talmadge, Michael; ...

2016-09-27

This paper presents a comparative techno-economic analysis (TEA) of five conversion pathways from biomass to gasoline-, jet-, and diesel-range hydrocarbons via indirect liquefaction with a specific focus on pathways utilizing oxygenated intermediates. The four emerging pathways of interest are compared with one conventional pathway (Fischer-Tropsch) for the production of the hydrocarbon blendstocks. The processing steps of the four emerging pathways include biomass-to-syngas via indirect gasification, syngas clean-up, conversion of syngas to alcohols/oxygenates followed by conversion of alcohols/oxygenates to hydrocarbon blendstocks via dehydration, oligomerization, and hydrogenation. Conversion of biomass-derived syngas to oxygenated intermediates occurs via three different pathways, producing: (i) mixedmore » alcohols over a MoS 2 catalyst, (ii) mixed oxygenates (a mixture of C 2+ oxygenated compounds, predominantly ethanol, acetic acid, acetaldehyde, ethyl acetate) using an Rh-based catalyst, and (iii) ethanol from syngas fermentation. This is followed by the conversion of oxygenates/alcohols to fuel-range olefins in two approaches: (i) mixed alcohols/ethanol to 1-butanol rich mixture via Guerbet reaction, followed by alcohol dehydration, oligomerization, and hydrogenation, and (ii) mixed oxygenates/ethanol to isobutene rich mixture and followed by oligomerization and hydrogenation. The design features a processing capacity of 2000 tonnes/day (2205 short tons) of dry biomass. The minimum fuel selling prices (MFSPs) for the four developing pathways range from 3.40 dollars to 5.04 dollars per gasoline-gallon equivalent (GGE), in 2011 US dollars. Sensitivity studies show that MFSPs can be improved with co-product credits and are comparable to the commercial Fischer-Tropsch benchmark ($3.58/GGE). Altogether, this comparative TEA study documents potential economics for the developmental biofuel pathways via mixed oxygenates.« less

Comparative techno-economic analysis and process design for indirect liquefaction pathways to distillate-range fuels via biomass-derived oxygenated intermediates upgrading

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Eric C. D.; Snowden-Swan, Lesley J.; Talmadge, Michael

This paper presents a comparative techno-economic analysis (TEA) of five conversion pathways from biomass to gasoline-, jet-, and diesel-range hydrocarbons via indirect liquefaction with a specific focus on pathways utilizing oxygenated intermediates. The four emerging pathways of interest are compared with one conventional pathway (Fischer-Tropsch) for the production of the hydrocarbon blendstocks. The processing steps of the four emerging pathways include biomass-to-syngas via indirect gasification, syngas clean-up, conversion of syngas to alcohols/oxygenates followed by conversion of alcohols/oxygenates to hydrocarbon blendstocks via dehydration, oligomerization, and hydrogenation. Conversion of biomass-derived syngas to oxygenated intermediates occurs via three different pathways, producing: (i) mixedmore » alcohols over a MoS 2 catalyst, (ii) mixed oxygenates (a mixture of C 2+ oxygenated compounds, predominantly ethanol, acetic acid, acetaldehyde, ethyl acetate) using an Rh-based catalyst, and (iii) ethanol from syngas fermentation. This is followed by the conversion of oxygenates/alcohols to fuel-range olefins in two approaches: (i) mixed alcohols/ethanol to 1-butanol rich mixture via Guerbet reaction, followed by alcohol dehydration, oligomerization, and hydrogenation, and (ii) mixed oxygenates/ethanol to isobutene rich mixture and followed by oligomerization and hydrogenation. The design features a processing capacity of 2000 tonnes/day (2205 short tons) of dry biomass. The minimum fuel selling prices (MFSPs) for the four developing pathways range from 3.40 dollars to 5.04 dollars per gasoline-gallon equivalent (GGE), in 2011 US dollars. Sensitivity studies show that MFSPs can be improved with co-product credits and are comparable to the commercial Fischer-Tropsch benchmark ($3.58/GGE). Altogether, this comparative TEA study documents potential economics for the developmental biofuel pathways via mixed oxygenates.« less

[Strategies of elucidation of biosynthetic pathways of natural products].

PubMed

Zou, Li-Qiu; Kuang, Xue-Jun; Sun, Chao; Chen, Shi-Lin

2016-11-01

Elucidation of the biosynthetic pathways of natural products is not only the major goal of herb genomics, but also the solid foundation of synthetic biology of natural products. Here, this paper reviewed recent advance in this field and put forward strategies to elucidate the biosynthetic pathway of natural products. Firstly, a proposed biosynthetic pathway should be set up based on well-known knowledge about chemical reactions and information on the identified compounds, as well as studies with isotope tracer. Secondly, candidate genes possibly involved in the biosynthetic pathway were screened out by co-expression analysis and/or gene cluster mining. Lastly, all the candidate genes were heterologously expressed in the host and then the enzyme involved in the biosynthetic pathway was characterized by activity assay. Sometimes, the function of the enzyme in the original plant could be further studied by RNAi or VIGS technology. Understanding the biosynthetic pathways of natural products will contribute to supply of new leading compounds by synthetic biology and provide "functional marker" for herbal molecular breeding, thus but boosting the development of traditional Chinese medicine agriculture. Copyright© by the Chinese Pharmaceutical Association.

Phosphoketolase Pathway for Xylose Catabolism in Clostridium acetobutylicum Revealed by 13C Metabolic Flux Analysis

PubMed Central

Liu, Lixia; Zhang, Lei; Tang, Wei; Gu, Yang; Hua, Qiang; Yang, Sheng; Jiang, Weihong

2012-01-01

Solvent-producing clostridia are capable of utilizing pentose sugars, including xylose and arabinose; however, little is known about how pentose sugars are catabolized through the metabolic pathways in clostridia. In this study, we identified the xylose catabolic pathways and quantified their fluxes in Clostridium acetobutylicum based on [1-13C]xylose labeling experiments. The phosphoketolase pathway was found to be active, which contributed up to 40% of the xylose catabolic flux in C. acetobutylicum. The split ratio of the phosphoketolase pathway to the pentose phosphate pathway was markedly increased when the xylose concentration in the culture medium was increased from 10 to 20 g liter−1. To our knowledge, this is the first time that the in vivo activity of the phosphoketolase pathway in clostridia has been revealed. A phosphoketolase from C. acetobutylicum was purified and characterized, and its activity with xylulose-5-P was verified. The phosphoketolase was overexpressed in C. acetobutylicum, which resulted in slightly increased xylose consumption rates during the exponential growth phase and a high level of acetate accumulation. PMID:22865845

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS.

PubMed

Durmaz, Arda; Henderson, Tim A D; Brubaker, Douglas; Bebek, Gurkan

2017-01-01

Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies. In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups. These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples. Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.

Epinephrine-Induced and Antiapoptotic Signaling in Prostate Cancer

DTIC Science & Technology

2009-05-01

Phosphorylation of ectopically expressed HA-BAD mirrors endogenous BAD phosphorylation, thus analysis of HA-BAD allows adequately interpret modifications...subjected to emotional stress (Fig.2 A). However, more extensive analysis of CREB phosphorylation in C42Luc xenografts showed that in some cases...data indicate that assignment of tumor samples for analysis of signaling pathways should be done based on blood epinephrine measurements. Given

Exploring pathway interactions in insulin resistant mouse liver

PubMed Central

2011-01-01

Background Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset. Results We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar. Conclusions Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well. PMID:21843341

Ab Initio-Based Kinetic Modeling for the Design of Molecular Catalysts: The Case of H 2 Production Electrocatalysts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ho, Ming-Hsun; Rousseau, Roger; Roberts, John A. S.

2015-09-04

Design of fast, efficient electrocatalysts for energy production and energy utilization requires a systematic approach to predict and tune the energetics of reaction intermediates and the kinetic barriers between them as well as to tune reaction conditions (e.g., concentration of reactants, acidity of the reaction medium, and applied electric potential). Thermodynamics schemes based on the knowledge of pKa values, hydride donor ability, redox potentials, and other relevant thermodynamic properties have been demonstrated to be very effective for exploring possible reaction pathways. We seek to identify high-energy intermediates, which may represent a catalytic bottleneck, and low-energy intermediates, which may represent amore » thermodynamic sink. In this study, working on a well-established Ni-based bioinspired electrocatalyst for H2 production, we performed a detailed kinetic analysis of the catalytic pathways to assess the limitations of our current (standard state) thermodynamic analysis with respect to prediction of optimal catalyst performance. To this end, we developed a microkinetic model based on extensive ab initio simulations. The model was validated against available experimental data, and it reproduces remarkably well the observed turnover rate as a function of the acid concentration and catalytic conditions, providing valuable information on the main factors limiting catalysis. Using this kinetic analysis as a reference, we show that indeed a purely thermodynamic analysis of the possible reaction pathways provides us with valuable information, such as a qualitative picture of the species involved during catalysis, identification of the possible branching points, and the origin of the observed overpotential, which are critical insights for electrocatalyst design. However, a significant limitation of this approach is understanding how these insights relate to rate, which is an equally critical piece of information. Taking our analysis a step further, we show that the kinetic model can easily be extended to different catalytic conditions by using linear free energy relationships for activation barriers based on simple thermodynamics quantities, such as pKa values. We also outline a possible procedure to extend it to other catalytic platforms, making it a general and effective way to design catalysts with improved performance.« less

BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine.

PubMed

Liu, Zhongyang; Guo, Feifei; Wang, Yong; Li, Chun; Zhang, Xinlei; Li, Honglei; Diao, Lihong; Gu, Jiangyong; Wang, Wei; Li, Dong; He, Fuchu

2016-02-16

Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, is gaining more and more attention and application worldwide. And TCM-based new drug development, especially for the treatment of complex diseases is promising. However, owing to the TCM's diverse ingredients and their complex interaction with human body, it is still quite difficult to uncover its molecular mechanism, which greatly hinders the TCM modernization and internationalization. Here we developed the first online Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM). Its main functions include 1) TCM ingredients' target prediction; 2) functional analyses of targets including biological pathway, Gene Ontology functional term and disease enrichment analyses; 3) the visualization of ingredient-target-pathway/disease association network and KEGG biological pathway with highlighted targets; 4) comparison analysis of multiple TCMs. Finally, we applied BATMAN-TCM to Qishen Yiqi dripping Pill (QSYQ) and combined with subsequent experimental validation to reveal the functions of renin-angiotensin system responsible for QSYQ's cardioprotective effects for the first time. BATMAN-TCM will contribute to the understanding of the "multi-component, multi-target and multi-pathway" combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCM's molecular mechanism. BATMAN-TCM is available at http://bionet.ncpsb.org/batman-tcm.

Novel degradation pathway and kinetic analysis for buprofezin removal by newly isolated Bacillus sp.

PubMed

Wang, Guangli; Xu, Dayong; Xiong, Minghua; Zhang, Hui; Li, Feng; Liu, Yuan

2016-09-15

Given the intensive and widespread application of the pesticide, buprofezin, its environmental residues potentially pose a problem; yet little is known about buprofezin's kinetic and metabolic behaviors. In this study, a novel gram-positive strain, designated BF-5, isolated from aerobic activated sludge, was found to be capable of metabolizing buprofezin as its sole energy, carbon, and nitrogen source. Based on its physiological and biochemical characteristics, other aspects of its phenotype, and a phylogenetic analysis, strain BF-5 was identified as Bacillus sp. This study investigated the effect of culture conditions on bacterial growth and substrate degradation, such as pH, temperature, initial concentration, different nitrogen source, and additional nitrogen sources as co-substrates. The degradation rate parameters, qmax, Ks, Ki and Sm were determined to be 0.6918 h(-1), 105.4 mg L(-1), 210.5 mg L(-1), and 148.95 mg L(-1) respectively. The capture of unpublished potential metabolites by gas chromatography-mass spectrometry (GC-MS) analysis has led to the proposal of a novel degradation pathway. Taken together, our results clarify buprofezin's biodegradation pathway(s) and highlight the promising potential of strain BF-5 in bioremediation of buprofezin-contaminated environments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Separate enrichment analysis of pathways for up- and downregulated genes.

PubMed

Hong, Guini; Zhang, Wenjing; Li, Hongdong; Shen, Xiaopei; Guo, Zheng

2014-03-06

Two strategies are often adopted for enrichment analysis of pathways: the analysis of all differentially expressed (DE) genes together or the analysis of up- and downregulated genes separately. However, few studies have examined the rationales of these enrichment analysis strategies. Using both microarray and RNA-seq data, we show that gene pairs with functional links in pathways tended to have positively correlated expression levels, which could result in an imbalance between the up- and downregulated genes in particular pathways. We then show that the imbalance could greatly reduce the statistical power for finding disease-associated pathways through the analysis of all-DE genes. Further, using gene expression profiles from five types of tumours, we illustrate that the separate analysis of up- and downregulated genes could identify more pathways that are really pertinent to phenotypic difference. In conclusion, analysing up- and downregulated genes separately is more powerful than analysing all of the DE genes together.

13C-based metabolic flux analysis: fundamentals and practice.

PubMed

Yang, Tae Hoon

2013-01-01

Isotope-based metabolic flux analysis is one of the emerging technologies applied to system level metabolic phenotype characterization in metabolic engineering. Among the developed approaches, (13)C-based metabolic flux analysis has been established as a standard tool and has been widely applied to quantitative pathway characterization of diverse biological systems. To implement (13)C-based metabolic flux analysis in practice, comprehending the underlying mathematical and computational modeling fundamentals is of importance along with carefully conducted experiments and analytical measurements. Such knowledge is also crucial when designing (13)C-labeling experiments and properly acquiring key data sets essential for in vivo flux analysis implementation. In this regard, the modeling fundamentals of (13)C-labeling systems and analytical data processing are the main topics we will deal with in this chapter. Along with this, the relevant numerical optimization techniques are addressed to help implementation of the entire computational procedures aiming at (13)C-based metabolic flux analysis in vivo.

In vitro FTIR microspectroscopy analysis of primary oral squamous carcinoma cells treated with cisplatin and 5-fluorouracil: a new spectroscopic approach for studying the drug-cell interaction.

PubMed

Giorgini, Elisabetta; Sabbatini, Simona; Rocchetti, Romina; Notarstefano, Valentina; Rubini, Corrado; Conti, Carla; Orilisi, Giulia; Mitri, Elisa; Bedolla, Diana E; Vaccari, Lisa

2018-06-22

In the present study, human primary oral squamous carcinoma cells treated with cisplatin and 5-fluorouracil were analyzed, for the first time, by in vitro FTIR Microspectroscopy (FTIRM), to improve the knowledge on the biochemical pathways activated by these two chemotherapy drugs. To date, most of the studies regarding FTIRM cellular analysis have been executed on fixed cells from immortalized cell lines. FTIRM analysis performed on primary tumor cells under controlled hydrated conditions provides more reliable information on the biochemical processes occurring in in vivo tumor cells. This spectroscopic analysis allows to get on the same sample and at the same time an overview of the composition and structure of the most remarkable cellular components. In vitro FTIRM analysis of primary oral squamous carcinoma cells evidenced a time-dependent drug-specific cellular response, also including apoptosis triggering. Furthermore, the univariate and multivariate analyses of IR data evidenced meaningful spectroscopic differences ascribable to alterations affecting cellular proteins, lipids and nucleic acids. These findings suggest for the two drugs different pathways and extents of cellular damage, not provided by conventional cell-based assays (MTT assay and image-based cytometry).

Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis.

PubMed

Yi, Ming; Mudunuri, Uma; Che, Anney; Stephens, Robert M

2009-06-29

One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php.

Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

PubMed

Muetze, Tanja; Goenawan, Ivan H; Wiencko, Heather L; Bernal-Llinares, Manuel; Bryan, Kenneth; Lynn, David J

2016-01-01

Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).

Identifying osteosarcoma metastasis associated genes by weighted gene co-expression network analysis (WGCNA).

PubMed

Tian, Honglai; Guan, Donghui; Li, Jianmin

2018-06-01

Osteosarcoma (OS), the most common malignant bone tumor, accounts for the heavy healthy threat in the period of children and adolescents. OS occurrence usually correlates with early metastasis and high death rate. This study aimed to better understand the mechanism of OS metastasis.Based on Gene Expression Omnibus (GEO) database, we downloaded 4 expression profile data sets associated with OS metastasis, and selected differential expressed genes. Weighted gene co-expression network analysis (WGCNA) approach allowed us to investigate the most OS metastasis-correlated module. Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to give annotation of selected OS metastasis-associated genes.We select 897 differential expressed genes from OS metastasis and OS non-metastasis groups. Based on these selected genes, WGCNA further explored 142 genes included in the most OS metastasis-correlated module. Gene Ontology functional and KEGG pathway enrichment analyses showed that significantly OS metastasis-associated genes were involved in pathway correlated with insulin-like growth factor binding.Our research figured out several potential molecules participating in metastasis process and factors acting as biomarker. With this study, we could better explore the mechanism of OS metastasis and further discover more therapy targets.

An image analysis toolbox for high-throughput C. elegans assays

PubMed Central

Wählby, Carolina; Kamentsky, Lee; Liu, Zihan H.; Riklin-Raviv, Tammy; Conery, Annie L.; O’Rourke, Eyleen J.; Sokolnicki, Katherine L.; Visvikis, Orane; Ljosa, Vebjorn; Irazoqui, Javier E.; Golland, Polina; Ruvkun, Gary; Ausubel, Frederick M.; Carpenter, Anne E.

2012-01-01

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays of C. elegans for the study of diverse biological pathways relevant to human disease. PMID:22522656

A study of pH-dependent photodegradation of amiloride by a multivariate curve resolution approach to combined kinetic and acid-base titration UV data.

PubMed

De Luca, Michele; Ioele, Giuseppina; Mas, Sílvia; Tauler, Romà; Ragno, Gaetano

2012-11-21

Amiloride photostability at different pH values was studied in depth by applying Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to the UV spectrophotometric data from drug solutions exposed to stressing irradiation. Resolution of all degradation photoproducts was possible by simultaneous spectrophotometric analysis of kinetic photodegradation and acid-base titration experiments. Amiloride photodegradation showed to be strongly dependent on pH. Two hard modelling constraints were sequentially used in MCR-ALS for the unambiguous resolution of all the species involved in the photodegradation process. An amiloride acid-base system was defined by using the equilibrium constraint, and the photodegradation pathway was modelled taking into account the kinetic constraint. The simultaneous analysis of photodegradation and titration experiments revealed the presence of eight different species, which were differently distributed according to pH and time. Concentration profiles of all the species as well as their pure spectra were resolved and kinetic rate constants were estimated. The values of rate constants changed with pH and under alkaline conditions the degradation pathway and photoproducts also changed. These results were compared to those obtained by LC-MS analysis from drug photodegradation experiments. MS analysis allowed the identification of up to five species and showed the simultaneous presence of more than one acid-base equilibrium.

Global Gene Expression Profiling in Omental Adipose Tissue of Morbidly Obese Diabetic African Americans.

PubMed

Doumatey, Ayo P; Xu, Huichun; Huang, Hanxia; Trivedi, Niraj S; Lei, Lin; Elkahloun, Abdel; Adeyemo, Adebowale; Rotimi, Charles N

2015-06-01

Adipose tissues play important role in the pathophysiology of obesity-related diseases including type 2 diabetes (T2D). To describe gene expression patterns and functional pathways in obesity-related T2D, we performed global transcript profiling of omental adipose tissue (OAT) in morbidly obese individuals with or without T2D. Twenty morbidly obese (mean BMI: about 54 kg/m 2 ) subjects were studied, including 14 morbidly obese individuals with T2D (cases) and 6 morbidly obese individuals without T2D (reference group). Gene expression profiling was performed using the Affymetrix U133 Plus 2.0 human genome expression array. Analysis of covariance was performed to identify differentially expressed genes (DEGs). Bioinformatics tools including PANTHER and Ingenuity Pathway Analysis (IPA) were applied to the DEGs to determine biological functions, networks and canonical pathways that were overrepresented in these individuals. At an absolute fold-change threshold of 2 and false discovery rate (FDR) < 0.05, 68 DEGs were identified in cases compared to the reference group. Myosin X (MYO10) and transforming growth factor beta regulator 1 (TBRG1) were upregulated. MYO10 encodes for an actin-based motor protein that has been associated with T2D. Telomere extension by telomerase ( HNRNPA1, TNKS2 ), D-myo-inositol (1, 4, 5)-trisphosphate biosynthesis (PIP5K1A, PIP4K2A), and regulation of actin-based motility by Rho (ARPC3) were the most significant canonical pathways and overlay with T2D signaling pathway. Upstream regulator analysis predicted 5 miRNAs (miR-320b, miR-381-3p, miR-3679-3p, miR-494-3p, and miR-141-3p,) as regulators of the expression changes identified. This study identified a number of transcripts and miRNAs in OAT as candidate novel players in the pathophysiology of T2D in African Americans.

Viral Small-RNA Analysis of Bombyx mori Larval Midgut during Persistent and Pathogenic Cytoplasmic Polyhedrosis Virus Infection.

PubMed

Zografidis, Aris; Van Nieuwerburgh, Filip; Kolliopoulou, Anna; Apostolou-Karampelis, Konstantinos; Head, Steven R; Deforce, Dieter; Smagghe, Guy; Swevers, Luc

2015-11-01

The lepidopteran innate immune response against RNA viruses remains poorly understood, while in other insects several studies have highlighted an essential role for the exo-RNAi pathway in combating viral infection. Here, by using deep-sequencing technology for viral small-RNA (vsRNA) assessment, we provide evidence that exo-RNAi is operative in the silkworm Bombyx mori against both persistent and pathogenic infection of B. mori cytoplasmic polyhedrosis virus (BmCPV) which is characterized by a segmented double-stranded RNA (dsRNA) genome. Further, we show that Dicer-2 predominantly targets viral dsRNA and produces 20-nucleotide (nt) vsRNAs, whereas an additional pathway is responsive to viral mRNA derived from segment 10. Importantly, vsRNA distributions, which define specific hot and cold spot profiles for each viral segment, to a considerable degree overlap between Dicer-2-related (19 to 21 nt) and Dicer-2-unrelated vsRNAs, suggesting a common origin for these profiles. We found a degenerate motif significantly enriched at the cut sites of vsRNAs of various lengths which link an unknown RNase to the origins of vsRNAs biogenesis and distribution. Accordingly, the indicated RNase activity may be an important early factor for the host's antiviral defense in Lepidoptera. This work contributes to the elucidation of the lepidopteran antiviral response against infection of segmented double-stranded RNA (dsRNA) virus (CPV; Reoviridae) and highlights the importance of viral small-RNA (vsRNA) analysis for getting insights into host-pathogen interactions. Three vsRNA pathways are implicated in antiviral defense. For dsRNA, two pathways are proposed, either based on Dicer-2 cleavage to generate 20-nucleotide vsRNAs or based on the activity of an uncharacterized endo-RNase that cleaves the viral RNA substrate at a degenerate motif. The analysis also indicates the existence of a degradation pathway that targets the positive strand of segment 10. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Viral Small-RNA Analysis of Bombyx mori Larval Midgut during Persistent and Pathogenic Cytoplasmic Polyhedrosis Virus Infection

PubMed Central

Van Nieuwerburgh, Filip; Kolliopoulou, Anna; Apostolou-Karampelis, Konstantinos; Head, Steven R.; Deforce, Dieter; Smagghe, Guy; Swevers, Luc

2015-01-01

ABSTRACT The lepidopteran innate immune response against RNA viruses remains poorly understood, while in other insects several studies have highlighted an essential role for the exo-RNAi pathway in combating viral infection. Here, by using deep-sequencing technology for viral small-RNA (vsRNA) assessment, we provide evidence that exo-RNAi is operative in the silkworm Bombyx mori against both persistent and pathogenic infection of B. mori cytoplasmic polyhedrosis virus (BmCPV) which is characterized by a segmented double-stranded RNA (dsRNA) genome. Further, we show that Dicer-2 predominantly targets viral dsRNA and produces 20-nucleotide (nt) vsRNAs, whereas an additional pathway is responsive to viral mRNA derived from segment 10. Importantly, vsRNA distributions, which define specific hot and cold spot profiles for each viral segment, to a considerable degree overlap between Dicer-2-related (19 to 21 nt) and Dicer-2-unrelated vsRNAs, suggesting a common origin for these profiles. We found a degenerate motif significantly enriched at the cut sites of vsRNAs of various lengths which link an unknown RNase to the origins of vsRNAs biogenesis and distribution. Accordingly, the indicated RNase activity may be an important early factor for the host's antiviral defense in Lepidoptera. IMPORTANCE This work contributes to the elucidation of the lepidopteran antiviral response against infection of segmented double-stranded RNA (dsRNA) virus (CPV; Reoviridae) and highlights the importance of viral small-RNA (vsRNA) analysis for getting insights into host-pathogen interactions. Three vsRNA pathways are implicated in antiviral defense. For dsRNA, two pathways are proposed, either based on Dicer-2 cleavage to generate 20-nucleotide vsRNAs or based on the activity of an uncharacterized endo-RNase that cleaves the viral RNA substrate at a degenerate motif. The analysis also indicates the existence of a degradation pathway that targets the positive strand of segment 10. PMID:26339065

Modeling central metabolism and energy biosynthesis across microbial life

DOE PAGES

Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal; ...

2016-08-08

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edirisinghe, Janaka N.; Weisenhorn, Pamela; Conrad, Neal

Here, automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. As a result, to overcome this challenge, we developed methods and tools to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of modelmore » organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. In conclusion, we predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.« less

Modeling central metabolism and energy biosynthesis across microbial life.

PubMed

Edirisinghe, Janaka N; Weisenhorn, Pamela; Conrad, Neal; Xia, Fangfang; Overbeek, Ross; Stevens, Rick L; Henry, Christopher S

2016-08-08

Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Identification of differentially expressed genes and pathways for intramuscular fat deposition in pectoralis major tissues of fast-and slow-growing chickens.

PubMed

Cui, Huan-Xian; Liu, Ran-Ran; Zhao, Gui-Ping; Zheng, Mai-Qing; Chen, Ji-Lan; Wen, Jie

2012-05-30

Intramuscular fat (IMF) is one of the important factors influencing meat quality, however, for chickens, the molecular regulatory mechanisms underlying this trait have not yet been determined. In this study, a systematic identification of candidate genes and new pathways related to IMF deposition in chicken breast tissue has been made using gene expression profiles of two distinct breeds: Beijing-you (BJY), a slow-growing Chinese breed possessing high meat quality and Arbor Acres (AA), a commercial fast-growing broiler line. Agilent cDNA microarray analyses were conducted to determine gene expression profiles of breast muscle sampled at different developmental stages of BJY and AA chickens. Relative to d 1 when there is no detectable IMF, breast muscle at d 21, d 42, d 90 and d 120 (only for BJY) contained 1310 differentially expressed genes (DEGs) in BJY and 1080 DEGs in AA. Of these, 34-70 DEGs related to lipid metabolism or muscle development processes were examined further in each breed based on Gene Ontology (GO) analysis. The expression of several DEGs was correlated, positively or negatively, with the changing patterns of lipid content or breast weight across the ages sampled, indicating that those genes may play key roles in these developmental processes. In addition, based on KEGG pathway analysis of DEGs in both BJY and AA chickens, it was found that in addition to pathways affecting lipid metabolism (pathways for MAPK & PPAR signaling), cell junction-related pathways (tight junction, ECM-receptor interaction, focal adhesion, regulation of actin cytoskeleton), which play a prominent role in maintaining the integrity of tissues, could contribute to the IMF deposition. The results of this study identified potential candidate genes associated with chicken IMF deposition and imply that IMF deposition in chicken breast muscle is regulated and mediated not only by genes and pathways related to lipid metabolism and muscle development, but also by others involved in cell junctions. These findings establish the groundwork and provide new clues for deciphering the molecular mechanisms underlying IMF deposition in poultry. Further studies at the translational and posttranslational level are now required to validate the genes and pathways identified here.

Diet and Colorectal Cancer: Analysis of a Candidate Pathway Using SNPS, Haplotypes, and Multi-Gene Assessment

PubMed Central

Slattery, Martha L.; Lundgreen, Abbie; Herrick, Jennifer S.; Caan, Bette J.; Potter, John D.; Wolff, Roger K.

2012-01-01

There is considerable biologic plausibility to the hypothesis that genetic variability in pathways involved in insulin signaling and energy homeostasis may modulate dietary risk associated with colorectal cancer. We utilized data from 2 population-based case-control studies of colon (n = 1,574 cases, 1,970 controls) and rectal (n = 791 cases, 999 controls) cancer to evaluate genetic variation in candidate SNPs identified from 9 genes in a candidate pathway: PDK1, RP6KA1, RPS6KA2, RPS6KB1, RPS6KB2, PTEN, FRAP1 (mTOR), TSC1, TSC2, Akt1, PIK3CA, and PRKAG2 with dietary intake of total energy, carbohydrates, fat, and fiber. We employed SNP, haplotype, and multiple-gene analysis to evaluate associations. PDK1 interacted with dietary fat for both colon and rectal cancer and with dietary carbohydrates for colon cancer. Statistically significant interaction with dietary carbohydrates and rectal cancer was detected by haplotype analysis of PDK1. Evaluation of dietary interactions with multiple genes in this candidate pathway showed several interactions with pairs of genes: Akt1 and PDK1, PDK1 and PTEN, PDK1 and TSC1, and PRKAG2 and PTEN. Analyses show that genetic variation influences risk of colorectal cancer associated with diet and illustrate the importance of evaluating dietary interactions beyond the level of single SNPs or haplotypes when a biologically relevant candidate pathway is examined. PMID:21999454

Structure and Dynamics Analysis on Plexin-B1 Rho GTPase Binding Domain as a Monomer and Dimer

PubMed Central

2015-01-01

Plexin-B1 is a single-pass transmembrane receptor. Its Rho GTPase binding domain (RBD) can associate with small Rho GTPases and can also self-bind to form a dimer. In total, more than 400 ns of NAMD molecular dynamics simulations were performed on RBD monomer and dimer. Different analysis methods, such as root mean squared fluctuation (RMSF), order parameters (S2), dihedral angle correlation, transfer entropy, principal component analysis, and dynamical network analysis, were carried out to characterize the motions seen in the trajectories. RMSF results show that after binding, the L4 loop becomes more rigid, but the L2 loop and a number of residues in other regions become slightly more flexible. Calculating order parameters (S2) for CH, NH, and CO bonds on both backbone and side chain shows that the L4 loop becomes essentially rigid after binding, but part of the L1 loop becomes slightly more flexible. Backbone dihedral angle cross-correlation results show that loop regions such as the L1 loop including residues Q25 and G26, the L2 loop including residue R61, and the L4 loop including residues L89–R91, are highly correlated compared to other regions in the monomer form. Analysis of the correlated motions at these residues, such as Q25 and R61, indicate two signal pathways. Transfer entropy calculations on the RBD monomer and dimer forms suggest that the binding process should be driven by the L4 loop and C-terminal. However, after binding, the L4 loop functions as the motion responder. The signal pathways in RBD were predicted based on a dynamical network analysis method using the pathways predicted from the dihedral angle cross-correlation calculations as input. It is found that the shortest pathways predicted from both inputs can overlap, but signal pathway 2 (from F90 to R61) is more dominant and overlaps all of the routes of pathway 1 (from F90 to P111). This project confirms the allosteric mechanism in signal transmission inside the RBD network, which was in part proposed in the previous experimental study. PMID:24901636

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage

PubMed Central

Huang, Ruili; Lin, Ja-An; Sedykh, Alexander; Zhao, Jinghua; Tice, Raymond R.; Paules, Richard S.; Xia, Menghang; Auerbach, Scott S.

2017-01-01

Cytotoxicity is a commonly used in vitro endpoint for evaluating chemical toxicity. In support of the U.S. Tox21 screening program, the cytotoxicity of ~10K chemicals was interrogated at 0, 8, 16, 24, 32, & 40 hours of exposure in a concentration dependent fashion in two cell lines (HEK293, HepG2) using two multiplexed, real-time assay technologies. One technology measures the metabolic activity of cells (i.e., cell viability, glo) while the other evaluates cell membrane integrity (i.e., cell death, flor). Using glo technology, more actives and greater temporal variations were seen in HEK293 cells, while results for the flor technology were more similar across the two cell types. Chemicals were grouped into classes based on their cytotoxicity kinetics profiles and these classes were evaluated for their associations with activity in the Tox21 nuclear receptor and stress response pathway assays. Some pathways, such as the activation of H2AX, were associated with the fast-responding cytotoxicity classes, while others, such as activation of TP53, were associated with the slow-responding cytotoxicity classes. By clustering pathways based on their degree of association to the different cytotoxicity kinetics labels, we identified clusters of pathways where active chemicals presented similar kinetics of cytotoxicity. Such linkages could be due to shared underlying biological processes between pathways, for example, activation of H2AX and heat shock factor. Others involving nuclear receptor activity are likely due to shared chemical structures rather than pathway level interactions. Based on the linkage between androgen receptor antagonism and Nrf2 activity, we surmise that a subclass of androgen receptor antagonists cause cytotoxicity via oxidative stress that is associated with Nrf2 activation. In summary, the real-time cytotoxicity screen provides informative chemical cytotoxicity kinetics data related to their cytotoxicity mechanisms, and with our analysis, it is possible to formulate mechanism-based hypotheses on the cytotoxic properties of the tested chemicals. PMID:28531190

RiboFACSeq: A new method for investigating metabolic and transport pathways in bacterial cells by combining a riboswitch-based sensor, fluorescence-activated cell sorting and next-generation sequencing

PubMed Central

Li, Yingfu

2017-01-01

The elucidation of the cellular processes involved in vitamin and cofactor biosynthesis is a challenging task. The conventional approaches to these investigations rely on the discovery and purification of the products (i.e proteins and metabolites) of a particular transport or biosynthetic pathway, prior to their subsequent analysis. However, the purification of low-abundance proteins or metabolites is a formidable undertaking that presents considerable technical challenges. As a solution, we present an alternative approach to such studies that circumvents the purification step. The proposed approach takes advantage of: (1) the molecular detection capabilities of a riboswitch-based sensor to detect the cellular levels of its cognate molecule, as a means to probe the integrity of the transport and biosynthetic pathways of the target molecule in cells, (2) the high-throughput screening ability of fluorescence-activated cell sorters to isolate cells in which only these specific pathways are disrupted, and (3) the ability of next-generation sequencing to quickly identify the genes of the FACS-sorted populations. This approach was named “RiboFACSeq”. Following their identification by RiboFACSeq, the role of these genes in the presumed pathway needs to be verified through appropriate functional assays. To demonstrate the utility of our approach, an adenosylcobalamin (AdoCbl)-responsive riboswitch-based sensor was used in this study to demonstrate that RiboFACSeq can be used to track and sort cells carrying genetic mutations in known AdoCbl transport and biosynthesis genes with desirable sensitivity and specificity. This method could potentially be used to elucidate any pathway of interest, as long as a suitable riboswitch-based sensor can be created. We believe that RiboFACSeq would be especially useful for the elucidation of biological pathways in which the proteins and/or their metabolites are present at very low physiological concentrations in cells, as is the case with vitamin and cofactor biosynthesis. PMID:29211762

Oleic acid-enhanced transdermal delivery pathways of fluorescent nanoparticles

NASA Astrophysics Data System (ADS)

Lo, Wen; Ghazaryan, Ara; Tso, Chien-Hsin; Hu, Po-Sheng; Chen, Wei-Liang; Kuo, Tsung-Rong; Lin, Sung-Jan; Chen, Shean-Jen; Chen, Chia-Chun; Dong, Chen-Yuan

2012-05-01

Transdermal delivery of nanocarriers provides an alternative pathway to transport therapeutic agents, alleviating pain, improving compliance of patients, and increasing overall effectiveness of delivery. In this work, enhancement of transdermal delivery of fluorescent nanoparticles and sulforhodamine B with assistance of oleic acid was visualized utilizing multiphoton microscopy (MPM) and analyzed quantitatively using multi-photon excitation-induced fluorescent signals. Results of MPM imaging and MPM intensity-based spatial depth-dependent analysis showed that oleic acid is effective in facilitating transdermal delivery of nanoparticles.

The Reactome pathway knowledgebase

PubMed Central

Croft, David; Mundo, Antonio Fabregat; Haw, Robin; Milacic, Marija; Weiser, Joel; Wu, Guanming; Caudy, Michael; Garapati, Phani; Gillespie, Marc; Kamdar, Maulik R.; Jassal, Bijay; Jupe, Steven; Matthews, Lisa; May, Bruce; Palatnik, Stanislav; Rothfels, Karen; Shamovsky, Veronica; Song, Heeyeon; Williams, Mark; Birney, Ewan; Hermjakob, Henning; Stein, Lincoln; D'Eustachio, Peter

2014-01-01

Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation. PMID:24243840

The Reactome pathway knowledgebase.

PubMed

Croft, David; Mundo, Antonio Fabregat; Haw, Robin; Milacic, Marija; Weiser, Joel; Wu, Guanming; Caudy, Michael; Garapati, Phani; Gillespie, Marc; Kamdar, Maulik R; Jassal, Bijay; Jupe, Steven; Matthews, Lisa; May, Bruce; Palatnik, Stanislav; Rothfels, Karen; Shamovsky, Veronica; Song, Heeyeon; Williams, Mark; Birney, Ewan; Hermjakob, Henning; Stein, Lincoln; D'Eustachio, Peter

2014-01-01

Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation.

Source identification of nitrous oxide emission pathways from a single-stage nitritation-anammox granular reactor.

PubMed

Ali, Muhammad; Rathnayake, Rathnayake M L D; Zhang, Lei; Ishii, Satoshi; Kindaichi, Tomonori; Satoh, Hisashi; Toyoda, Sakae; Yoshida, Naohiro; Okabe, Satoshi

2016-10-01

Nitrous oxide (N2O) production pathway in a signal-stage nitritation-anammox sequencing batch reactor (SBR) was investigated based on a multilateral approach including real-time N2O monitoring, N2O isotopic composition analysis, and in-situ analyses of spatial distribution of N2O production rate and microbial populations in granular biomass. N2O emission rate was high in the initial phase of the operation cycle and gradually decreased with decreasing NH4(+) concentration. The average emission of N2O was 0.98 ± 0.42% and 1.35 ± 0.72% of the incoming nitrogen load and removed nitrogen, respectively. The N2O isotopic composition analysis revealed that N2O was produced via NH2OH oxidation and NO2(-) reduction pathways equally, although there is an unknown influence from N2O reduction and/or anammox N2O production. However, the N2O isotopomer analysis could not discriminate the relative contribution of nitrifier denitrification and heterotrophic denitrification in the NO2(-) reduction pathway. Various in-situ techniques (e.g. microsensor measurements and FISH (fluorescent in-situ hybridization) analysis) were therefore applied to further identify N2O producers. Microsensor measurements revealed that approximately 70% of N2O was produced in the oxic surface zone, where nitrifiers were predominantly localized. Thus, NH2OH oxidation and NO2 reduction by nitrifiers (nitrifier-denitrification) could be responsible for the N2O production in the oxic zone. The rest of N2O (ca. 30%) was produced in the anammox bacteria-dominated anoxic zone, probably suggesting that NO2(-) reduction by coexisting putative heterotrophic denitrifiers and some other unknown pathway(s) including the possibility of anammox process account for the anaerobic N2O production. Further study is required to identify the anaerobic N2O production pathways. Our multilateral approach can be useful to quantitatively examine the relative contributions of N2O production pathways. Good understanding of the key N2O production pathways is essential to establish a strategy to mitigate N2O emission from biological nitrogen removal processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Transcriptome profiling in engrailed-2 mutant mice reveals common molecular pathways associated with autism spectrum disorders.

PubMed

Sgadò, Paola; Provenzano, Giovanni; Dassi, Erik; Adami, Valentina; Zunino, Giulia; Genovesi, Sacha; Casarosa, Simona; Bozzi, Yuri

2013-12-19

Transcriptome analysis has been used in autism spectrum disorder (ASD) to unravel common pathogenic pathways based on the assumption that distinct rare genetic variants or epigenetic modifications affect common biological pathways. To unravel recurrent ASD-related neuropathological mechanisms, we took advantage of the En2-/- mouse model and performed transcriptome profiling on cerebellar and hippocampal adult tissues. Cerebellar and hippocampal tissue samples from three En2-/- and wild type (WT) littermate mice were assessed for differential gene expression using microarray hybridization followed by RankProd analysis. To identify functional categories overrepresented in the differentially expressed genes, we used integrated gene-network analysis, gene ontology enrichment and mouse phenotype ontology analysis. Furthermore, we performed direct enrichment analysis of ASD-associated genes from the SFARI repository in our differentially expressed genes. Given the limited number of animals used in the study, we used permissive criteria and identified 842 differentially expressed genes in En2-/- cerebellum and 862 in the En2-/- hippocampus. Our functional analysis revealed that the molecular signature of En2-/- cerebellum and hippocampus shares convergent pathological pathways with ASD, including abnormal synaptic transmission, altered developmental processes and increased immune response. Furthermore, when directly compared to the repository of the SFARI database, our differentially expressed genes in the hippocampus showed enrichment of ASD-associated genes significantly higher than previously reported. qPCR was performed for representative genes to confirm relative transcript levels compared to those detected in microarrays. Despite the limited number of animals used in the study, our bioinformatic analysis indicates the En2-/- mouse is a valuable tool for investigating molecular alterations related to ASD.

SASD: the Synthetic Alternative Splicing Database for identifying novel isoform from proteomics

PubMed Central

2013-01-01

Background Alternative splicing is an important and widespread mechanism for generating protein diversity and regulating protein expression. High-throughput identification and analysis of alternative splicing in the protein level has more advantages than in the mRNA level. The combination of alternative splicing database and tandem mass spectrometry provides a powerful technique for identification, analysis and characterization of potential novel alternative splicing protein isoforms from proteomics. Therefore, based on the peptidomic database of human protein isoforms for proteomics experiments, our objective is to design a new alternative splicing database to 1) provide more coverage of genes, transcripts and alternative splicing, 2) exclusively focus on the alternative splicing, and 3) perform context-specific alternative splicing analysis. Results We used a three-step pipeline to create a synthetic alternative splicing database (SASD) to identify novel alternative splicing isoforms and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing. First, we extracted information on gene structures of all genes in the Ensembl Genes 71 database and incorporated the Integrated Pathway Analysis Database. Then, we compiled artificial splicing transcripts. Lastly, we translated the artificial transcripts into alternative splicing peptides. The SASD is a comprehensive database containing 56,630 genes (Ensembl gene IDs), 95,260 transcripts (Ensembl transcript IDs), and 11,919,779 Alternative Splicing peptides, and also covering about 1,956 pathways, 6,704 diseases, 5,615 drugs, and 52 organs. The database has a web-based user interface that allows users to search, display and download a single gene/transcript/protein, custom gene set, pathway, disease, drug, organ related alternative splicing. Moreover, the quality of the database was validated with comparison to other known databases and two case studies: 1) in liver cancer and 2) in breast cancer. Conclusions The SASD provides the scientific community with an efficient means to identify, analyze, and characterize novel Exon Skipping and Intron Retention protein isoforms from mass spectrometry and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing. PMID:24267658

MutSα's Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning

NASA Astrophysics Data System (ADS)

Melvin, Ryan L.; Thompson, William G.; Godwin, Ryan C.; Gmeiner, William H.; Salsbury, Freddie R.

2017-03-01

MutSalpha is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimer’s post-recognition, post-binding response to three types of DNA damage involving cytotoxic, anti-cancer agents - carboplatin, cisplatin, and FdU. Through a combination of supervised and unsupervised machine learning techniques along with more traditional structural and kinetic analysis applied to all-atom molecular dynamics (MD) calculations, we predict that MutSalpha has a distinct response to each of the three damage types. Via a binary classification tree (a supervised machine learning technique), we identify key hydrogen bond motifs unique to each type of damage and suggest residues for experimental mutation studies. Through a combination of a recently developed clustering (unsupervised learning) algorithm, RMSF calculations, PCA, and correlated motions we predict that each type of damage causes MutS↵to explore a specific region of conformation space. Detailed analysis suggests a short range effect for carboplatin - primarily altering the structures and kinetics of residues within 10 angstroms of the damaged DNA - and distinct longer-range effects for cisplatin and FdU. In our simulations, we also observe that a key phenylalanine residue - known to stack with a mismatched or unmatched bases in MMR - stacks with the base complementary to the damaged base in 88.61% of MD frames containing carboplatinated DNA. Similarly, this Phe71 stacks with the base complementary to damage in 91.73% of frames with cisplatinated DNA. This residue, however, stacks with the damaged base itself in 62.18% of trajectory frames with FdU-substituted DNA and has no stacking interaction at all in 30.72% of these frames. Each drug investigated here induces a unique perturbation in the MutS↵complex, indicating the possibility of a distinct signaling event and specific repair or death pathway (or set of pathways) for a given type of damage.

Identifying molecular features for prostate cancer with Gleason 7 based on microarray gene expression profiles.

PubMed

Bălăcescu, Loredana; Bălăcescu, O; Crişan, N; Fetica, B; Petruţ, B; Bungărdean, Cătălina; Rus, Meda; Tudoran, Oana; Meurice, G; Irimie, Al; Dragoş, N; Berindan-Neagoe, Ioana

2011-01-01

Prostate cancer represents the first leading cause of cancer among western male population, with different clinical behavior ranging from indolent to metastatic disease. Although many molecules and deregulated pathways are known, the molecular mechanisms involved in the development of prostate cancer are not fully understood. The aim of this study was to explore the molecular variation underlying the prostate cancer, based on microarray analysis and bioinformatics approaches. Normal and prostate cancer tissues were collected by macrodissection from prostatectomy pieces. All prostate cancer specimens used in our study were Gleason score 7. Gene expression microarray (Agilent Technologies) was used for Whole Human Genome evaluation. The bioinformatics and functional analysis were based on Limma and Ingenuity software. The microarray analysis identified 1119 differentially expressed genes between prostate cancer and normal prostate, which were up- or down-regulated at least 2-fold. P-values were adjusted for multiple testing using Benjamini-Hochberg method with a false discovery rate of 0.01. These genes were analyzed with Ingenuity Pathway Analysis software and were established 23 genetic networks. Our microarray results provide new information regarding the molecular networks in prostate cancer stratified as Gleason 7. These data highlighted gene expression profiles for better understanding of prostate cancer progression.

Point Analysis in Java applied to histological images of the perforant pathway: a user's account.

PubMed

Scorcioni, Ruggero; Wright, Susan N; Patrick Card, J; Ascoli, Giorgio A; Barrionuevo, Germán

2008-01-01

The freeware Java tool Point Analysis in Java (PAJ), created to perform 3D point analysis, was tested in an independent laboratory setting. The input data consisted of images of the hippocampal perforant pathway from serial immunocytochemical localizations of the rat brain in multiple views at different resolutions. The low magnification set (x2 objective) comprised the entire perforant pathway, while the high magnification set (x100 objective) allowed the identification of individual fibers. A preliminary stereological study revealed a striking linear relationship between the fiber count at high magnification and the optical density at low magnification. PAJ enabled fast analysis for down-sampled data sets and a friendly interface with automated plot drawings. Noted strengths included the multi-platform support as well as the free availability of the source code, conducive to a broad user base and maximum flexibility for ad hoc requirements. PAJ has great potential to extend its usability by (a) improving its graphical user interface, (b) increasing its input size limit, (c) improving response time for large data sets, and (d) potentially being integrated with other Java graphical tools such as ImageJ.

Unveiling the excited state energy transfer pathways in peridinin-chlorophyll a-protein by ultrafast multi-pulse transient absorption spectroscopy.

PubMed

Redeckas, Kipras; Voiciuk, Vladislava; Zigmantas, Donatas; Hiller, Roger G; Vengris, Mikas

2017-04-01

Time-resolved multi-pulse methods were applied to investigate the excited state dynamics, the interstate couplings, and the excited state energy transfer pathways between the light-harvesting pigments in peridinin-chlorophyll a-protein (PCP). The utilized pump-dump-probe techniques are based on perturbation of the regular PCP energy transfer pathway. The PCP complexes were initially excited with an ultrashort pulse, resonant to the S 0 →S 2 transition of the carotenoid peridinin. A portion of the peridinin-based emissive intramolecular charge transfer (ICT) state was then depopulated by applying an ultrashort NIR pulse that perturbed the interaction between S 1 and ICT states and the energy flow from the carotenoids to the chlorophylls. The presented data indicate that the peridinin S 1 and ICT states are spectrally distinct and coexist in an excited state equilibrium in the PCP complex. Moreover, numeric analysis of the experimental data asserts ICT→Chl-a as the main energy transfer pathway in the photoexcited PCP systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Multiple pathways from the neighborhood food environment to increased body mass index through dietary behaviors: A structural equation-based analysis in the CARDIA study

PubMed Central

Richardson, Andrea S.; Meyer, Katie A.; Howard, Annie Green; Boone-Heinonen, Janne; Popkin, Barry M.; Evenson, Kelly R.; Shikany, James M.; Lewis, Cora E.; Gordon-Larsen, Penny

2016-01-01

Objectives To examine longitudinal pathways from multiple types of neighborhood restaurants and food stores to BMI, through dietary behaviors. Methods We used data from participants (n=5114) in the United States-based Coronary Artery Risk Development in Young Adults study and a structural equation model to estimate longitudinal (1985–86 to 2005–06) pathways simultaneously from neighborhood fast food restaurants, sit-down restaurants, supermarkets, and convenience stores to BMI through dietary behaviors, controlling for socioeconomic status (SES) and physical activity. Results Higher numbers of neighborhood fast food restaurants and lower numbers of sit-down restaurants were associated with higher consumption of an obesogenic fast food-type diet. The pathways from food stores to BMI through diet were inconsistent in magnitude and statistical significance. Conclusions Efforts to decrease the numbers of neighborhood fast food restaurants and to increase the numbers of sit-down restaurant options could influence diet behaviors. Availability of neighborhood fast food and sit-down restaurants may play comparatively stronger roles than food stores in shaping dietary behaviors and BMI. PMID:26454248

Nanocrystalline Si pathway induced unipolar resistive switching behavior from annealed Si-rich SiN{sub x}/SiN{sub y} multilayers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Xiaofan; Ma, Zhongyuan, E-mail: zyma@nju.edu.cn; Yang, Huafeng

2014-09-28

Adding a resistive switching functionality to a silicon microelectronic chip is a new challenge in materials research. Here, we demonstrate that unipolar and electrode-independent resistive switching effects can be realized in the annealed Si-rich SiN{sub x}/SiN{sub y} multilayers with high on/off ratio of 10{sup 9}. High resolution transmission electron microscopy reveals that for the high resistance state broken pathways composed of discrete nanocrystalline silicon (nc-Si) exist in the Si nitride multilayers. While for the low resistance state the discrete nc-Si regions is connected, forming continuous nc-Si pathways. Based on the analysis of the temperature dependent I-V characteristics and HRTEM photos,more » we found that the break-and-bridge evolution of nc-Si pathway is the origin of resistive switching memory behavior. Our findings provide insights into the mechanism of the resistive switching behavior in nc-Si films, opening a way for it to be utilized as a material in Si-based memories.« less

Sharp Curvature of Frontal Lobe White Matter Pathways in Children with Autism Spectrum Disorder: Tract-Based Morphometry Analysis

PubMed Central

Jeong, Jeong-Won; Kumar, Ajay; Sundaram, Senthil K.; Chugani, Harry T.; Chugani, Diane C.

2013-01-01

Background and Purpose As we had previously observed geometrical changes of frontal lobe association pathways in children with autism spectrum disorder (ASD), in the present study we analyzed the curvature of these white matter pathways using an objective tract based morphometry (TBM) analysis. Materials and Methods Diffusion tensor imaging (DTI) was performed in 32 children with ASD and 14 children with typical development. Curvature, fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) of bilateral arcuate fasciculus (AF), uncinate fasciculus (UF), and genu of corpus callosum (gCC) were investigated using the TBM group analysis assessed by False Discovery Rate p-value (PFDR) for multiple comparisons. Results Significantly higher curvatures were found in children with ASD especially at the parieto-temporal junction for AF (left: PFDR < 0.001; right: PFDR < 0.01), at the fronto-temporal junction for UF (left: PFDR < 0.005; right: PFDR < 0.03), and at the midline of the gCC (PFDR < 0.0001). RD was significantly higher in children with ASD at the same bending regions of AF (left: PFDR < 0.03, right: PFDR < 0.02), UF (left: PFDR < 0.04), and gCC (PFDR < 0.01). Conclusion Higher curvature and curvature dependent RD changes in children with ASD may be the result of higher density of thinner axons in these frontal lobe tracts. PMID:21757519

Improvement of Blood-Brain Barrier Integrity in Traumatic Brain Injury and Hemorrhagic Shock Following Treatment With Valproic Acid and Fresh Frozen Plasma.

PubMed

Nikolian, Vahagn C; Dekker, Simone E; Bambakidis, Ted; Higgins, Gerald A; Dennahy, Isabel S; Georgoff, Patrick E; Williams, Aaron M; Andjelkovic, Anuska V; Alam, Hasan B

2018-01-01

Combined traumatic brain injury and hemorrhagic shock are highly lethal. Following injuries, the integrity of the blood-brain barrier can be impaired, contributing to secondary brain insults. The status of the blood-brain barrier represents a potential factor impacting long-term neurologic outcomes in combined injuries. Treatment strategies involving plasma-based resuscitation and valproic acid therapy have shown efficacy in this setting. We hypothesize that a component of this beneficial effect is related to blood-brain barrier preservation. Following controlled traumatic brain injury, hemorrhagic shock, various resuscitation and treatment strategies were evaluated for their association with blood-brain barrier integrity. Analysis of gene expression profiles was performed using Porcine Gene ST 1.1 microarray. Pathway analysis was completed using network analysis tools (Gene Ontology, Ingenuity Pathway Analysis, and Parametric Gene Set Enrichment Analysis). Female Yorkshire swine were subjected to controlled traumatic brain injury and 2 hours of hemorrhagic shock (40% blood volume, mean arterial pressure 30-35 mmHg). Subjects were resuscitated with 1) normal saline, 2) fresh frozen plasma, 3) hetastarch, 4) fresh frozen plasma + valproic acid, or 5) hetastarch + valproic acid (n = 5 per group). After 6 hours of observation, brains were harvested for evaluation. Immunofluoroscopic evaluation of the traumatic brain injury site revealed significantly increased expression of tight-junction associated proteins (zona occludin-1, claudin-5) following combination therapy (fresh frozen plasma + valproic acid and hetastarch + valproic acid). The extracellular matrix protein laminin was found to have significantly improved expression with combination therapies. Pathway analysis indicated that valproic acid significantly modulated pathways involved in endothelial barrier function and cell signaling. Resuscitation with fresh frozen plasma results in improved expression of proteins essential for blood-brain barrier integrity. The addition of valproic acid provides significant improvement to these protein expression profiles. This is likely secondary to activation of key pathways related to endothelial functions.

Global gene expression analysis reveals pathway differences between teratogenic and non-teratogenic exposure concentrations of bisphenol A and 17β-estradiol in embryonic zebrafish

PubMed Central

Saili, Katerine S.; Tilton, Susan C.; Waters, Katrina M.; Tanguay, Robert L.

2013-01-01

Transient developmental exposure to 0.1 μM bisphenol A (BPA) results in larval zebrafish hyperactivity and learning impairments in the adult, while exposure to 80 μM BPA results in teratogenic responses, including craniofacial abnormalities and edema. The mode of action underlying these effects is unclear. We used global gene expression analysis to identify candidate genes and signaling pathways that mediate BPA’s developmental toxicity in zebrafish. Exposure concentrations were selected and anchored to the positive control, 17β-estradiol (E2), based on previously determined behavioral or teratogenic phenotypes. Functional analysis of differentially expressed genes revealed distinct expression profiles at 24 hours post fertilization for 0.1 versus 80 μM BPA and 0.1 versus 15 μM E2 exposure, identification of prothrombin activation as a top canonical pathway impacted by both 0.1 μM BPA and 0.1 μM E2 exposure, and suppressed expression of several genes involved in nervous system development and function following 0.1 μM BPAexposure. PMID:23557687

A Research Roadmap for Computation-Based Human Reliability Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boring, Ronald; Mandelli, Diego; Joe, Jeffrey

2015-08-01

The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is oftenmore » secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.« less

[Analysis on "component-target-pathway" of Paeonia lactiflora in treating cardiac diseases based on data mining].

PubMed

Liu, Yang; Zhang, Fang-Bo; Tang, Shi-Huan; Wang, Ping; Li, Sen; Su, Jin; Zhou, Rong-Rong; Zhang, Jia-Qi; Sun, Hui-Feng

2018-04-01

Based on the literature review and modern application of Paeonia lactiflora in heart diseases, this article would predict the target of drug and disease by intergrative pharmacology platform of traditional Chinese medicine (TCMIP, http://www.tcmip.cn), and then explore the molecular mechanism of P. lactiflora in treatment of heart disease, providing theoretical basis and method for further studies on P. lactiflora. According to the ancient books, P. lactiflora with functions of "removing the vascular obstruction, removing the lumps, relieving pain, diuretic, nutrient qi" and other effects, have been used for many times to treat heart disease. Some prescriptions are also favored by the modern physicians nowadays. With the development of science, the chemical components that play a role in heart disease and the interrelation between these components and the body become the research hotspot. In order to further reveal the pharmacological substance base and molecular mechanism of P. lactiflora for the treatment of such diseases, TCM-IP was used to obtain multiple molecular targets and signaling pathways in treatment of heart disease. ATP1A1, a common target of drug and disease, was related to energy, and HDAC2 mainly regulated cardiomyocyte hypertrophy gene and cardiomyocyte expression. Other main drug targets such as GCK, CHUK and PRKAA2 indirectly regulated heart disease through many pathways; multiple disease-associated signaling pathways interfered with various heart diseases including coronary heart disease, myocardial ischemia and myocardial hypertrophy through influencing energy metabolism, enzyme activity and gene expression. In conclusion, P. lactiflora plays a role in protecting heart function by regulating the gene expression of cardiomyocytes directly. Meanwhile, it can indirectly intervene in other pathways of heart function, and thus participate in the treatment of heart disease. In this paper, the molecular mechanism of P. lactiflora for treatment of heart disease was in computer prediction analysis level, and the specific mechanism of action still needs further experimental verification. Copyright© by the Chinese Pharmaceutical Association.

A novel bi-level meta-analysis approach: applied to biological pathway analysis.

PubMed

Nguyen, Tin; Tagett, Rebecca; Donato, Michele; Mitrea, Cristina; Draghici, Sorin

2016-02-01

The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study. We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis. The R scripts are available on demand from the authors. sorin@wayne.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Biosynthesis of the active compounds of Isatis indigotica based on transcriptome sequencing and metabolites profiling

PubMed Central

2013-01-01

Backgroud Isatis indigotica is a widely used herb for the clinical treatment of colds, fever, and influenza in Traditional Chinese Medicine (TCM). Various structural classes of compounds have been identified as effective ingredients. However, little is known at genetics level about these active metabolites. In the present study, we performed de novo transcriptome sequencing for the first time to produce a comprehensive dataset of I. indigotica. Results A database of 36,367 unigenes (average length = 1,115.67 bases) was generated by performing transcriptome sequencing. Based on the gene annotation of the transcriptome, 104 unigenes were identified covering most of the catalytic steps in the general biosynthetic pathways of indole, terpenoid, and phenylpropanoid. Subsequently, the organ-specific expression patterns of the genes involved in these pathways, and their responses to methyl jasmonate (MeJA) induction, were investigated. Metabolites profile of effective phenylpropanoid showed accumulation pattern of secondary metabolites were mostly correlated with the transcription of their biosynthetic genes. According to the analysis of UDP-dependent glycosyltransferases (UGT) family, several flavonoids were indicated to exist in I. indigotica and further identified by metabolic profile using UPLC/Q-TOF. Moreover, applying transcriptome co-expression analysis, nine new, putative UGTs were suggested as flavonol glycosyltransferases and lignan glycosyltransferases. Conclusions This database provides a pool of candidate genes involved in biosynthesis of effective metabolites in I. indigotica. Furthermore, the comprehensive analysis and characterization of the significant pathways are expected to give a better insight regarding the diversity of chemical composition, synthetic characteristics, and the regulatory mechanism which operate in this medical herb. PMID:24308360

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

PubMed Central

Kaushik, Abhinav; Ali, Shakir; Gupta, Dinesh

2017-01-01

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA. PMID:28084397

SFRP1 is a possible candidate for epigenetic therapy in non-small cell lung cancer.

PubMed

Taguchi, Y-H; Iwadate, Mitsuo; Umeyama, Hideaki

2016-08-12

Non-small cell lung cancer (NSCLC) remains a lethal disease despite many proposed treatments. Recent studies have indicated that epigenetic therapy, which targets epigenetic effects, might be a new therapeutic methodology for NSCLC. However, it is not clear which objects (e.g., genes) this treatment specifically targets. Secreted frizzled-related proteins (SFRPs) are promising candidates for epigenetic therapy in many cancers, but there have been no reports of SFRPs targeted by epigenetic therapy for NSCLC. This study performed a meta-analysis of reprogrammed NSCLC cell lines instead of the direct examination of epigenetic therapy treatment to identify epigenetic therapy targets. In addition, mRNA expression/promoter methylation profiles were processed by recently proposed principal component analysis based unsupervised feature extraction and categorical regression analysis based feature extraction. The Wnt/β-catenin signalling pathway was extensively enriched among 32 genes identified by feature extraction. Among the genes identified, SFRP1 was specifically indicated to target β-catenin, and thus might be targeted by epigenetic therapy in NSCLC cell lines. A histone deacetylase inhibitor might reactivate SFRP1 based upon the re-analysis of a public domain data set. Numerical computation validated the binding of SFRP1 to WNT1 to suppress Wnt signalling pathway activation in NSCLC. The meta-analysis of reprogrammed NSCLC cell lines identified SFRP1 as a promising target of epigenetic therapy for NSCLC.

Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations

PubMed Central

2017-01-01

Recent studies have challenged the ventral/“what” and dorsal/“where” two-visual-processing-pathway view by showing the existence of “what” and “where” information in both pathways. Is the two-pathway distinction still valid? Here, we examined how goal-directed visual information processing may differentially impact visual representations in these two pathways. Using fMRI and multivariate pattern analysis, in three experiments on human participants (57% females), by manipulating whether color or shape was task-relevant and how they were conjoined, we examined shape-based object category decoding in occipitotemporal and parietal regions. We found that object category representations in all the regions examined were influenced by whether or not object shape was task-relevant. This task effect, however, tended to decrease as task-relevant and irrelevant features were more integrated, reflecting the well-known object-based feature encoding. Interestingly, task relevance played a relatively minor role in driving the representational structures of early visual and ventral object regions. They were driven predominantly by variations in object shapes. In contrast, the effect of task was much greater in dorsal than ventral regions, with object category and task relevance both contributing significantly to the representational structures of the dorsal regions. These results showed that, whereas visual representations in the ventral pathway are more invariant and reflect “what an object is,” those in the dorsal pathway are more adaptive and reflect “what we do with it.” Thus, despite the existence of “what” and “where” information in both visual processing pathways, the two pathways may still differ fundamentally in their roles in visual information representation. SIGNIFICANCE STATEMENT Visual information is thought to be processed in two distinctive pathways: the ventral pathway that processes “what” an object is and the dorsal pathway that processes “where” it is located. This view has been challenged by recent studies revealing the existence of “what” and “where” information in both pathways. Here, we found that goal-directed visual information processing differentially modulates shape-based object category representations in the two pathways. Whereas ventral representations are more invariant to the demand of the task, reflecting what an object is, dorsal representations are more adaptive, reflecting what we do with the object. Thus, despite the existence of “what” and “where” information in both pathways, visual representations may still differ fundamentally in the two pathways. PMID:28821655

Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis

PubMed Central

Yang, Jing; Zhang, Wei; Sun, Jian; Xi, Zhiqin; Qiao, Zusha; Zhang, Jinyu; Wang, Yan; Ji, Ying; Feng, Wenli

2017-01-01

The aim of the present study was to investigate the potential genes involved in drug resistance of Candida albicans (C. albicans) by performing microarray analysis. The gene expression profile of GSE65396 was downloaded from the Gene Expression Omnibus, including a control, 15-min and 45-min macrocyclic compound RF59-treated group with three repeats for each. Following preprocessing using RAM, the differentially expressed genes (DEGs) were screened using the Limma package. Subsequently, the Kyoto Encyclopedia of Genes and Genomes pathways of these genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Based on interactions estimated by the Search Tool for Retrieval of Interacting Gene, the protein-protein interaction (PPI) network was visualized using Cytoscape. Subnetwork analysis was performed using ReactomeFI. A total of 154 upregulated and 27 downregulated DEGs were identified in the 15-min treated group, compared with the control, and 235 upregulated and 233 downregulated DEGs were identified in the 45-min treated group, compared with the control. The upregulated DEGs were significantly enriched in the ribosome pathway. Based on the PPI network, PRP5, RCL1, NOP13, NOP4 and MRT4 were the top five nodes in the 15-min treated comparison. GIS2, URA3, NOP58, ELP3 and PLP7 were the top five nodes in the 45-min treated comparison, and its subnetwork was significantly enriched in the ribosome pathway. The macrocyclic compound RF59 had a notable effect on the ribosome and its associated pathways of C. albicans. RCL1, NOP4, MRT4, GIS2 and NOP58 may be important in RF59-resistance. PMID:28944888

Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells

DOE PAGES

Gong, Chunhong; Zhang, Yi; Shankaran, Harish; ...

2014-10-02

Human epidermal growth factor receptors (HER, also known as ErbB) drive cellular proliferation, pro-survival and stress responses by activating several downstream kinases, in particular ERK, p38, JNK (SAPK), the PI3K/AKT, as well as various transcriptional regulators such as STAT3. When co-expressed, first three members of HER family (HER1-3) can form homo- and hetero-dimers. Based on the considerable evidence which suggest that every receptor dimer activates intracellular signaling pathways differentially, we hypothesized that the HER dimerization pattern is a better predictor of downstream signaling than the total receptor activation levels. We validated our hypothesis using a combination of model-based analysis tomore » quantify the HER dimerization patterns and multi-factorial experiments where HER dimerization patterns and signaling crosstalk were rationally perturbed. We have measured the activation of HER1-3 receptors and of the sentinel signaling proteins ERK, AKT, p38, JNK, STAT3 as a function of time in a panel of human mammary epithelial (HME) cells expressing different levels of HER1-3 stimulated with various ligand combinations. Our analysis using multiple ways of clustering the activation data has confirmed that the HER receptor dimer is a better predictor of the signaling through p38, ERK and AKT pathways than the total HER receptor expression and activation levels. Targeted inhibition studies to identify the causal effects allowed us to obtain a consensus regulatory interaction model, which revealed that STAT3 occupies a central role in the crosstalk between the studied pathways.« less

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

PubMed

Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

2004-05-05

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques. Copyright 2004 Wiley Periodicals, Inc.

Transcriptomic analyses of tributyltin-induced sexual dimorphisms in rare minnow (Gobiocypris rarus) brains.

PubMed

Zhang, Ji-Liang; Liu, Min; Zhang, Chun-Nuan; Li, Er-Chao; Fan, Ming-Zhen; Huang, Mao-Xian

2018-07-30

The brain of fish displays sexual dimorphisms and exhibits remarkable sexual plasticity throughout their life span. Although reproductive toxicity of tributyltin (TBT) in fish is well documented in fish, it remains unknown whether TBT interrupts sexual dimorphisms of fish brains. In this work, brain transcriptomic profiles of rare minnow (Gobiocypris rarus) was characterized and sex-biased genes were identified using RNA sequencing. Functional annotation and enrichment analysis were performed to reveal differences of gene products and pathways between the brains of male and female fish. Furthermore, transcriptomic responses of male and female brains to TBT at 10 ng/L were also investigated to understand effects of TBT on brain sexual dimorphisms. Only 345 male-biased and 273 female-biased genes were found in the brains. However, significant female-biased pathways of circadian rhythm and phototransduction were identified in the brains by enrichment analysis. Interestingly, following TBT exposure in the female fish, the circadian rhythm pathway was significantly disrupted based on enrichment analysis, while in the male fish, the phototransduction pathway was significantly disrupted. In the female fish, expression of genes (Per, Cry, Rev-Erb α, Ror, Dec and CK1δ/ε) in the circadian rhythm pathway was down-regulated after TBT exposure; while in the male fish, expression of genes (Rec, GNAT1_2, GNGT1, Rh/opsin, PDE and Arr) in the phototransduction pathway was up-regulated after TBT exposure. Overall, our results not only provide key data on the molecular basis of brain sexual dimorphisms in fish, but also offer valuable resources for investigating molecular mechanisms by which environmental chemicals might influence brain sexual plasticity. Copyright © 2018 Elsevier Inc. All rights reserved.

A new approach to systematization of the management of paper-based clinical pathways.

PubMed

Wakamiya, Shunji; Yamauchi, Kazunobu

2006-05-01

The present study was performed to explore a new approach to systematization of the management of paper-based clinical pathways by developing a new system requiring little capital investment. A new system was developed and incorporated into an existing network at a hospital with a paper-based clinical pathway management system. The effectiveness of this new system was examined by comparing the management efficiency of clinical pathways before and after its introduction, and by comparison of the new system with other such systems currently in place at other medical institutions with regard to efficiency. In addition, the acceptability of the system for other medical institutions was examined by providing free access to the software on the Internet. The development costs of the new system were low. Although the new system has been in place for more than 3 years, no problems have yet been encountered in either the existing network system or in the management system itself. The new system allows the processing of statistics and analysis of circulation or variance automatically, neither of which were possible in the original paper-based system. We provided open access to the system as free software on the Internet, and it has since been downloaded by many medical institutions and enterprises in Japan. This system is very useful for institutions where it is difficult to introduce expensive new systems for systematic management of clinical pathways, such as electronic medical records, because of problems regarding capital or system management, and it may also be useful in other countries.

Emergence of differentially regulated pathways associated with the development of regional specificity in chicken skin.

PubMed

Chang, Kai-Wei; Huang, Nancy A; Liu, I-Hsuan; Wang, Yi-Hui; Wu, Ping; Tseng, Yen-Tzu; Hughes, Michael W; Jiang, Ting Xin; Tsai, Mong-Hsun; Chen, Chien-Yu; Oyang, Yen-Jen; Lin, En-Chung; Chuong, Cheng-Ming; Lin, Shau-Ping

2015-01-23

Regional specificity allows different skin regions to exhibit different characteristics, enabling complementary functions to make effective use of the integumentary surface. Chickens exhibit a high degree of regional specificity in the skin and can serve as a good model for when and how these regional differences begin to emerge. We used developing feather and scale regions in embryonic chickens as a model to gauge the differences in their molecular pathways. We employed cosine similarity analysis to identify the differentially regulated and co-regulated genes. We applied low cell techniques for expression validation and chromatin immunoprecipitation (ChIP)-based enhancer identification to overcome limited cell availabilities from embryonic chicken skin. We identified a specific set of genes demonstrating a high correlation as being differentially expressed during feather and scale development and maturation. Some members of the WNT, TGF-beta/BMP, and Notch family known to be involved in feathering skin differentiation were found to be differentially regulated. Interestingly, we also found genes along calcium channel pathways that are differentially regulated. From the analysis of differentially regulated pathways, we used calcium signaling pathways as an example for further verification. Some voltage-gated calcium channel subunits, particularly CACNA1D, are expressed spatio-temporally in the skin epithelium. These calcium signaling pathway members may be involved in developmental decisions, morphogenesis, or epithelial maturation. We further characterized enhancers associated with histone modifications, including H3K4me1, H3K27ac, and H3K27me3, near calcium channel-related genes and identified signature intensive hotspots that may be correlated with certain voltage-gated calcium channel genes. We demonstrated the applicability of cosine similarity analysis for identifying novel regulatory pathways that are differentially regulated during development. Our study concerning the effects of signaling pathways and histone signatures on enhancers suggests that voltage-gated calcium signaling may be involved in early skin development. This work lays the foundation for studying the roles of these gene pathways and their genomic regulation during the establishment of skin regional specificity.

Skin sensitizers differentially regulate signaling pathways in MUTZ-3 cells in relation to their individual potency

PubMed Central

2014-01-01

Background Due to the recent European legislations posing a ban of animal tests for safety assessment within the cosmetic industry, development of in vitro alternatives for assessment of skin sensitization is highly prioritized. To date, proposed in vitro assays are mainly based on single biomarkers, which so far have not been able to classify and stratify chemicals into subgroups, related to risk or potency. Methods Recently, we presented the Genomic Allergen Rapid Detection (GARD) assay for assessment of chemical sensitizers. In this paper, we show how the genome wide readout of GARD can be expanded and used to identify differentially regulated pathways relating to individual chemical sensitizers. In this study, we investigated the mechanisms of action of a range of skin sensitizers through pathway identification, pathway classification and transcription factor analysis and related this to the reactive mechanisms and potency of the sensitizing agents. Results By transcriptional profiling of chemically stimulated MUTZ-3 cells, 33 canonical pathways intimately involved in sensitization to chemical substances were identified. The results showed that metabolic processes, cell cycling and oxidative stress responses are the key events activated during skin sensitization, and that these functions are engaged differently depending on the reactivity mechanisms of the sensitizing agent. Furthermore, the results indicate that the chemical reactivity groups seem to gradually engage more pathways and more molecules in each pathway with increasing sensitizing potency of the chemical used for stimulation. Also, a switch in gene regulation from up to down regulation, with increasing potency, was seen both in genes involved in metabolic functions and cell cycling. These observed pathway patterns were clearly reflected in the regulatory elements identified to drive these processes, where 33 regulatory elements have been proposed for further analysis. Conclusions This study demonstrates that functional analysis of biomarkers identified from our genomics study of human MUTZ-3 cells can be used to assess sensitizing potency of chemicals in vitro, by the identification of key cellular events, such as metabolic and cell cycling pathways. PMID:24517095

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

PubMed Central

Niu, Yulong; Liu, Chengcheng; Moghimyfiroozabad, Shayan; Yang, Yi

2017-01-01

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/. PMID:28875072

Guajadial: an unusual meroterpenoid from guava leaves Psidium guajava.

PubMed

Yang, Xiao-Long; Hsieh, Kun-Lung; Liu, Ji-Kai

2007-11-22

Guajadial (1), a novel caryophyllene-based meroterpenoid, was isolated from the Leaves of Psidium guajava (guava). The structure and relative stereochemistry of guajadial (1) were elucidated by extensive spectroscopic analysis. A possible biosynthetic pathway for 1 was proposed.

Genotoxicity Assessment of Drinking Water Disinfection Byproducts by DNA Damage and Repair Pathway Profiling Analysis.

PubMed

Lan, Jiaqi; Rahman, Sheikh Mokhlesur; Gou, Na; Jiang, Tao; Plewa, Micheal J; Alshawabkeh, Akram; Gu, April Z

2018-06-05

Genotoxicity is considered a major concern for drinking water disinfection byproducts (DBPs). Of over 700 DBPs identified to date, only a small number has been assessed with limited information for DBP genotoxicity mechanism(s). In this study, we evaluated genotoxicity of 20 regulated and unregulated DBPs applying a quantitative toxicogenomics approach. We used GFP-fused yeast strains that examine protein expression profiling of 38 proteins indicative of all known DNA damage and repair pathways. The toxicogenomics assay detected genotoxicity potential of these DBPs that is consistent with conventional genotoxicity assays end points. Furthermore, the high-resolution, real-time pathway activation and protein expression profiling, in combination with clustering analysis, revealed molecular level details in the genotoxicity mechanisms among different DBPs and enabled classification of DBPs based on their distinct DNA damage effects and repair mechanisms. Oxidative DNA damage and base alkylation were confirmed to be the main molecular mechanisms of DBP genotoxicity. Initial exploration of QSAR modeling using moleular genotoxicity end points (PELI) suggested that genotoxicity of DBPs in this study was correlated with topological and quantum chemical descriptors. This study presents a toxicogenomics-based assay for fast and efficient mechanistic genotoxicity screening and assessment of a large number of DBPs. The results help to fill in the knowledge gap in the understanding of the molecular mechanisms of DBP genotoxicity.

An “EAR” on environmental surveillance and monitoring: A case study on the use of Exposure–Activity Ratios (EARs) to prioritize sites, chemicals, and bioactivities of concern in Great Lakes waters

USGS Publications Warehouse

Blackwell, Brett R.; Ankley, Gerald T.; Corsi, Steven; DeCicco, Laura; Houck, Kieth A.; Judson, Richard S.; Li, Shibin; Martin, Matthew T.; Murphy, Elizabeth; Schroeder, Anthony L.; Smith, Edwin R.; Swintek, Joe; Villeneuve, Daniel L.

2017-01-01

Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on concentration alone, it can be difficult to identify which compounds may be of toxicological concern and should be prioritized for further monitoring, in-depth testing, or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high-throughput screening (HTS) data, such as the ToxCast database, which contains information for over 9000 compounds screened through up to 1100 bioassays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast effects database were used to calculate exposure–activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Prioritized bioactivities from the EAR analysis were linked to discrete adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts.

Pure mechanistic analysis of additive neuroprotective effects between baicalin and jasminoidin in ischemic stroke mice.

PubMed

Wang, Peng-Qian; Liu, Qiong; Xu, Wen-Juan; Yu, Ya-Nan; Zhang, Ying-Ying; Li, Bing; Liu, Jun; Wang, Zhong

2018-06-01

Both baicalin (BA) and jasminoidin (JA) are active ingredients in Chinese herb medicine Scutellaria baicalensis and Fructus gardeniae, respectively. They have been shown to exert additive neuroprotective action in ischemic stroke models. In this study we used transcriptome analysis to explore the pure therapeutic mechanisms of BA, JA and their combination (BJ) contributing to phenotype variation and reversal of pathological processes. Mice with middle cerebral artery obstruction were treated with BA, JA, their combination (BJ), or concha margaritifera (CM). Cerebral infarct volume was examined to determine the effect of these compounds on phenotype. Using the hippocampus microarray and ingenuity pathway analysis (IPA) software, we exacted the differentially expressed genes, networks, pathways, and functions in positive-phenotype groups (BA, JA and BJ) by comparing with the negative-phenotype group (CM). In the BA, JA, and BJ groups, a total of 7, 4, and 11 specific target molecules, 1, 1, and 4 networks, 51, 59, and 18 canonical pathways and 70, 53, and 64 biological functions, respectively, were identified. Pure therapeutic mechanisms of BA and JA were mainly overlapped in specific target molecules, functions and pathways, which were related to the nervous system, inflammation and immune response. The specific mechanisms of BA and JA were associated with apoptosis and cancer-related signaling and endocrine and hormone regulation, respectively. In the BJ group, novel target profiles distinct from mono-therapies were revealed, including 11 specific target molecules, 10 functions, and 10 pathways, the majority of which were related to a virus-mediated immune response. The pure additive effects between BA and JA were based on enhanced action in virus-mediated immune response. This pure mechanistic analysis may provide a clearer outline of the target profiles of multi-target compounds and combination therapies.

Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytren’s Disease

PubMed Central

Becker, Kerstin; Siegert, Sabine; Toliat, Mohammad Reza; Du, Juanjiangmeng; Casper, Ramona; Dolmans, Guido H.; Werker, Paul M.; Tinschert, Sigrid; Franke, Andre; Gieger, Christian; Strauch, Konstantin; Nothnagel, Michael; Nürnberg, Peter; Hennies, Hans Christian

2016-01-01

Dupuytren´s disease, a fibromatosis of the connective tissue in the palm, is a common complex disease with a strong genetic component. Up to date nine genetic loci have been found to be associated with the disease. Six of these loci contain genes that code for Wnt signalling proteins. In spite of this striking first insight into the genetic factors in Dupuytren´s disease, much of the inherited risk in Dupuytren´s disease still needs to be discovered. The already identified loci jointly explain ~1% of the heritability in this disease. To further elucidate the genetic basis of Dupuytren´s disease, we performed a genome-wide meta-analysis combining three genome-wide association study (GWAS) data sets, comprising 1,580 cases and 4,480 controls. We corroborated all nine previously identified loci, six of these with genome-wide significance (p-value < 5x10-8). In addition, we identified 14 new suggestive loci (p-value < 10−5). Intriguingly, several of these new loci contain genes associated with Wnt signalling and therefore represent excellent candidates for replication. Next, we compared whole-transcriptome data between patient- and control-derived tissue samples and found the Wnt/β-catenin pathway to be the top deregulated pathway in patient samples. We then conducted network and pathway analyses in order to identify protein networks that are enriched for genes highlighted in the GWAS meta-analysis and expression data sets. We found further evidence that the Wnt signalling pathways in conjunction with other pathways may play a critical role in Dupuytren´s disease. PMID:27467239

A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

PubMed Central

2013-01-01

Background Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments. Results Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer. Conclusions Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics. PMID:24153255

Comparison of tumor related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma.

PubMed

Xu, Song; Liu, Renwang; Da, Yurong

2018-06-05

This study compared tumor-related signaling pathways with known compounds to determine potential agents for lung adenocarcinoma (LUAD) treatment. Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses were performed based on LUAD differentially expressed genes from The Cancer Genome Atlas (TCGA) project and genotype-tissue expression controls. These results were compared to various known compounds using the Connectivity Mapping dataset. The clinical significance of the hub genes identified by overlapping pathway enrichment analysis was further investigated using data mining from multiple sources. A drug-pathway network for LUAD was constructed, and molecular docking was carried out. After the integration of 57 LUAD-related pathways and 35 pathways affected by small molecules, five overlapping pathways were revealed. Among these five pathways, the p53 signaling pathway was the most significant, with CCNB1, CCNB2, CDK1, CDKN2A, and CHEK1 being identified as hub genes. The p53 signaling pathway is implicated as a risk factor for LUAD tumorigenesis and survival. A total of 88 molecules significantly inhibiting the five LUAD-related oncogenic pathways were involved in the LUAD drug-pathway network. Daunorubicin, mycophenolic acid, and pyrvinium could potentially target the hub gene CHEK1 directly. Our study highlights the critical pathways that should be targeted in the search for potential LUAD treatments, most importantly, the p53 signaling pathway. Some compounds, such as ciclopirox and AG-028671, may have potential roles for LUAD treatment but require further experimental verification. © 2018 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

Pathway analysis of genome-wide association datasets of personality traits.

PubMed

Kim, H-N; Kim, B-H; Cho, J; Ryu, S; Shin, H; Sung, J; Shin, C; Cho, N H; Sung, Y A; Choi, B-O; Kim, H-L

2015-04-01

Although several genome-wide association (GWA) studies of human personality have been recently published, genetic variants that are highly associated with certain personality traits remain unknown, due to difficulty reproducing results. To further investigate these genetic variants, we assessed biological pathways using GWA datasets. Pathway analysis using GWA data was performed on 1089 Korean women whose personality traits were measured with the Revised NEO Personality Inventory for the 5-factor model of personality. A total of 1042 pathways containing 8297 genes were included in our study. Of these, 14 pathways were highly enriched with association signals that were validated in 1490 independent samples. These pathways include association of: Neuroticism with axon guidance [L1 cell adhesion molecule (L1CAM) interactions]; Extraversion with neuronal system and voltage-gated potassium channels; Agreeableness with L1CAM interaction, neurotransmitter receptor binding and downstream transmission in postsynaptic cells; and Conscientiousness with the interferon-gamma and platelet-derived growth factor receptor beta polypeptide pathways. Several genes that contribute to top-ranked pathways in this study were previously identified in GWA studies or by pathway analysis in schizophrenia or other neuropsychiatric disorders. Here we report the first pathway analysis of all five personality traits. Importantly, our analysis identified novel pathways that contribute to understanding the etiology of personality traits. © 2015 The Authors. Genes, Brain and Behavior published by International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd.

Multiple intracellular signaling pathways orchestrate adipocytic differentiation of human bone marrow stromal stem cells.

PubMed

Ali, Dalia; Abuelreich, Sarah; Alkeraishan, Nora; Shwish, Najla Bin; Hamam, Rimi; Kassem, Moustapha; Alfayez, Musaad; Aldahmash, Abdullah; Alajez, Nehad M

2018-02-28

Bone marrow adipocyte formation plays a role in bone homeostasis and whole body energy metabolism. However, the transcriptional landscape and signaling pathways associated with adipocyte lineage commitment and maturation are not fully delineated. Thus, we performed global gene expression profiling during adipocyte differentiation of human bone marrow stromal (mesenchymal) stem cells (hMSCs) and identified 2,589 up-regulated and 2,583 down-regulated mRNA transcripts. Pathway analysis on the up-regulated gene list untraveled enrichment in multiple signaling pathways including insulin receptor signaling, focal Adhesion, metapathway biotransformation, a number of metabolic pathways e.g. selenium metabolism, Benzo(a)pyrene metabolism, fatty acid, triacylglycerol, ketone body metabolism, tryptophan metabolism, and catalytic cycle of mammalian flavin-containing monooxygenase (FMOs). On the other hand, pathway analysis on the down-regulated genes revealed significant enrichment in pathways related to cell cycle regulation. Based on these data, we assessed the effect of pharmacological inhibition of FAK signaling using PF-573228, PF-562271, and InsR/IGF-1R using NVP-AEW541 and GSK-1904529A on adipocyte differentiation. hMSCs exposed to FAK or IGF-1R/InsR inhibitors exhibited fewer adipocyte formation (27-58% inhibition, P <0005). Concordantly, the expression of adipocyte-specific genes AP2, AdipoQ, and CEBPα was significantly reduced. On the other hand, we did not detect significant effects on cell viability as a result of FAK or IGF-1R/InsR inhibition. Our data identified FAK and insulin signaling as important intracellular signaling pathways relevant to bone marrow adipogenesis. © 2018 The Author(s).

Plasma metabonomics study of the patients with acute anterior uveitis based on ultra-performance liquid chromatography-mass spectrometry.

PubMed

Guo, Junguo; Yan, Tingqin; Bi, Hongsheng; Xie, Xiaofeng; Wang, Xingrong; Guo, Dadong; Jiang, Haiqiang

2014-06-01

The identification of the biomarkers of patients with acute anterior uveitis (AAU) may allow for a less invasive and more accurate diagnosis, as well as serving as a predictor in AAU progression and treatment response. The aim of this study was to identify the potential biomarkers and the metabolic pathways from plasma in patients with AAU. Both plasma metabolic biomarkers and metabolic pathways in the AAU patients versus healthy volunteers were investigated using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and a metabonomics approach. The principal component analysis (PCA) was used to separate AAU patients from healthy volunteers as well as to identify the different biomarkers between the two groups. Metabolic compounds were matched to the KEGG, METLIN, and HMDB databases, and metabolic pathways associated with AAU were identified. The PCA for UPLC-MS data shows that the metabolites in AAU patients were significantly different from those of healthy volunteers. Of the 4,396 total features detected by UPLC-MS, 102 features were significantly different between AAU patients and healthy volunteers according to the variable importance plot (VIP) values (greater than two) of partial least squares discriminate analysis (PLS-DA). Thirty-three metabolic compounds were identified and were considered as potential biomarkers. Meanwhile, ten metabolic pathways were found that were related to the AAU according to the identified biomarkers. These data suggest that metabolomics study can identify potential metabolites that differ between AAU patients and healthy volunteers. Based on the PCA, PLS-DA, several potential metabolic biomarkers and pathways in AAU patients were found and identified. In addition, the UPLC-MS technique combined with metabonomics could be a suitable systematic biology tool in research in clinical problems in ophthalmology, and can provide further insight into the pathophysiology of AAU.

An "EAR" on environmental surveillance and monitoring: A ...

EPA Pesticide Factsheets

Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on chemical concentration alone, it can be difficult to identify which compounds may be of toxicological concern for prioritization for further monitoring or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high throughput screening data like the ToxCast™ database, which contains data for over 9,000 compounds screened through up to 1,100 assays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast™ effects database were used to calculate exposure-activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast™ database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Biological pathways were then linked to adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts. Anthropogenic contaminants are frequently reported in environm

Identification of hub subnetwork based on topological features of genes in breast cancer

PubMed Central

ZHUANG, DA-YONG; JIANG, LI; HE, QING-QING; ZHOU, PENG; YUE, TAO

2015-01-01

The aim of this study was to provide functional insight into the identification of hub subnetworks by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We applied a protein network-based approach to identify subnetworks which may provide new insight into the functions of pathways involved in breast cancer rather than individual genes. Five groups of breast cancer data were downloaded and analyzed from the Gene Expression Omnibus (GEO) database of high-throughput gene expression data to identify gene signatures using the genome-wide global significance (GWGS) method. A PPI network was constructed using Cytoscape and clusters that focused on highly connected nodes were obtained using the molecular complex detection (MCODE) clustering algorithm. Pathway analysis was performed to assess the functional relevance of selected gene signatures based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Topological centrality was used to characterize the biological importance of gene signatures, pathways and clusters. The results revealed that, cluster1, as well as the cell cycle and oocyte meiosis pathways were significant subnetworks in the analysis of degree and other centralities, in which hub nodes mostly distributed. The most important hub nodes, with top ranked centrality, were also similar with the common genes from the above three subnetwork intersections, which was viewed as a hub subnetwork with more reproducible than individual critical genes selected without network information. This hub subnetwork attributed to the same biological process which was essential in the function of cell growth and death. This increased the accuracy of identifying gene interactions that took place within the same functional process and was potentially useful for the development of biomarkers and networks for breast cancer. PMID:25573623

Protein-protein interaction analysis of Alzheimer`s disease and NAFLD based on systems biology methods unhide common ancestor pathways.

PubMed

Karbalaei, Reza; Allahyari, Marzieh; Rezaei-Tavirani, Mostafa; Asadzadeh-Aghdaei, Hamid; Zali, Mohammad Reza

2018-01-01

Analysis reconstruction networks from two diseases, NAFLD and Alzheimer`s diseases and their relationship based on systems biology methods. NAFLD and Alzheimer`s diseases are two complex diseases, with progressive prevalence and high cost for countries. There are some reports on relation and same spreading pathways of these two diseases. In addition, they have some similar risk factors, exclusively lifestyle such as feeding, exercises and so on. Therefore, systems biology approach can help to discover their relationship. DisGeNET and STRING databases were sources of disease genes and constructing networks. Three plugins of Cytoscape software, including ClusterONE, ClueGO and CluePedia, were used to analyze and cluster networks and enrichment of pathways. An R package used to define best centrality method. Finally, based on degree and Betweenness, hubs and bottleneck nodes were defined. Common genes between NAFLD and Alzheimer`s disease were 190 genes that used construct a network with STRING database. The resulting network contained 182 nodes and 2591 edges and comprises from four clusters. Enrichment of these clusters separately lead to carbohydrate metabolism, long chain fatty acid and regulation of JAK-STAT and IL-17 signaling pathways, respectively. Also seven genes selected as hub-bottleneck include: IL6, AKT1, TP53, TNF, JUN, VEGFA and PPARG. Enrichment of these proteins and their first neighbors in network by OMIM database lead to diabetes and obesity as ancestors of NAFLD and AD. Systems biology methods, specifically PPI networks, can be useful for analyzing complicated related diseases. Finding Hub and bottleneck proteins should be the goal of drug designing and introducing disease markers.

Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data

PubMed Central

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

2015-01-01

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions. To facilitate such an effort, we have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis. In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community. Database URL: http://www.bio-bigdata.com/Co-LncRNA/ PMID:26363020

Computational analysis of multimorbidity between asthma, eczema and rhinitis

PubMed Central

Aguilar, Daniel; Pinart, Mariona; Koppelman, Gerard H.; Saeys, Yvan; Nawijn, Martijn C.; Postma, Dirkje S.; Akdis, Mübeccel; Auffray, Charles; Ballereau, Stéphane; Benet, Marta; García-Aymerich, Judith; González, Juan Ramón; Guerra, Stefano; Keil, Thomas; Kogevinas, Manolis; Lambrecht, Bart; Lemonnier, Nathanael; Melen, Erik; Sunyer, Jordi; Valenta, Rudolf; Valverde, Sergi; Wickman, Magnus; Bousquet, Jean; Oliva, Baldo; Antó, Josep M.

2017-01-01

Background The mechanisms explaining the co-existence of asthma, eczema and rhinitis (allergic multimorbidity) are largely unknown. We investigated the mechanisms underlying multimorbidity between three main allergic diseases at a molecular level by identifying the proteins and cellular processes that are common to them. Methods An in silico study based on computational analysis of the topology of the protein interaction network was performed in order to characterize the molecular mechanisms of multimorbidity of asthma, eczema and rhinitis. As a first step, proteins associated to either disease were identified using data mining approaches, and their overlap was calculated. Secondly, a functional interaction network was built, allowing to identify cellular pathways involved in allergic multimorbidity. Finally, a network-based algorithm generated a ranked list of newly predicted multimorbidity-associated proteins. Results Asthma, eczema and rhinitis shared a larger number of associated proteins than expected by chance, and their associated proteins exhibited a significant degree of interconnectedness in the interaction network. There were 15 pathways involved in the multimorbidity of asthma, eczema and rhinitis, including IL4 signaling and GATA3-related pathways. A number of proteins potentially associated to these multimorbidity processes were also obtained. Conclusions These results strongly support the existence of an allergic multimorbidity cluster between asthma, eczema and rhinitis, and suggest that type 2 signaling pathways represent a relevant multimorbidity mechanism of allergic diseases. Furthermore, we identified new candidates contributing to multimorbidity that may assist in identifying new targets for multimorbid allergic diseases. PMID:28598986

Identification of additive, dominant, and epistatic variation conferred by key genes in cellulose biosynthesis pathway in Populus tomentosa†

PubMed Central

Du, Qingzhang; Tian, Jiaxing; Yang, Xiaohui; Pan, Wei; Xu, Baohua; Li, Bailian; Ingvarsson, Pär K.; Zhang, Deqiang

2015-01-01

Economically important traits in many species generally show polygenic, quantitative inheritance. The components of genetic variation (additive, dominant and epistatic effects) of these traits conferred by multiple genes in shared biological pathways remain to be defined. Here, we investigated 11 full-length genes in cellulose biosynthesis, on 10 growth and wood-property traits, within a population of 460 unrelated Populus tomentosa individuals, via multi-gene association. To validate positive associations, we conducted single-marker analysis in a linkage population of 1,200 individuals. We identified 118, 121, and 43 associations (P< 0.01) corresponding to additive, dominant, and epistatic effects, respectively, with low to moderate proportions of phenotypic variance (R2). Epistatic interaction models uncovered a combination of three non-synonymous sites from three unique genes, representing a significant epistasis for diameter at breast height and stem volume. Single-marker analysis validated 61 associations (false discovery rate, Q ≤ 0.10), representing 38 SNPs from nine genes, and its average effect (R2 = 3.8%) nearly 2-fold higher than that identified with multi-gene association, suggesting that multi-gene association can capture smaller individual variants. Moreover, a structural gene–gene network based on tissue-specific transcript abundances provides a better understanding of the multi-gene pathway affecting tree growth and lignocellulose biosynthesis. Our study highlights the importance of pathway-based multiple gene associations to uncover the nature of genetic variance for quantitative traits and may drive novel progress in molecular breeding. PMID:25428896

Computational analysis of multimorbidity between asthma, eczema and rhinitis.

PubMed

Aguilar, Daniel; Pinart, Mariona; Koppelman, Gerard H; Saeys, Yvan; Nawijn, Martijn C; Postma, Dirkje S; Akdis, Mübeccel; Auffray, Charles; Ballereau, Stéphane; Benet, Marta; García-Aymerich, Judith; González, Juan Ramón; Guerra, Stefano; Keil, Thomas; Kogevinas, Manolis; Lambrecht, Bart; Lemonnier, Nathanael; Melen, Erik; Sunyer, Jordi; Valenta, Rudolf; Valverde, Sergi; Wickman, Magnus; Bousquet, Jean; Oliva, Baldo; Antó, Josep M

2017-01-01

The mechanisms explaining the co-existence of asthma, eczema and rhinitis (allergic multimorbidity) are largely unknown. We investigated the mechanisms underlying multimorbidity between three main allergic diseases at a molecular level by identifying the proteins and cellular processes that are common to them. An in silico study based on computational analysis of the topology of the protein interaction network was performed in order to characterize the molecular mechanisms of multimorbidity of asthma, eczema and rhinitis. As a first step, proteins associated to either disease were identified using data mining approaches, and their overlap was calculated. Secondly, a functional interaction network was built, allowing to identify cellular pathways involved in allergic multimorbidity. Finally, a network-based algorithm generated a ranked list of newly predicted multimorbidity-associated proteins. Asthma, eczema and rhinitis shared a larger number of associated proteins than expected by chance, and their associated proteins exhibited a significant degree of interconnectedness in the interaction network. There were 15 pathways involved in the multimorbidity of asthma, eczema and rhinitis, including IL4 signaling and GATA3-related pathways. A number of proteins potentially associated to these multimorbidity processes were also obtained. These results strongly support the existence of an allergic multimorbidity cluster between asthma, eczema and rhinitis, and suggest that type 2 signaling pathways represent a relevant multimorbidity mechanism of allergic diseases. Furthermore, we identified new candidates contributing to multimorbidity that may assist in identifying new targets for multimorbid allergic diseases.

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling.

PubMed

Wen, Shi; Zhan, Bohan; Feng, Jianghua; Hu, Weize; Lin, Xianchao; Bai, Jianxi; Huang, Heguang

2017-11-02

The differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy. By implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1 H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight. An obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation. The NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.

Informatics and computational strategies for the study of lipids.

PubMed

Yetukuri, Laxman; Ekroos, Kim; Vidal-Puig, Antonio; Oresic, Matej

2008-02-01

Recent advances in mass spectrometry (MS)-based techniques for lipidomic analysis have empowered us with the tools that afford studies of lipidomes at the systems level. However, these techniques pose a number of challenges for lipidomic raw data processing, lipid informatics, and the interpretation of lipidomic data in the context of lipid function and structure. Integration of lipidomic data with other systemic levels, such as genomic or proteomic, in the context of molecular pathways and biophysical processes provides a basis for the understanding of lipid function at the systems level. The present report, based on the limited literature, is an update on a young but rapidly emerging field of lipid informatics and related pathway reconstruction strategies.

Pathway analysis of high-throughput biological data within a Bayesian network framework.

PubMed

Isci, Senol; Ozturk, Cengizhan; Jones, Jon; Otu, Hasan H

2011-06-15

Most current approaches to high-throughput biological data (HTBD) analysis either perform individual gene/protein analysis or, gene/protein set enrichment analysis for a list of biologically relevant molecules. Bayesian Networks (BNs) capture linear and non-linear interactions, handle stochastic events accounting for noise, and focus on local interactions, which can be related to causal inference. Here, we describe for the first time an algorithm that models biological pathways as BNs and identifies pathways that best explain given HTBD by scoring fitness of each network. Proposed method takes into account the connectivity and relatedness between nodes of the pathway through factoring pathway topology in its model. Our simulations using synthetic data demonstrated robustness of our approach. We tested proposed method, Bayesian Pathway Analysis (BPA), on human microarray data regarding renal cell carcinoma (RCC) and compared our results with gene set enrichment analysis. BPA was able to find broader and more specific pathways related to RCC. Accompanying BPA software (BPAS) package is freely available for academic use at http://bumil.boun.edu.tr/bpa.

Metabolome analysis-based design and engineering of a metabolic pathway in Corynebacterium glutamicum to match rates of simultaneous utilization of D-glucose and L-arabinose.

PubMed

Kawaguchi, Hideo; Yoshihara, Kumiko; Hara, Kiyotaka Y; Hasunuma, Tomohisa; Ogino, Chiaki; Kondo, Akihiko

2018-05-17

L-Arabinose is the second most abundant component of hemicellulose in lignocellulosic biomass, next to D-xylose. However, few microorganisms are capable of utilizing pentoses, and catabolic genes and operons enabling bacterial utilization of pentoses are typically subject to carbon catabolite repression by more-preferred carbon sources, such as D-glucose, leading to a preferential utilization of D-glucose over pentoses. In order to simultaneously utilize both D-glucose and L-arabinose at the same rate, a modified metabolic pathway was rationally designed based on metabolome analysis. Corynebacterium glutamicum ATCC 31831 utilized D-glucose and L-arabinose simultaneously at a low concentration (3.6 g/L each) but preferentially utilized D-glucose over L-arabinose at a high concentration (15 g/L each), although L-arabinose and D-glucose were consumed at comparable rates in the absence of the second carbon source. Metabolome analysis revealed that phosphofructokinase and pyruvate kinase were major bottlenecks for D-glucose and L-arabinose metabolism, respectively. Based on the results of metabolome analysis, a metabolic pathway was engineered by overexpressing pyruvate kinase in combination with deletion of araR, which encodes a repressor of L-arabinose uptake and catabolism. The recombinant strain utilized high concentrations of D-glucose and L-arabinose (15 g/L each) at the same consumption rate. During simultaneous utilization of both carbon sources at high concentrations, intracellular levels of phosphoenolpyruvate declined and acetyl-CoA levels increased significantly as compared with the wild-type strain that preferentially utilized D-glucose. These results suggest that overexpression of pyruvate kinase in the araR deletion strain increased the specific consumption rate of L-arabinose and that citrate synthase activity becomes a new bottleneck in the engineered pathway during the simultaneous utilization of D-glucose and L-arabinose. Metabolome analysis identified potential bottlenecks in D-glucose and L-arabinose metabolism and was then applied to the following rational metabolic engineering. Manipulation of only two genes enabled simultaneous utilization of D-glucose and L-arabinose at the same rate in metabolically engineered C. glutamicum. This is the first report of rational metabolic design and engineering for simultaneous hexose and pentose utilization without inactivating the phosphotransferase system.

Better organized care via care pathways: A multicenter study.

PubMed

Seys, Deborah; Bruyneel, Luk; Deneckere, Svin; Kul, Seval; Van der Veken, Liz; van Zelm, Ruben; Sermeus, Walter; Panella, Massimiliano; Vanhaecht, Kris

2017-01-01

An increased need for efficiency and effectiveness in today's healthcare system urges professionals to improve the organization of care. Care pathways are an important tool to achieve this. The overall aim of this study was to analyze if care pathways lead to better organization of care processes. For this, the Care Process Self-Evaluation tool (CPSET) was used to evaluate how healthcare professionals perceive the organization of care processes. Based on information from 2692 health care professionals gathered between November 2007 and October 2011 we audited 261 care processes in 108 organizations. Multilevel analysis was used to compare care processes without and with care pathways and analyze if care pathways led to better organization of care processes. A significant difference between care processes with and without care pathways was found. A care pathway in use led to significant better scores on the overall CPSET scale (p<0.001) and its subscales, "coordination of care" (p<0.001) and "follow-up of care" (p<0.001). Physicians had the highest score on the overall CPSET scale and the five subscales. Care processes organized by care pathways had a 2.6 times higher probability that the care process was well-organized. In around 75% of the cases a care pathway led to better organized care processes. Care processes supported by care pathways were better organized, but not all care pathways were well-organized. Managers can use care pathways to make healthcare professionals more aware of their role in the organization of the care process.

Development of an image-based screening system for inhibitors of the plastidial MEP pathway and of protein geranylgeranylation

PubMed Central

Hartmann, Michael; Gas-Pascual, Elisabet; Hemmerlin, Andrea; Rohmer, Michel; Bach, Thomas J.

2015-01-01

In a preceding study we have recently established an in vivo visualization system for the geranylgeranylation of proteins in a stably transformed tobacco BY-2 cell line, which involves expressing a dexamethasone-inducible GFP fused to the prenylable, carboxy-terminal basic domain of the rice calmodulin CaM61, which naturally bears a CaaL geranylgeranylation motif (GFP-BD-CVIL). By using pathway-specific inhibitors it was there demonstrated that inhibition of the methylerythritol phosphate (MEP) pathway with oxoclomazone and fosmidomycin, as well as inhibition of protein geranylgeranyl transferase type 1 (PGGT-1), shifted the localization of the GFP-BD-CVIL protein from the membrane to the nucleus. In contrast, the inhibition of the mevalonate (MVA) pathway with mevinolin did not affect this localization. Furthermore, in this initial study complementation assays with pathway-specific intermediates confirmed that the precursors for the cytosolic isoprenylation of this fusion protein are predominantly provided by the MEP pathway. In order to optimize this visualization system from a more qualitative assay to a statistically trustable medium or a high-throughput screening system, we established now new conditions that permit culture and analysis in 96-well microtiter plates, followed by fluorescence microscopy. For further refinement, the existing GFP-BD-CVIL cell line was transformed with an estradiol-inducible vector driving the expression of a RFP protein, C-terminally fused to a nuclear localization signal (NLS-RFP). We are thus able to quantify the total number of viable cells versus the number of inhibited cells after various treatments. This approach also includes a semi-automatic counting system, based on the freely available image processing software. As a result, the time of image analysis as well as the risk of user-generated bias is reduced to a minimum. Moreover, there is no cross-induction of gene expression by dexamethasone and estradiol, which is an important prerequisite for this test system. PMID:26309725

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data

PubMed Central

Chen, Yi-Hau

2017-01-01

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed T2-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T2-statistic into an R package T2GA, which is available at https://github.com/roqe/T2GA. PMID:28622336

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.

PubMed

Lai, En-Yu; Chen, Yi-Hau; Wu, Kun-Pin

2017-06-01

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed T2-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T2-statistic into an R package T2GA, which is available at https://github.com/roqe/T2GA.

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling

PubMed Central

Shin, Junha; Lee, Insuk

2015-01-01

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life—Archaea, Bacteria, and Eukaryota—suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes. PMID:26394049

Note of the methodological flaws in the paper entitled "Polymorphisms in IL-4/IL-13 pathway genes and glioma risk: an updated meta-analysis".

PubMed

Wang, Ting-Ting; Li, Jin-Mei; Zhou, Dong

2016-01-01

With great interest, we read the paper "Polymorphisms in IL-4/IL-13 pathway genes and glioma risk: an updated meta-analysis" (by Chen PQ et al.) [1], which has reached important conclusions about the relationship between polymorphisms in interleukin (IL)-4/IL-13 pathway genes and glioma risk. Through quantitative analysis, the meta-analysis found no association between IL-4/IL-13 pathway genetic polymorphisms and glioma risk (Chen et al. in Tumor Biol 36:121-127, 2015). The meta-analysis is the most comprehensive study of polymorphisms in the IL-4/IL-13 pathway and glioma risk. Nevertheless, some deficiencies still exist in this meta-analysis that we would like to raise.

Integration of parallel 13 C-labeling experiments and in silico pathway analysis for enhanced production of ascomycin.

PubMed

Qi, Haishan; Lv, Mengmeng; Song, Kejing; Wen, Jianping

2017-05-01

Herein, the hyper-producing strain for ascomycin was engineered based on 13 C-labeling experiments and elementary flux modes analysis (EFMA). First, the metabolism of non-model organism Streptomyces hygroscopicus var. ascomyceticus SA68 was investigated and an updated network model was reconstructed using 13 C- metabolic flux analysis. Based on the precise model, EFMA was further employed to predict genetic targets for higher ascomycin production. Chorismatase (FkbO) and pyruvate carboxylase (Pyc) were predicted as the promising overexpression and deletion targets, respectively. The corresponding mutant TD-FkbO and TD-ΔPyc exhibited the consistency effects between model prediction and experimental results. Finally, the combined genetic manipulations were performed, achieving a high-yield ascomycin engineering strain TD-ΔPyc-FkbO with production up to 610 mg/L, 84.8% improvement compared with the parent strain SA68. These results manifested that the integration of 13 C-labeling experiments and in silico pathway analysis could serve as a promising concept to enhance ascomycin production, as well as other valuable products. Biotechnol. Bioeng. 2017;114: 1036-1044. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Solution state nuclear magnetic resonance spectroscopy for biological metabolism and pathway intermediate analysis.

PubMed

Nealon, Gareth L; Howard, Mark J

2016-12-15

Using nuclear magnetic resonance (NMR) spectroscopy in the study of metabolism has been immensely popular in medical- and health-related research but has yet to be widely applied to more fundamental biological problems. This review provides some NMR background relevant to metabolism, describes why 1 H NMR spectra are complex as well as introducing relevant terminology and definitions. The applications and practical considerations of NMR metabolic profiling and 13 C NMR-based flux analyses are discussed together with the elegant 'enzyme trap' approach for identifying novel metabolic pathway intermediates. The importance of sample preparation and data analysis are also described and explained with reference to data precision and multivariate analysis to introduce researchers unfamiliar with NMR and metabolism to consider this technique for their research interests. Finally, a brief glance into the future suggests NMR-based metabolism has room to expand in the 21st century through new isotope labels, and NMR technologies and methodologies. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Phosphorylation of ETS-1 is a critical event in DNA polymerase iota-induced invasion and metastasis of esophageal squamous cell carcinoma.

PubMed

He, Chao; Wu, Shuhua; Gao, Aidi; Su, Ye; Min, Han; Shang, Zeng-Fu; Wu, Jinchang; Yang, Li; Ding, Wei-Qun; Zhou, Jundong

2017-12-01

An aberrantly elevated expression of DNA polymerase ι (Pol ι) is significantly associated with poor prognosis of patients with esophageal squamous cell carcinoma (ESCC), yet the mechanisms behind this phenomenon remain obscure. Based on the RNA-Seq transcriptome and real-time PCR analysis, we identified ETS-1 as a candidate gene involved in Pol ι-mediated progression of ESCC. Wound-healing and transwell assay indicated that downregulation of ETS-1 attenuates Pol ι-mediated invasiveness of ESCC. Signaling pathway analysis showed that Pol ι enhances ETS-1 phosphorylation at threonine-38 through the Erk signaling pathway in ESCC cells. Kaplan-Meier analysis, based on 93 clinical tissue samples, revealed that ETS-1 phosphorylation at threonine-38 is associated with poor prognosis of ESCC patients. The present study thus demonstrates that phosphorylation of ETS-1 is a critical event in the Pol ι-induced invasion and metastasis of ESCC. © 2017 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

Hydrological landscape analysis based on digital elevation data

NASA Astrophysics Data System (ADS)

Seibert, J.; McGlynn, B.; Grabs, T.; Jensco, K.

2008-12-01

Topography is a major factor controlling both hydrological and soil processes at the landscape scale. While this is well-accepted qualitatively, quantifying relationships between topography and spatial variations of hydrologically relevant variables at the landscape scale still remains a challenging research topic. In this presentation, we describe hydrological landscape analysis HLA) as a way to derive relevant topographic indicies to describe the spatial variations of hydrological variables at the landscape scale. We demonstrate our HLA approach with four high-resolution digital elevation models (DEMs) from Sweden, Switzerland and Montana (USA). To investigate scale effects HLA metrics, we compared DEMs of different resolutions. These LiDAR-derived DEMs of 3m, 10m, and 30m, resolution represent catchments of ~ 5 km2 ranging from low to high relief. A central feature of HLA is the flowpath-based analysis of topography and the separation of hillslopes, riparian areas, and the stream network. We included the following metrics: riparian area delineation, riparian buffer potential, separation of stream inflows into right and left bank components, travel time proxies based on flowpath distances and gradients to the channel, and as a hydrologic similarity to the hypsometric curve we suggest the distribution of elevations above the stream network (computed based on the location where a certain flow pathway enters the stream). Several of these indices depended clearly on DEM resolution, whereas this effect was minor for others. While the hypsometric curves all were S-shaped the 'hillslope-hypsometric curves' had the shape of a power function with exponents less than 1. In a similar way we separated flow pathway lengths and gradients between hillslopes and streams and compared a topographic travel time proxy, which was based on the integration of gradients along the flow pathways. Besides the comparison of HLA-metrics for different catchments and DEM resolutions we present examples from experimental catchments to illustrate how these metrics can be used to describe catchment scale hydrological processes and provide context for plot scale observations.

Microkinetic modeling of H 2SO 4 formation on Pt based diesel oxidation catalysts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sharma, Hom N.; Sun, Yunwei; Glascoe, Elizabeth A.

The presence of water vapor and sulfur oxides in diesel engine exhaust leads to the formation of sulfuric acid (H 2SO 4), which severely impacts the performance of Pt/Pd based emissions aftertreatment catalysts. In this study, a microkinetic model is developed to investigate the reaction pathways of H 2SO 4 formation on Pt based diesel oxidation catalysts (DOCs). The microkinetic model consists of 14 elementary step reactions (7 reversible pairs) and yields prediction in excellent agreement with data obtained from experiments at practically relevant sulfur oxides environment in engine exhaust. The model simulation utilizing a steady-state plug flow reactor demonstratesmore » that it matches experimental data in both kinetically and thermodynamically controlled regions. Results clearly show the negative impact of SO 3 on the SO 2 oxidation light-off temperature, consistent with experimental observations. A reaction pathway analysis shows that the primary pathway of sulfuric acid formation on Pt surface involves SO 2* oxidation to form SO 3* with the subsequent interaction of SO 3* with H 2O* to form H 2SO 4*.« less

Microkinetic modeling of H 2SO 4 formation on Pt based diesel oxidation catalysts

DOE PAGES

Sharma, Hom N.; Sun, Yunwei; Glascoe, Elizabeth A.

2017-08-10

The presence of water vapor and sulfur oxides in diesel engine exhaust leads to the formation of sulfuric acid (H 2SO 4), which severely impacts the performance of Pt/Pd based emissions aftertreatment catalysts. In this study, a microkinetic model is developed to investigate the reaction pathways of H 2SO 4 formation on Pt based diesel oxidation catalysts (DOCs). The microkinetic model consists of 14 elementary step reactions (7 reversible pairs) and yields prediction in excellent agreement with data obtained from experiments at practically relevant sulfur oxides environment in engine exhaust. The model simulation utilizing a steady-state plug flow reactor demonstratesmore » that it matches experimental data in both kinetically and thermodynamically controlled regions. Results clearly show the negative impact of SO 3 on the SO 2 oxidation light-off temperature, consistent with experimental observations. A reaction pathway analysis shows that the primary pathway of sulfuric acid formation on Pt surface involves SO 2* oxidation to form SO 3* with the subsequent interaction of SO 3* with H 2O* to form H 2SO 4*.« less

Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake

PubMed Central

Do, Duy N.; Strathe, Anders B.; Ostersen, Tage; Pant, Sameer D.; Kadarmideen, Haja N.

2014-01-01

Residual feed intake (RFI) is a complex trait that is economically important for livestock production; however, the genetic and biological mechanisms regulating RFI are largely unknown in pigs. Therefore, the study aimed to identify single nucleotide polymorphisms (SNPs), candidate genes and biological pathways involved in regulating RFI using Genome-wide association (GWA) and pathway analyses. A total of 596 Yorkshire boars with phenotypes for two different measures of RFI (RFI1 and 2) and 60k genotypic data was used. GWA analysis was performed using a univariate mixed model and 12 and 7 SNPs were found to be significantly associated with RFI1 and RFI2, respectively. Several genes such as xin actin-binding repeat-containing protein 2 (XIRP2),tetratricopeptide repeat domain 29 (TTC29),suppressor of glucose, autophagy associated 1 (SOGA1),MAS1,G-protein-coupled receptor (GPCR) kinase 5 (GRK5),prospero-homeobox protein 1 (PROX1),GPCR 155 (GPR155), and FYVE domain containing the 26 (ZFYVE26) were identified as putative candidates for RFI based on their genomic location in the vicinity of these SNPs. Genes located within 50 kbp of SNPs significantly associated with RFI and RFI2 (q-value ≤ 0.2) were subsequently used for pathway analyses. These analyses were performed by assigning genes to biological pathways and then testing the association of individual pathways with RFI using a Fisher’s exact test. Metabolic pathway was significantly associated with both RFIs. Other biological pathways regulating phagosome, tight junctions, olfactory transduction, and insulin secretion were significantly associated with both RFI traits when relaxed threshold for cut-off p-value was used (p ≤ 0.05). These results implied porcine RFI is regulated by multiple biological mechanisms, although the metabolic processes might be the most important. Olfactory transduction pathway controlling the perception of feed via smell, insulin pathway controlling food intake might be important pathways for RFI. Furthermore, our study revealed key genes and genetic variants that control feed efficiency that could potentially be useful for genetic selection of more feed efficient pigs. PMID:25250046

Impact of care pathways for in-hospital management of COPD exacerbation: a systematic review.

PubMed

Lodewijckx, C; Sermeus, W; Panella, M; Deneckere, S; Leigheb, F; Decramer, M; Vanhaecht, K

2011-11-01

In-hospital management of COPD exacerbation is suboptimal, and outcomes are poor. Care pathways are a possible strategy for optimizing care processes and outcomes. The aim of the literature review was to explore characteristics of existing care pathways for in-hospital management of COPD exacerbations and to address their impact on performance of care processes, clinical outcomes, and team functioning. A literature search was conducted for articles published between 1990 and 2010 in the electronic databases of Medline, CINAHL, EMBASE, and Cochrane Library. Main inclusion criteria were (I) patients hospitalized for a COPD exacerbation; (II) implementation and evaluation of a care pathway; (III) report of original research, including experimental and quasi experimental designs, variance analysis, and interviews of professionals and patients about their perception on pathway effectiveness. Four studies with a quasi experimental design were included. Three studies used a pre-post test design; the fourth study was a non randomized controlled trial comparing an experimental group where patients were treated according to a care pathway with a control group where usual care was provided. The four studied care pathways were multidisciplinary structured care plans, outlining time-specific clinical interventions and responsibilities by discipline. Statistic analyses were rarely performed, and the trials used very divergent indicators to evaluate the impact of the care pathways. The studies described positive effects on blood sampling, daily weight measurement, arterial blood gas measurement, referral to rehabilitation, feelings of anxiety, length of stay, readmission, and in-hospital mortality. Research on COPD care pathways is very limited. The studies described few positive effects of the care pathways on diagnostic processes and on clinical outcomes. Though due to limited statistical analysis and weak design of the studies, the internal validity of results is limited. Therefore, based on these studies the impact of care pathways on COPD exacerbation is inconclusive. These findings indicate the need for properly designed research like a cluster randomized controlled trial to evaluate the impact of COPD care pathways on performance of care processes, clinical outcomes, and teamwork. Copyright © 2011 Elsevier Ltd. All rights reserved.