Biological Conversion of Sugars to Hydrocarbons Technology Pathway

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

Davis, Ryan; Biddy, Mary J.; Tan, Eric

2013-03-31

In support of the Bioenergy Technologies Office, the National Renewable Energy Laboratory (NREL) and the Pacific Northwest National Laboratory (PNNL) are undertaking studies of biomass conversion technologies to identify barriers and target research toward reducing conversion costs. Process designs and preliminary economic estimates for each of these pathway cases were developed using rigorous modeling tools (Aspen Plus and Chemcad). These analyses incorporated the best information available at the time of development, including data from recent pilot and bench-scale demonstrations, collaborative industrial and academic partners, and published literature and patents. This technology pathway case investigates the biological conversion of biomass derivedmore » sugars to hydrocarbon biofuels, utilizing data from recent literature references and information consistent with recent pilot scale demonstrations at NREL. Technical barriers and key research needs have been identified that should be pursued for the pathway to become competitive with petroleum-derived gasoline, diesel and jet range hydrocarbon blendstocks.« less

Integrative analyses of miRNA and proteomics identify potential biological pathways associated with onset of pulmonary fibrosis in the bleomycin rat model

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

Fukunaga, Satoki; Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., 3-1-98 Kasugade-Naka, Konohana-ku, Osaka 554-8558; Kakehashi, Anna

To determine miRNAs and their predicted target proteins regulatory networks which are potentially involved in onset of pulmonary fibrosis in the bleomycin rat model, we conducted integrative miRNA microarray and iTRAQ-coupled LC-MS/MS proteomic analyses, and evaluated the significance of altered biological functions and pathways. We observed that alterations of miRNAs and proteins are associated with the early phase of bleomycin-induced pulmonary fibrosis, and identified potential target pairs by using ingenuity pathway analysis. Using the data set of these alterations, it was demonstrated that those miRNAs, in association with their predicted target proteins, are potentially involved in canonical pathways reflective ofmore » initial epithelial injury and fibrogenic processes, and biofunctions related to induction of cellular development, movement, growth, and proliferation. Prediction of activated functions suggested that lung cells acquire proliferative, migratory, and invasive capabilities, and resistance to cell death especially in the very early phase of bleomycin-induced pulmonary fibrosis. The present study will provide new insights for understanding the molecular pathogenesis of idiopathic pulmonary fibrosis. - Highlights: • We analyzed bleomycin-induced pulmonary fibrosis in the rat. • Integrative analyses of miRNA microarray and proteomics were conducted. • We determined the alterations of miRNAs and their potential target proteins. • The alterations may control biological functions and pathways in pulmonary fibrosis. • Our result may provide new insights of pulmonary fibrosis.« less

Significant Deregulated Pathways in Diabetes Type II Complications Identified through Expression Based Network Biology

NASA Astrophysics Data System (ADS)

Ukil, Sanchaita; Sinha, Meenakshee; Varshney, Lavneesh; Agrawal, Shipra

Type 2 Diabetes is a complex multifactorial disease, which alters several signaling cascades giving rise to serious complications. It is one of the major risk factors for cardiovascular diseases. The present research work describes an integrated functional network biology approach to identify pathways that get transcriptionally altered and lead to complex complications thereby amplifying the phenotypic effect of the impaired disease state. We have identified two sub-network modules, which could be activated under abnormal circumstances in diabetes. Present work describes key proteins such as P85A and SRC serving as important nodes to mediate alternate signaling routes during diseased condition. P85A has been shown to be an important link between stress responsive MAPK and CVD markers involved in fibrosis. MAPK8 has been shown to interact with P85A and further activate CTGF through VEGF signaling. We have traced a novel and unique route correlating inflammation and fibrosis by considering P85A as a key mediator of signals. The next sub-network module shows SRC as a junction for various signaling processes, which results in interaction between NF-kB and beta catenin to cause cell death. The powerful interaction between these important genes in response to transcriptionally altered lipid metabolism and impaired inflammatory response via SRC causes apoptosis of cells. The crosstalk between inflammation, lipid homeostasis and stress, and their serious effects downstream have been explained in the present analyses.

An overview of bioinformatics methods for modeling biological pathways in yeast

PubMed Central

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao

2016-01-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein–protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae. In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways in S. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. PMID:26476430

Constructing biological pathway models with hybrid functional Petri nets.

PubMed

Doi, Atsushi; Fujita, Sachie; Matsuno, Hiroshi; Nagasaki, Masao; Miyano, Satoru

2004-01-01

In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.

Constructing biological pathway models with hybrid functional petri nets.

PubMed

Doi, Atsushi; Fujita, Sachie; Matsuno, Hiroshi; Nagasaki, Masao; Miyano, Satoru

2011-01-01

In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.

A Systems Toxicology Approach Reveals Biological Pathways Dysregulated by Prenatal Arsenic Exposure

PubMed Central

Laine, Jessica E.; Fry, Rebecca C.

2016-01-01

BACKGROUND Prenatal exposure to inorganic arsenic (iAs) is associated with dysregulated gene and protein expression in the fetus, both evident at birth. Potential epigenetic mechanisms that underlie these changes include but are not limited to the methylation of cytosines (CpG). OBJECTIVE The aim of the present study was to compile datasets from studies on prenatal arsenic exposure to identify whether key genes, proteins, or both and their associated biological pathways are perturbed. METHODS We compiled datasets from 12 studies that analyzed the relationship between prenatal iAs exposure and fetal changes to the epigenome (5-methyl cytosine), transcriptome (mRNA expression), and/or proteome (protein expression changes). FINDINGS Across the 12 studies, a set of 845 unique genes was identified and found to enrich for their role in biological pathways, including those signaled by peroxisome proliferator-activated receptor, nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, and the glucocorticoid receptor. Tumor necrosis factor was identified as a putative cellular regulator underlying most (n = 277) of the identified iAs-associated genes or proteins. CONCLUSIONS Given their common identification across numerous human cohorts and their known toxicologic role in disease, the identified genes and pathways may underlie altered disease susceptibility associated with prenatal exposure to iAs. PMID:27325076

An overview of bioinformatics methods for modeling biological pathways in yeast.

PubMed

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao; Cheng, Jianlin

2016-03-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Molecular profiles to biology and pathways: a systems biology approach.

PubMed

Van Laere, Steven; Dirix, Luc; Vermeulen, Peter

2016-06-16

Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.

PathNER: a tool for systematic identification of biological pathway mentions in the literature

PubMed Central

2013-01-01

Background Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature. Results We developed a tool, named PathNER (Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase. Conclusions In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at http://sourceforge.net/projects/pathner/. PMID:24555844

[Exploration of common biological pathways for attention deficit hyperactivity disorder and low birth weight].

PubMed

Xiang, Bo; Yu, Minglan; Liang, Xuemei; Lei, Wei; Huang, Chaohua; Chen, Jing; He, Wenying; Zhang, Tao; Li, Tao; Liu, Kezhi

2017-12-10

To explore common biological pathways for attention deficit hyperactivity disorder (ADHD) and low birth weight (LBW). Thei-Gsea4GwasV2 software was used to analyze the result of genome-wide association analysis (GWAS) for LBW (pathways were derived from Reactome), and nominally significant (P< 0.05, FDR< 0.25) pathways were tested for replication in ADHD.Significant pathways were analyzed with DAPPLE and Reatome FI software to identify genes involved in such pathways, with each cluster enriched with the gene ontology (GO). The Centiscape2.0 software was used to calculate the degree of genetic networks and the betweenness value to explore the core node (gene). Weighed gene co-expression network analysis (WGCNA) was then used to explore the co-expression of genes in these pathways.With gene expression data derived from BrainSpan, GO enrichment was carried out for each gene module. Eleven significant biological pathways was identified in association with LBW, among which two (Selenoamino acid metabolism and Diseases associated with glycosaminoglycan metabolism) were replicated during subsequent ADHD analysis. Network analysis of 130 genes in these pathways revealed that some of the sub-networksare related with morphology of cerebellum, development of hippocampus, and plasticity of synaptic structure. Upon co-expression network analysis, 120 genes passed the quality control and were found to express in 3 gene modules. These modules are mainly related to the regulation of synaptic structure and activity regulation. ADHD and LBW share some biological regulation processes. Anomalies of such proces sesmay predispose to ADHD.

Presenilin-Based Genetic Screens in Drosophila melanogaster Identify Novel Notch Pathway Modifiers

PubMed Central

Mahoney, Matt B.; Parks, Annette L.; Ruddy, David A.; Tiong, Stanley Y. K.; Esengil, Hanife; Phan, Alexander C.; Philandrinos, Panos; Winter, Christopher G.; Chatterjee, Runa; Huppert, Kari; Fisher, William W.; L'Archeveque, Lynn; Mapa, Felipa A.; Woo, Wendy; Ellis, Michael C.; Curtis, Daniel

2006-01-01

Presenilin is the enzymatic component of γ-secretase, a multisubunit intramembrane protease that processes several transmembrane receptors, such as the amyloid precursor protein (APP). Mutations in human Presenilins lead to altered APP cleavage and early-onset Alzheimer's disease. Presenilins also play an essential role in Notch receptor cleavage and signaling. The Notch pathway is a highly conserved signaling pathway that functions during the development of multicellular organisms, including vertebrates, Drosophila, and C. elegans. Recent studies have shown that Notch signaling is sensitive to perturbations in subcellular trafficking, although the specific mechanisms are largely unknown. To identify genes that regulate Notch pathway function, we have performed two genetic screens in Drosophila for modifiers of Presenilin-dependent Notch phenotypes. We describe here the cloning and identification of 19 modifiers, including nicastrin and several genes with previously undescribed involvement in Notch biology. The predicted functions of these newly identified genes are consistent with extracellular matrix and vesicular trafficking mechanisms in Presenilin and Notch pathway regulation and suggest a novel role for γ-tubulin in the pathway. PMID:16415372

Presenilin-based genetic screens in Drosophila melanogaster identify novel notch pathway modifiers.

PubMed

Mahoney, Matt B; Parks, Annette L; Ruddy, David A; Tiong, Stanley Y K; Esengil, Hanife; Phan, Alexander C; Philandrinos, Panos; Winter, Christopher G; Chatterjee, Runa; Huppert, Kari; Fisher, William W; L'Archeveque, Lynn; Mapa, Felipa A; Woo, Wendy; Ellis, Michael C; Curtis, Daniel

2006-04-01

Presenilin is the enzymatic component of gamma-secretase, a multisubunit intramembrane protease that processes several transmembrane receptors, such as the amyloid precursor protein (APP). Mutations in human Presenilins lead to altered APP cleavage and early-onset Alzheimer's disease. Presenilins also play an essential role in Notch receptor cleavage and signaling. The Notch pathway is a highly conserved signaling pathway that functions during the development of multicellular organisms, including vertebrates, Drosophila, and C. elegans. Recent studies have shown that Notch signaling is sensitive to perturbations in subcellular trafficking, although the specific mechanisms are largely unknown. To identify genes that regulate Notch pathway function, we have performed two genetic screens in Drosophila for modifiers of Presenilin-dependent Notch phenotypes. We describe here the cloning and identification of 19 modifiers, including nicastrin and several genes with previously undescribed involvement in Notch biology. The predicted functions of these newly identified genes are consistent with extracellular matrix and vesicular trafficking mechanisms in Presenilin and Notch pathway regulation and suggest a novel role for gamma-tubulin in the pathway.

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.

The multiscale backbone of the human phenotype network based on biological pathways.

PubMed

Darabos, Christian; White, Marquitta J; Graham, Britney E; Leung, Derek N; Williams, Scott M; Moore, Jason H

2014-01-25

Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments.

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

PubMed Central

Waagmeester, Andra; Pico, Alexander R.

2016-01-01

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web. PMID:27336457

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources.

PubMed

Waagmeester, Andra; Kutmon, Martina; Riutta, Anders; Miller, Ryan; Willighagen, Egon L; Evelo, Chris T; Pico, Alexander R

2016-06-01

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

e-Science and biological pathway semantics

PubMed Central

Luciano, Joanne S; Stevens, Robert D

2007-01-01

Background The development of e-Science presents a major set of opportunities and challenges for the future progress of biological and life scientific research. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. Nowhere is the demand greater than in the semantic integration of these data. Semantic Web tools and technologies afford the chance to achieve this semantic integration. Since pathway knowledge is central to much of the scientific research today it is a good test-bed for semantic integration. Within the context of biological pathways, the BioPAX initiative, part of a broader movement towards the standardization and integration of life science databases, forms a necessary prerequisite for its successful application of e-Science in health care and life science research. This paper examines whether BioPAX, an effort to overcome the barrier of disparate and heterogeneous pathway data sources, addresses the needs of e-Science. Results We demonstrate how BioPAX pathway data can be used to ask and answer some useful biological questions. We find that BioPAX comes close to meeting a broad range of e-Science needs, but certain semantic weaknesses mean that these goals are missed. We make a series of recommendations for re-modeling some aspects of BioPAX to better meet these needs. Conclusion Once these semantic weaknesses are addressed, it will be possible to integrate pathway information in a manner that would be useful in e-Science. PMID:17493286

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

Bayesian parameter estimation for nonlinear modelling of biological pathways.

PubMed

Ghasemi, Omid; Lindsey, Merry L; Yang, Tianyi; Nguyen, Nguyen; Huang, Yufei; Jin, Yu-Fang

2011-01-01

The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems. Our proposed

[Advance in flavonoids biosynthetic pathway and synthetic biology].

PubMed

Zou, Li-Qiu; Wang, Cai-Xia; Kuang, Xue-Jun; Li, Ying; Sun, Chao

2016-11-01

Flavonoids are the valuable components in medicinal plants, which possess a variety of pharmacological activities, including anti-tumor, antioxidant and anti-inflammatory activities. There is an unambiguous understanding about flavonoids biosynthetic pathway, that is,2S-flavanones including naringenin and pinocembrin are the skeleton of other flavonoids and they can transform to other flavonoids through branched metabolic pathway. Elucidation of the flavonoids biosynthetic pathway lays a solid foundation for their synthetic biology. A few flavonoids have been produced in Escherichia coli or yeast with synthetic biological technologies, such as naringenin, pinocembrin and fisetin. Synthetic biology will provide a new way to get valuable flavonoids and promote the research and development of flavonoid drugs and health products, making flavonoids play more important roles in human diet and health. Copyright© by the Chinese Pharmaceutical Association.

Relation extraction for biological pathway construction using node2vec.

PubMed

Kim, Munui; Baek, Seung Han; Song, Min

2018-06-13

Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk. Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction.

Pathway-driven gene stability selection of two rheumatoid arthritis GWAS identifies and validates new susceptibility genes in receptor mediated signalling pathways.

PubMed

Eleftherohorinou, Hariklia; Hoggart, Clive J; Wright, Victoria J; Levin, Michael; Coin, Lachlan J M

2011-09-01

Rheumatoid arthritis (RA) is the commonest chronic, systemic, inflammatory disorder affecting ∼1% of the world population. It has a strong genetic component and a growing number of associated genes have been discovered in genome-wide association studies (GWAS), which nevertheless only account for 23% of the total genetic risk. We aimed to identify additional susceptibility loci through the analysis of GWAS in the context of biological function. We bridge the gap between pathway and gene-oriented analyses of GWAS, by introducing a pathway-driven gene stability-selection methodology that identifies potential causal genes in the top-associated disease pathways that may be driving the pathway association signals. We analysed the WTCCC and the NARAC studies of ∼5000 and ∼2000 subjects, respectively. We examined 700 pathways comprising ∼8000 genes. Ranking pathways by significance revealed that the NARAC top-ranked ∼6% laid within the top 10% of WTCCC. Gene selection on those pathways identified 58 genes in WTCCC and 61 in NARAC; 21 of those were common (P(overlap)< 10(-21)), of which 16 were novel discoveries. Among the identified genes, we validated 10 known RA associations in WTCCC and 13 in NARAC, not discovered using single-SNP approaches on the same data. Gene ontology functional enrichment analysis on the identified genes showed significant over-representation of signalling activity (P< 10(-29)) in both studies. Our findings suggest a novel model of RA genetic predisposition, which involves cell-membrane receptors and genes in second messenger signalling systems, in addition to genes that regulate immune responses, which have been the focus of interest previously.

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.

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/.

Novel Myopia Genes and Pathways Identified From Syndromic Forms of Myopia

PubMed Central

Loughman, James; Wildsoet, Christine F.; Williams, Cathy; Guggenheim, Jeremy A.

2018-01-01

Purpose To test the hypothesis that genes known to cause clinical syndromes featuring myopia also harbor polymorphisms contributing to nonsyndromic refractive errors. Methods Clinical phenotypes and syndromes that have refractive errors as a recognized feature were identified using the Online Mendelian Inheritance in Man (OMIM) database. One hundred fifty-four unique causative genes were identified, of which 119 were specifically linked with myopia and 114 represented syndromic myopia (i.e., myopia and at least one other clinical feature). Myopia was the only refractive error listed for 98 genes and hyperopia and the only refractive error noted for 28 genes, with the remaining 28 genes linked to phenotypes with multiple forms of refractive error. Pathway analysis was carried out to find biological processes overrepresented within these sets of genes. Genetic variants located within 50 kb of the 119 myopia-related genes were evaluated for involvement in refractive error by analysis of summary statistics from genome-wide association studies (GWAS) conducted by the CREAM Consortium and 23andMe, using both single-marker and gene-based tests. Results Pathway analysis identified several biological processes already implicated in refractive error development through prior GWAS analyses and animal studies, including extracellular matrix remodeling, focal adhesion, and axon guidance, supporting the research hypothesis. Novel pathways also implicated in myopia development included mannosylation, glycosylation, lens development, gliogenesis, and Schwann cell differentiation. Hyperopia was found to be linked to a different pattern of biological processes, mostly related to organogenesis. Comparison with GWAS findings further confirmed that syndromic myopia genes were enriched for genetic variants that influence refractive errors in the general population. Gene-based analyses implicated 21 novel candidate myopia genes (ADAMTS18, ADAMTS2, ADAMTSL4, AGK, ALDH18A1, ASXL1, COL4A1

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

PubMed

Singh, Nitesh Kumar; Ernst, Mathias; Liebscher, Volkmar; Fuellen, Georg; Taher, Leila

2016-10-20

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

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

Identifying pathways affected by cancer mutations.

PubMed

Iengar, Prathima

2017-12-16

Mutations in 15 cancers, sourced from the COSMIC Whole Genomes database, and 297 human pathways, arranged into pathway groups based on the processes they orchestrate, and sourced from the KEGG pathway database, have together been used to identify pathways affected by cancer mutations. Genes studied in ≥15, and mutated in ≥10 samples of a cancer have been considered recurrently mutated, and pathways with recurrently mutated genes have been considered affected in the cancer. Novel doughnut plots have been presented which enable visualization of the extent to which pathways and genes, in each pathway group, are targeted, in each cancer. The 'organismal systems' pathway group (including organism-level pathways; e.g., nervous system) is the most targeted, more than even the well-recognized signal transduction, cell-cycle and apoptosis, and DNA repair pathway groups. The important, yet poorly-recognized, role played by the group merits attention. Pathways affected in ≥7 cancers yielded insights into processes affected. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

FamNet: A Framework to Identify Multiplied Modules Driving Pathway Expansion in Plants1

PubMed Central

Tohge, Takayuki; Klie, Sebastian; Fernie, Alisdair R.

2016-01-01

Gene duplications generate new genes that can acquire similar but often diversified functions. Recent studies of gene coexpression networks have indicated that, not only genes, but also pathways can be multiplied and diversified to perform related functions in different parts of an organism. Identification of such diversified pathways, or modules, is needed to expand our knowledge of biological processes in plants and to understand how biological functions evolve. However, systematic explorations of modules remain scarce, and no user-friendly platform to identify them exists. We have established a statistical framework to identify modules and show that approximately one-third of the genes of a plant’s genome participate in hundreds of multiplied modules. Using this framework as a basis, we implemented a platform that can explore and visualize multiplied modules in coexpression networks of eight plant species. To validate the usefulness of the platform, we identified and functionally characterized pollen- and root-specific cell wall modules that multiplied to confer tip growth in pollen tubes and root hairs, respectively. Furthermore, we identified multiplied modules involved in secondary metabolite synthesis and corroborated them by metabolite profiling of tobacco (Nicotiana tabacum) tissues. The interactive platform, referred to as FamNet, is available at http://www.gene2function.de/famnet.html. PMID:26754669

Impact of constitutional copy number variants on biological pathway evolution.

PubMed

Poptsova, Maria; Banerjee, Samprit; Gokcumen, Omer; Rubin, Mark A; Demichelis, Francesca

2013-01-23

Inherited Copy Number Variants (CNVs) can modulate the expression levels of individual genes. However, little is known about how CNVs alter biological pathways and how this varies across different populations. To trace potential evolutionary changes of well-described biological pathways, we jointly queried the genomes and the transcriptomes of a collection of individuals with Caucasian, Asian or Yoruban descent combining high-resolution array and sequencing data. We implemented an enrichment analysis of pathways accounting for CNVs and genes sizes and detected significant enrichment not only in signal transduction and extracellular biological processes, but also in metabolism pathways. Upon the estimation of CNV population differentiation (CNVs with different polymorphism frequencies across populations), we evaluated that 22% of the pathways contain at least one gene that is proximal to a CNV (CNV-gene pair) that shows significant population differentiation. The majority of these CNV-gene pairs belong to signal transduction pathways and 6% of the CNV-gene pairs show statistical association between the copy number states and the transcript levels. The analysis suggested possible examples of positive selection within individual populations including NF-kB, MAPK signaling pathways, and Alu/L1 retrotransposition factors. Altogether, our results suggest that constitutional CNVs may modulate subtle pathway changes through specific pathway enzymes, which may become fixed in some populations.

PathJam: a new service for integrating biological pathway information.

PubMed

Glez-Peña, Daniel; Reboiro-Jato, Miguel; Domínguez, Rubén; Gómez-López, Gonzalo; Pisano, David G; Fdez-Riverola, Florentino

2010-10-28

Biological pathways are crucial to much of the scientific research today including the study of specific biological processes related with human diseases. PathJam is a new comprehensive and freely accessible web-server application integrating scattered human pathway annotation from several public sources. The tool has been designed for both (i) being intuitive for wet-lab users providing statistical enrichment analysis of pathway annotations and (ii) giving support to the development of new integrative pathway applications. PathJam’s unique features and advantages include interactive graphs linking pathways and genes of interest, downloadable results in fully compatible formats, GSEA compatible output files and a standardized RESTful API.

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.

Impact of constitutional copy number variants on biological pathway evolution

PubMed Central

2013-01-01

Background Inherited Copy Number Variants (CNVs) can modulate the expression levels of individual genes. However, little is known about how CNVs alter biological pathways and how this varies across different populations. To trace potential evolutionary changes of well-described biological pathways, we jointly queried the genomes and the transcriptomes of a collection of individuals with Caucasian, Asian or Yoruban descent combining high-resolution array and sequencing data. Results We implemented an enrichment analysis of pathways accounting for CNVs and genes sizes and detected significant enrichment not only in signal transduction and extracellular biological processes, but also in metabolism pathways. Upon the estimation of CNV population differentiation (CNVs with different polymorphism frequencies across populations), we evaluated that 22% of the pathways contain at least one gene that is proximal to a CNV (CNV-gene pair) that shows significant population differentiation. The majority of these CNV-gene pairs belong to signal transduction pathways and 6% of the CNV-gene pairs show statistical association between the copy number states and the transcript levels. Conclusions The analysis suggested possible examples of positive selection within individual populations including NF-kB, MAPK signaling pathways, and Alu/L1 retrotransposition factors. Altogether, our results suggest that constitutional CNVs may modulate subtle pathway changes through specific pathway enzymes, which may become fixed in some populations. PMID:23342974

Synergy and Interactions Among Biological Pathways Leading to Preterm Premature Rupture of Membranes

PubMed Central

Lannon, Sophia M. R.; Vanderhoeven, Jeroen P.; Eschenbach, David A.; Gravett, Michael G.; Waldorf, Kristina M. Adams

2014-01-01

Preterm premature rupture of membranes (PPROM) occurs in 1% to 2% of births. Impact of PPROM is greatest in low- and middle-income countries where prematurity-related deaths are most common. Recent investigations identify cytokine and matrix metalloproteinase activation, oxidative stress, and apoptosis as primary pathways to PPROM. These biological processes are initiated by heterogeneous etiologies including infection/inflammation, placental bleeding, uterine overdistention, and genetic polymorphisms. We hypothesize that pathways to PPROM overlap and act synergistically to weaken membranes. We focus our discussion on membrane composition and strength, pathways linking risk factors to membrane weakening, and future research directions to reduce the global burden of PPROM. PMID:24840939

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.

Use of a bovine genome chip to identify new biological pathways for beef quality in cattle.

PubMed

Guifen, Liu; Xiaomu, Liu; Fachun, Wan; Xiuwen, Tan; Haijian, Cheng; Enliang, Song

2012-12-01

The accumulation of muscle is largely influenced by the genetic background of cattle. Muscle tissue was collected from the longissimus muscle of Lilu beef cattle at 12, 18, 24 and 30 months old. Using meat quality analysis, we found that the Lilu beef cattle have good production and slaughter performance, the performance meets the criterion of beef cattle. Microarray analysis was able to identify a total of 4,219 genes that are differentially expressed (P ≤ 0.01) between the two groups of cattle (12 vs 18; 18 vs 24; 24 vs 30). Bioinformatics analysis results suggested that most of the differentially expressed genes are involved in the metabolic pathways and neuroactive ligand-receptor interaction pathways. In the future study that aims to look for genes relating to growth and meat quality, we will focus on the genes that have been shown to have a significant variation between groups and are involved in the two pathways.

GOSAP: Gene Ontology-Based Semantic Alignment of Biological Pathways.

PubMed

Gamalielsson, Jonas; Olsson, Bjorn

2008-01-01

We present a new method for semantic comparison of biological pathways, aiming to discover evolutionary conservation of pathways between species. Our method uses all three sub-ontologies of Gene Ontology (GO) and a measure of semantic similarity to calculate match scores between gene products. These scores are used for finding local pairwise pathway alignments. This approach has the advantage of being applicable to all types of pathways where nodes are gene products, e.g., regulatory pathways, signalling pathways and metabolic enzyme-to-enzyme pathways. We demonstrate the usefulness of the method using regulatory and metabolic pathways from E. coli and S. cerevisiae as examples.

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.

An Integrated Human/Murine Transcriptome and Pathway Approach To Identify Prenatal Treatments For Down Syndrome.

PubMed

Guedj, Faycal; Pennings, Jeroen LA; Massingham, Lauren J; Wick, Heather C; Siegel, Ashley E; Tantravahi, Umadevi; Bianchi, Diana W

2016-09-02

Anatomical and functional brain abnormalities begin during fetal life in Down syndrome (DS). We hypothesize that novel prenatal treatments can be identified by targeting signaling pathways that are consistently perturbed in cell types/tissues obtained from human fetuses with DS and mouse embryos. We analyzed transcriptome data from fetuses with trisomy 21, age and sex-matched euploid controls, and embryonic day 15.5 forebrains from Ts1Cje, Ts65Dn, and Dp16 mice. The new datasets were compared to other publicly available datasets from humans with DS. We used the human Connectivity Map (CMap) database and created a murine adaptation to identify FDA-approved drugs that can rescue affected pathways. USP16 and TTC3 were dysregulated in all affected human cells and two mouse models. DS-associated pathway abnormalities were either the result of gene dosage specific effects or the consequence of a global cell stress response with activation of compensatory mechanisms. CMap analyses identified 56 molecules with high predictive scores to rescue abnormal gene expression in both species. Our novel integrated human/murine systems biology approach identified commonly dysregulated genes and pathways. This can help to prioritize therapeutic molecules on which to further test safety and efficacy. Additional studies in human cells are ongoing prior to pre-clinical prenatal treatment in mice.

An Integrative data mining approach to identifying Adverse Outcome Pathway (AOP) Signatures

EPA Science Inventory

The Adverse Outcome Pathway (AOP) framework is a tool for making biological connections and summarizing key information across different levels of biological organization to connect biological perturbations at the molecular level to adverse outcomes for an individual or populatio...

A taxonomy of visualization tasks for the analysis of biological pathway data.

PubMed

Murray, Paul; McGee, Fintan; Forbes, Angus G

2017-02-15

Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity. Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy. Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts.

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

A Novel Method to Identify Differential Pathways in Hippocampus Alzheimer's Disease.

PubMed

Liu, Chun-Han; Liu, Lian

2017-05-08

BACKGROUND Alzheimer's disease (AD) is the most common type of dementia. The objective of this paper is to propose a novel method to identify differential pathways in hippocampus AD. MATERIAL AND METHODS We proposed a combined method by merging existed methods. Firstly, pathways were identified by four known methods (DAVID, the neaGUI package, the pathway-based co-expressed method, and the pathway network approach), and differential pathways were evaluated through setting weight thresholds. Subsequently, we combined all pathways by a rank-based algorithm and called the method the combined method. Finally, common differential pathways across two or more of five methods were selected. RESULTS Pathways obtained from different methods were also different. The combined method obtained 1639 pathways and 596 differential pathways, which included all pathways gained from the four existing methods; hence, the novel method solved the problem of inconsistent results. Besides, a total of 13 common pathways were identified, such as metabolism, immune system, and cell cycle. CONCLUSIONS We have proposed a novel method by combining four existing methods based on a rank product algorithm, and identified 13 significant differential pathways based on it. These differential pathways might provide insight into treatment and diagnosis of hippocampus AD.

PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization.

PubMed

Dogrusoz, U; Erson, E Z; Giral, E; Demir, E; Babur, O; Cetintas, A; Colak, R

2006-02-01

Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.

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

A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes

PubMed Central

Dutta, B; Pusztai, L; Qi, Y; André, F; Lazar, V; Bianchini, G; Ueno, N; Agarwal, R; Wang, B; Shiang, C Y; Hortobagyi, G N; Mills, G B; Symmans, W F; Balázsi, G

2012-01-01

Background: The rapid collection of diverse genome-scale data raises the urgent need to integrate and utilise these resources for biological discovery or biomedical applications. For example, diverse transcriptomic and gene copy number variation data are currently collected for various cancers, but relatively few current methods are capable to utilise the emerging information. Methods: We developed and tested a data-integration method to identify gene networks that drive the biology of breast cancer clinical subtypes. The method simultaneously overlays gene expression and gene copy number data on protein–protein interaction, transcriptional-regulatory and signalling networks by identifying coincident genomic and transcriptional disturbances in local network neighborhoods. Results: We identified distinct driver-networks for each of the three common clinical breast cancer subtypes: oestrogen receptor (ER)+, human epidermal growth factor receptor 2 (HER2)+, and triple receptor-negative breast cancers (TNBC) from patient and cell line data sets. Driver-networks inferred from independent datasets were significantly reproducible. We also confirmed the functional relevance of a subset of randomly selected driver-network members for TNBC in gene knockdown experiments in vitro. We found that TNBC driver-network members genes have increased functional specificity to TNBC cell lines and higher functional sensitivity compared with genes selected by differential expression alone. Conclusion: Clinical subtype-specific driver-networks identified through data integration are reproducible and functionally important. PMID:22343619

Interleukins and their signaling pathways in the Reactome biological pathway database.

PubMed

Jupe, Steve; Ray, Keith; Roca, Corina Duenas; Varusai, Thawfeek; Shamovsky, Veronica; Stein, Lincoln; D'Eustachio, Peter; Hermjakob, Henning

2018-04-01

There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as

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

A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

PubMed Central

Han, Junwei; Li, Chunquan; Yang, Haixiu; Xu, Yanjun; Zhang, Chunlong; Ma, Jiquan; Shi, Xinrui; Liu, Wei; Shang, Desi; Yao, Qianlan; Zhang, Yunpeng; Su, Fei; Feng, Li; Li, Xia

2015-01-01

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools (http://bioinfo.hrbmu.edu.cn/PAGI). PMID:25551156

Stress and DNA repair biology of the Fanconi anemia pathway

PubMed Central

Longerich, Simonne; Li, Jian; Xiong, Yong; Sung, Patrick

2014-01-01

Fanconi anemia (FA) represents a paradigm of rare genetic diseases, where the quest for cause and cure has led to seminal discoveries in cancer biology. Although a total of 16 FA genes have been identified thus far, the biochemical function of many of the FA proteins remains to be elucidated. FA is rare, yet the fact that 5 FA genes are in fact familial breast cancer genes and FA gene mutations are found frequently in sporadic cancers suggest wider applicability in hematopoiesis and oncology. Establishing the interaction network involving the FA proteins and their associated partners has revealed an intersection of FA with several DNA repair pathways, including homologous recombination, DNA mismatch repair, nucleotide excision repair, and translesion DNA synthesis. Importantly, recent studies have shown a major involvement of the FA pathway in the tolerance of reactive aldehydes. Moreover, despite improved outcomes in stem cell transplantation in the treatment of FA, many challenges remain in patient care. PMID:25237197

A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.

PubMed

Chang, Tzu-Hao; Wu, Shih-Lin; Wang, Wei-Jen; Horng, Jorng-Tzong; Chang, Cheng-Wei

2014-01-01

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

Integrated Analysis of Mutation Data from Various Sources Identifies Key Genes and Signaling Pathways in Hepatocellular Carcinoma

PubMed Central

Wei, Lin; Tang, Ruqi; Lian, Baofeng; Zhao, Yingjun; He, Xianghuo; Xie, Lu

2014-01-01

Background Recently, a number of studies have performed genome or exome sequencing of hepatocellular carcinoma (HCC) and identified hundreds or even thousands of mutations in protein-coding genes. However, these studies have only focused on a limited number of candidate genes, and many important mutation resources remain to be explored. Principal Findings In this study, we integrated mutation data obtained from various sources and performed pathway and network analysis. We identified 113 pathways that were significantly mutated in HCC samples and found that the mutated genes included in these pathways contained high percentages of known cancer genes, and damaging genes and also demonstrated high conservation scores, indicating their important roles in liver tumorigenesis. Five classes of pathways that were mutated most frequently included (a) proliferation and apoptosis related pathways, (b) tumor microenvironment related pathways, (c) neural signaling related pathways, (d) metabolic related pathways, and (e) circadian related pathways. Network analysis further revealed that the mutated genes with the highest betweenness coefficients, such as the well-known cancer genes TP53, CTNNB1 and recently identified novel mutated genes GNAL and the ADCY family, may play key roles in these significantly mutated pathways. Finally, we highlight several key genes (e.g., RPS6KA3 and PCLO) and pathways (e.g., axon guidance) in which the mutations were associated with clinical features. Conclusions Our workflow illustrates the increased statistical power of integrating multiple studies of the same subject, which can provide biological insights that would otherwise be masked under individual sample sets. This type of bioinformatics approach is consistent with the necessity of making the best use of the ever increasing data provided in valuable databases, such as TCGA, to enhance the speed of deciphering human cancers. PMID:24988079

Integrated analysis of mutation data from various sources identifies key genes and signaling pathways in hepatocellular carcinoma.

PubMed

Zhang, Yuannv; Qiu, Zhaoping; Wei, Lin; Tang, Ruqi; Lian, Baofeng; Zhao, Yingjun; He, Xianghuo; Xie, Lu

2014-01-01

Recently, a number of studies have performed genome or exome sequencing of hepatocellular carcinoma (HCC) and identified hundreds or even thousands of mutations in protein-coding genes. However, these studies have only focused on a limited number of candidate genes, and many important mutation resources remain to be explored. In this study, we integrated mutation data obtained from various sources and performed pathway and network analysis. We identified 113 pathways that were significantly mutated in HCC samples and found that the mutated genes included in these pathways contained high percentages of known cancer genes, and damaging genes and also demonstrated high conservation scores, indicating their important roles in liver tumorigenesis. Five classes of pathways that were mutated most frequently included (a) proliferation and apoptosis related pathways, (b) tumor microenvironment related pathways, (c) neural signaling related pathways, (d) metabolic related pathways, and (e) circadian related pathways. Network analysis further revealed that the mutated genes with the highest betweenness coefficients, such as the well-known cancer genes TP53, CTNNB1 and recently identified novel mutated genes GNAL and the ADCY family, may play key roles in these significantly mutated pathways. Finally, we highlight several key genes (e.g., RPS6KA3 and PCLO) and pathways (e.g., axon guidance) in which the mutations were associated with clinical features. Our workflow illustrates the increased statistical power of integrating multiple studies of the same subject, which can provide biological insights that would otherwise be masked under individual sample sets. This type of bioinformatics approach is consistent with the necessity of making the best use of the ever increasing data provided in valuable databases, such as TCGA, to enhance the speed of deciphering human cancers.

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

PubMed Central

King, Zachary A.; Dräger, Andreas; Ebrahim, Ali; Sonnenschein, Nikolaus; Lewis, Nathan E.; Palsson, Bernhard O.

2015-01-01

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools. PMID:26313928

The oxalate-carbonate pathway: at the interface between biology and geology

NASA Astrophysics Data System (ADS)

Junier, P.; Cailleau, G.; Martin, G.; Guggiari, M.; Bravo, D.; Clerc, M.; Aragno, M.; Job, D.; Verrecchia, E.

2012-04-01

The formation of calcite in otherwise carbonate-free acidic soils through the biological degradation of oxalate is a mechanism termed oxalate-carbonate pathway. This pathway lies at the interface between biological and geological systems and constitutes an important, although underestimated, soil mineral carbon sink. In this case, atmospheric CO2 is fixed by the photosynthetic activity of oxalogenic plants, which is partly destined to the production of oxalate used for the chelation of metals, and particularly, calcium. Fungi are also able to produce oxalate to cope with elevated concentrations of metals. In spite of its abundance as a substrate, oxalate is a very stable organic anion that can be metabolized only by a group of bacteria that use it as carbon and energy sources. These bacteria close the biological cycle by degrading calcium oxalate, releasing Ca2+ and inducing a change in local soil pH. If parameters are favourable, the geological part of the pathway begins, because this change in pH will indirectly lead to the precipitation of secondary calcium carbonate (calcite) in unexpected geological conditions. Due to the initial acidic soil conditions, and the absence of geological carbonate in the basement, it is unexpected to find C in the form of calcite. The activity of the oxalate-carbonate pathway has now been demonstrated in several places around the world, suggesting that its importance can be even greater than expected. In addition, new roles for each of the biological players of the pathway have been revealed recently forcing us to reconsider a global biogeochemical model for oxalate cycling.

Inference of Evolutionary Forces Acting on Human Biological Pathways

PubMed Central

Daub, Josephine T.; Dupanloup, Isabelle; Robinson-Rechavi, Marc; Excoffier, Laurent

2015-01-01

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald–Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures. PMID:25971280

Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps

PubMed Central

Silver, Matt; Montana, Giovanni

2012-01-01

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways. We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our “pathways group lasso with adaptive weights” (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets. In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small. PMID:22499682

Biological pathways, candidate genes and molecular markers associated with quality-of-life domains: an update

PubMed Central

Sprangers, Mirjam A.G.; Thong, Melissa S.Y.; Bartels, Meike; Barsevick, Andrea; Ordoñana, Juan; Shi, Qiuling; Wang, Xin Shelley; Klepstad, Pål; Wierenga, Eddy A.; Singh, Jasvinder A.; Sloan, Jeff A.

2014-01-01

Background There is compelling evidence of a genetic foundation of patient-reported QOL. Given the rapid development of substantial scientific advances in this area of research, the current paper updates and extends reviews published in 2010. Objectives The objective is to provide an updated overview of the biological pathways, candidate genes and molecular markers involved in fatigue, pain, negative (depressed mood) and positive (well-being/happiness) emotional functioning, social functioning, and overall QOL. Methods We followed a purposeful search algorithm of existing literature to capture empirical papers investigating the relationship between biological pathways and molecular markers and the identified QOL domains. Results Multiple major pathways are involved in each QOL domain. The inflammatory pathway has the strongest evidence as a controlling mechanism underlying fatigue. Inflammation and neurotransmission are key processes involved in pain perception and the COMT gene is associated with multiple sorts of pain. The neurotransmitter and neuroplasticity theories have the strongest evidence for their relationship with depression. Oxytocin-related genes and genes involved in the serotonergic and dopaminergic pathways play a role in social functioning. Inflammatory pathways, via cytokines, also play an important role in overall QOL. Conclusions Whereas the current findings need future experiments and replication efforts, they will provide researchers supportive background information when embarking on studies relating candidate genes and/or molecular markers to QOL domains. The ultimate goal of this area of research is to enhance patients’ QOL. PMID:24604075

Biological pathways, candidate genes, and molecular markers associated with quality-of-life domains: an update.

PubMed

Sprangers, Mirjam A G; Thong, Melissa S Y; Bartels, Meike; Barsevick, Andrea; Ordoñana, Juan; Shi, Qiuling; Wang, Xin Shelley; Klepstad, Pål; Wierenga, Eddy A; Singh, Jasvinder A; Sloan, Jeff A

2014-09-01

There is compelling evidence of a genetic foundation of patient-reported quality of life (QOL). Given the rapid development of substantial scientific advances in this area of research, the current paper updates and extends reviews published in 2010. The objective was to provide an updated overview of the biological pathways, candidate genes, and molecular markers involved in fatigue, pain, negative (depressed mood) and positive (well-being/happiness) emotional functioning, social functioning, and overall QOL. We followed a purposeful search algorithm of existing literature to capture empirical papers investigating the relationship between biological pathways and molecular markers and the identified QOL domains. Multiple major pathways are involved in each QOL domain. The inflammatory pathway has the strongest evidence as a controlling mechanism underlying fatigue. Inflammation and neurotransmission are key processes involved in pain perception, and the catechol-O-methyltransferase (COMT) gene is associated with multiple sorts of pain. The neurotransmitter and neuroplasticity theories have the strongest evidence for their relationship with depression. Oxytocin-related genes and genes involved in the serotonergic and dopaminergic pathways play a role in social functioning. Inflammatory pathways, via cytokines, also play an important role in overall QOL. Whereas the current findings need future experiments and replication efforts, they will provide researchers supportive background information when embarking on studies relating candidate genes and/or molecular markers to QOL domains. The ultimate goal of this area of research is to enhance patients' QOL.

Integrated pathway-based approach identifies association between genomic regions at CTCF and CACNB2 and schizophrenia.

PubMed

Juraeva, Dilafruz; Haenisch, Britta; Zapatka, Marc; Frank, Josef; Witt, Stephanie H; Mühleisen, Thomas W; Treutlein, Jens; Strohmaier, Jana; Meier, Sandra; Degenhardt, Franziska; Giegling, Ina; Ripke, Stephan; Leber, Markus; Lange, Christoph; Schulze, Thomas G; Mössner, Rainald; Nenadic, Igor; Sauer, Heinrich; Rujescu, Dan; Maier, Wolfgang; Børglum, Anders; Ophoff, Roel; Cichon, Sven; Nöthen, Markus M; Rietschel, Marcella; Mattheisen, Manuel; Brors, Benedikt

2014-06-01

In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to schizophrenia; (2) detect genes that may be potentially affected in these pathways since they contain an associated polymorphism; and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database. Fourteen of the gene-sets or pathways identified in the discovery dataset were confirmed in the replication dataset. These include functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis. For two genes, i.e. CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in both the discovery study and published data from the Psychiatric Genomics Consortium schizophrenia study. Furthermore, these genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest. Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2) detection of genes of interest for future follow-up studies; and (3) the highlighting of novel genes in previously reported candidate regions for schizophrenia.

Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson's disease.

PubMed

Zhang, Mingming; Mu, Hongbo; Shang, Zhenwei; Kang, Kai; Lv, Hongchao; Duan, Lian; Li, Jin; Chen, Xinren; Teng, Yanbo; Jiang, Yongshuai; Zhang, Ruijie

2017-01-06

Parkinson's disease (PD) is the second most common neurodegenerative disease. It is generally believed that it is influenced by both genetic and environmental factors, but the precise pathogenesis of PD is unknown to date. In this study, we performed a pathway analysis based on genome-wide association study (GWAS) to detect risk pathways of PD in three GWAS datasets. We first mapped all SNP markers to autosomal genes in each GWAS dataset. Then, we evaluated gene risk values using the minimum P-value of the tagSNPs. We took a pathway as a unit to identify the risk pathways based on the cumulative risks of the genes in the pathway. Finally, we combine the analysis results of the three datasets to detect the high risk pathways associated with PD. We found there were five same pathways in the three datasets. Besides, we also found there were five pathways which were shared in two datasets. Most of these pathways are associated with nervoussystem. Five pathways had been reported to be PD-related pathways in the previous literature. Our findings also implied that there was a close association between immune response and PD. Continued investigation of these pathways will further help us explain the pathogenesis of PD. Copyright © 2016. Published by Elsevier Ltd.

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.

Genome-wide association study and biological pathway analysis of the Eimeria maxima response in broilers.

PubMed

Hamzić, Edin; Buitenhuis, Bart; Hérault, Frédéric; Hawken, Rachel; Abrahamsen, Mitchel S; Servin, Bertrand; Elsen, Jean-Michel; Pinard-van der Laan, Marie-Hélène; Bed'Hom, Bertrand

2015-11-25

Coccidiosis is the most common and costly disease in the poultry industry and is caused by protozoans of the Eimeria genus. The current control of coccidiosis, based on the use of anticoccidial drugs and vaccination, faces serious obstacles such as drug resistance and the high costs for the development of efficient vaccines, respectively. Therefore, the current control programs must be expanded with complementary approaches such as the use of genetics to improve the host response to Eimeria infections. Recently, we have performed a large-scale challenge study on Cobb500 broilers using E. maxima for which we investigated variability among animals in response to the challenge. As a follow-up to this challenge study, we performed a genome-wide association study (GWAS) to identify genomic regions underlying variability of the measured traits in the response to Eimeria maxima in broilers. Furthermore, we conducted a post-GWAS functional analysis to increase our biological understanding of the underlying response to Eimeria maxima challenge. In total, we identified 22 single nucleotide polymorphisms (SNPs) with q value <0.1 distributed across five chromosomes. The highly significant SNPs were associated with body weight gain (three SNPs on GGA5, one SNP on GGA1 and one SNP on GGA3), plasma coloration measured as optical density at wavelengths in the range 465-510 nm (10 SNPs and all on GGA10) and the percentage of β2-globulin in blood plasma (15 SNPs on GGA1 and one SNP on GGA2). Biological pathways related to metabolic processes, cell proliferation, and primary innate immune processes were among the most frequent significantly enriched biological pathways. Furthermore, the network-based analysis produced two networks of high confidence, with one centered on large tumor suppressor kinase 1 (LATS1) and 2 (LATS2) and the second involving the myosin heavy chain 6 (MYH6). We identified several strong candidate genes and genomic regions associated with traits measured in

Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers.

PubMed

Kaluarachchi, Manuja R; Boulangé, Claire L; Garcia-Perez, Isabel; Lindon, John C; Minet, Emmanuel F

2016-10-01

Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events. A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC-MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed. Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment.

Detecting gene subnetworks under selection in biological pathways.

PubMed

Gouy, Alexandre; Daub, Joséphine T; Excoffier, Laurent

2017-09-19

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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 .

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

PubMed Central

Paley, Suzanne M.; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M.; Lee, Thomas J.; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M.; Caspi, Ron

2010-01-01

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry. PMID:19955237

The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways.

PubMed

Beltrame, Luca; Calura, Enrica; Popovici, Razvan R; Rizzetto, Lisa; Guedez, Damariz Rivero; Donato, Michele; Romualdi, Chiara; Draghici, Sorin; Cavalieri, Duccio

2011-08-01

Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and 'wet lab' scientists. The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X.

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

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

King, Zachary A.; Drager, Andreas; Ebrahim, Ali

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics).more » Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.« less

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

DOE PAGES

King, Zachary A.; Drager, Andreas; Ebrahim, Ali; ...

2015-08-27

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics).more » Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.« less

The Hippo signaling pathway in stem cell biology and cancer

PubMed Central

Mo, Jung-Soon; Park, Hyun Woo; Guan, Kun-Liang

2014-01-01

The Hippo signaling pathway, consisting of a highly conserved kinase cascade (MST and Lats) and downstream transcription coactivators (YAP and TAZ), plays a key role in tissue homeostasis and organ size control by regulating tissue-specific stem cells. Moreover, this pathway plays a prominent role in tissue repair and regeneration. Dysregulation of the Hippo pathway is associated with cancer development. Recent studies have revealed a complex network of upstream inputs, including cell density, mechanical sensation, and G-protein-coupled receptor (GPCR) signaling, that modulate Hippo pathway activity. This review focuses on the role of the Hippo pathway in stem cell biology and its potential implications in tissue homeostasis and cancer. PMID:24825474

Label-Free LC-MS/MS Proteomic Analysis of Cerebrospinal Fluid Identifies Protein/Pathway Alterations and Candidate Biomarkers for Amyotrophic Lateral Sclerosis.

PubMed

Collins, Mahlon A; An, Jiyan; Hood, Brian L; Conrads, Thomas P; Bowser, Robert P

2015-11-06

Analysis of the cerebrospinal fluid (CSF) proteome has proven valuable to the study of neurodegenerative disorders. To identify new protein/pathway alterations and candidate biomarkers for amyotrophic lateral sclerosis (ALS), we performed comparative proteomic profiling of CSF from sporadic ALS (sALS), healthy control (HC), and other neurological disease (OND) subjects using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 1712 CSF proteins were detected and relatively quantified by spectral counting. Levels of several proteins with diverse biological functions were significantly altered in sALS samples. Enrichment analysis was used to link these alterations to biological pathways, which were predominantly related to inflammation, neuronal activity, and extracellular matrix regulation. We then used our CSF proteomic profiles to create a support vector machines classifier capable of discriminating training set ALS from non-ALS (HC and OND) samples. Four classifier proteins, WD repeat-containing protein 63, amyloid-like protein 1, SPARC-like protein 1, and cell adhesion molecule 3, were identified by feature selection and externally validated. The resultant classifier distinguished ALS from non-ALS samples with 83% sensitivity and 100% specificity in an independent test set. Collectively, our results illustrate the utility of CSF proteomic profiling for identifying ALS protein/pathway alterations and candidate disease biomarkers.

Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.

PubMed

Pattaro, Cristian; Teumer, Alexander; Gorski, Mathias; Chu, Audrey Y; Li, Man; Mijatovic, Vladan; Garnaas, Maija; Tin, Adrienne; Sorice, Rossella; Li, Yong; Taliun, Daniel; Olden, Matthias; Foster, Meredith; Yang, Qiong; Chen, Ming-Huei; Pers, Tune H; Johnson, Andrew D; Ko, Yi-An; Fuchsberger, Christian; Tayo, Bamidele; Nalls, Michael; Feitosa, Mary F; Isaacs, Aaron; Dehghan, Abbas; d'Adamo, Pio; Adeyemo, Adebowale; Dieffenbach, Aida Karina; Zonderman, Alan B; Nolte, Ilja M; van der Most, Peter J; Wright, Alan F; Shuldiner, Alan R; Morrison, Alanna C; Hofman, Albert; Smith, Albert V; Dreisbach, Albert W; Franke, Andre; Uitterlinden, Andre G; Metspalu, Andres; Tonjes, Anke; Lupo, Antonio; Robino, Antonietta; Johansson, Åsa; Demirkan, Ayse; Kollerits, Barbara; Freedman, Barry I; Ponte, Belen; Oostra, Ben A; Paulweber, Bernhard; Krämer, Bernhard K; Mitchell, Braxton D; Buckley, Brendan M; Peralta, Carmen A; Hayward, Caroline; Helmer, Catherine; Rotimi, Charles N; Shaffer, Christian M; Müller, Christian; Sala, Cinzia; van Duijn, Cornelia M; Saint-Pierre, Aude; Ackermann, Daniel; Shriner, Daniel; Ruggiero, Daniela; Toniolo, Daniela; Lu, Yingchang; Cusi, Daniele; Czamara, Darina; Ellinghaus, David; Siscovick, David S; Ruderfer, Douglas; Gieger, Christian; Grallert, Harald; Rochtchina, Elena; Atkinson, Elizabeth J; Holliday, Elizabeth G; Boerwinkle, Eric; Salvi, Erika; Bottinger, Erwin P; Murgia, Federico; Rivadeneira, Fernando; Ernst, Florian; Kronenberg, Florian; Hu, Frank B; Navis, Gerjan J; Curhan, Gary C; Ehret, George B; Homuth, Georg; Coassin, Stefan; Thun, Gian-Andri; Pistis, Giorgio; Gambaro, Giovanni; Malerba, Giovanni; Montgomery, Grant W; Eiriksdottir, Gudny; Jacobs, Gunnar; Li, Guo; Wichmann, H-Erich; Campbell, Harry; Schmidt, Helena; Wallaschofski, Henri; Völzke, Henry; Brenner, Hermann; Kroemer, Heyo K; Kramer, Holly; Lin, Honghuang; Leach, I Mateo; Ford, Ian; Guessous, Idris; Rudan, Igor; Prokopenko, Inga; Borecki, Ingrid; Heid, Iris M; Kolcic, Ivana; Persico, Ivana; Jukema, J Wouter; Wilson, James F; Felix, Janine F; Divers, Jasmin; Lambert, Jean-Charles; Stafford, Jeanette M; Gaspoz, Jean-Michel; Smith, Jennifer A; Faul, Jessica D; Wang, Jie Jin; Ding, Jingzhong; Hirschhorn, Joel N; Attia, John; Whitfield, John B; Chalmers, John; Viikari, Jorma; Coresh, Josef; Denny, Joshua C; Karjalainen, Juha; Fernandes, Jyotika K; Endlich, Karlhans; Butterbach, Katja; Keene, Keith L; Lohman, Kurt; Portas, Laura; Launer, Lenore J; Lyytikäinen, Leo-Pekka; Yengo, Loic; Franke, Lude; Ferrucci, Luigi; Rose, Lynda M; Kedenko, Lyudmyla; Rao, Madhumathi; Struchalin, Maksim; Kleber, Marcus E; Cavalieri, Margherita; Haun, Margot; Cornelis, Marilyn C; Ciullo, Marina; Pirastu, Mario; de Andrade, Mariza; McEvoy, Mark A; Woodward, Mark; Adam, Martin; Cocca, Massimiliano; Nauck, Matthias; Imboden, Medea; Waldenberger, Melanie; Pruijm, Menno; Metzger, Marie; Stumvoll, Michael; Evans, Michele K; Sale, Michele M; Kähönen, Mika; Boban, Mladen; Bochud, Murielle; Rheinberger, Myriam; Verweij, Niek; Bouatia-Naji, Nabila; Martin, Nicholas G; Hastie, Nick; Probst-Hensch, Nicole; Soranzo, Nicole; Devuyst, Olivier; Raitakari, Olli; Gottesman, Omri; Franco, Oscar H; Polasek, Ozren; Gasparini, Paolo; Munroe, Patricia B; Ridker, Paul M; Mitchell, Paul; Muntner, Paul; Meisinger, Christa; Smit, Johannes H; Kovacs, Peter; Wild, Philipp S; Froguel, Philippe; Rettig, Rainer; Mägi, Reedik; Biffar, Reiner; Schmidt, Reinhold; Middelberg, Rita P S; Carroll, Robert J; Penninx, Brenda W; Scott, Rodney J; Katz, Ronit; Sedaghat, Sanaz; Wild, Sarah H; Kardia, Sharon L R; Ulivi, Sheila; Hwang, Shih-Jen; Enroth, Stefan; Kloiber, Stefan; Trompet, Stella; Stengel, Benedicte; Hancock, Stephen J; Turner, Stephen T; Rosas, Sylvia E; Stracke, Sylvia; Harris, Tamara B; Zeller, Tanja; Zemunik, Tatijana; Lehtimäki, Terho; Illig, Thomas; Aspelund, Thor; Nikopensius, Tiit; Esko, Tonu; Tanaka, Toshiko; Gyllensten, Ulf; Völker, Uwe; Emilsson, Valur; Vitart, Veronique; Aalto, Ville; Gudnason, Vilmundur; Chouraki, Vincent; Chen, Wei-Min; Igl, Wilmar; März, Winfried; Koenig, Wolfgang; Lieb, Wolfgang; Loos, Ruth J F; Liu, Yongmei; Snieder, Harold; Pramstaller, Peter P; Parsa, Afshin; O'Connell, Jeffrey R; Susztak, Katalin; Hamet, Pavel; Tremblay, Johanne; de Boer, Ian H; Böger, Carsten A; Goessling, Wolfram; Chasman, Daniel I; Köttgen, Anna; Kao, W H Linda; Fox, Caroline S

2016-01-21

Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways.

Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function

PubMed Central

Pattaro, Cristian; Teumer, Alexander; Gorski, Mathias; Chu, Audrey Y.; Li, Man; Mijatovic, Vladan; Garnaas, Maija; Tin, Adrienne; Sorice, Rossella; Li, Yong; Taliun, Daniel; Olden, Matthias; Foster, Meredith; Yang, Qiong; Chen, Ming-Huei; Pers, Tune H.; Johnson, Andrew D.; Ko, Yi-An; Fuchsberger, Christian; Tayo, Bamidele; Nalls, Michael; Feitosa, Mary F.; Isaacs, Aaron; Dehghan, Abbas; d'Adamo, Pio; Adeyemo, Adebowale; Dieffenbach, Aida Karina; Zonderman, Alan B.; Nolte, Ilja M.; van der Most, Peter J.; Wright, Alan F.; Shuldiner, Alan R.; Morrison, Alanna C.; Hofman, Albert; Smith, Albert V.; Dreisbach, Albert W.; Franke, Andre; Uitterlinden, Andre G.; Metspalu, Andres; Tonjes, Anke; Lupo, Antonio; Robino, Antonietta; Johansson, Åsa; Demirkan, Ayse; Kollerits, Barbara; Freedman, Barry I.; Ponte, Belen; Oostra, Ben A.; Paulweber, Bernhard; Krämer, Bernhard K.; Mitchell, Braxton D.; Buckley, Brendan M.; Peralta, Carmen A.; Hayward, Caroline; Helmer, Catherine; Rotimi, Charles N.; Shaffer, Christian M.; Müller, Christian; Sala, Cinzia; van Duijn, Cornelia M.; Saint-Pierre, Aude; Ackermann, Daniel; Shriner, Daniel; Ruggiero, Daniela; Toniolo, Daniela; Lu, Yingchang; Cusi, Daniele; Czamara, Darina; Ellinghaus, David; Siscovick, David S.; Ruderfer, Douglas; Gieger, Christian; Grallert, Harald; Rochtchina, Elena; Atkinson, Elizabeth J.; Holliday, Elizabeth G.; Boerwinkle, Eric; Salvi, Erika; Bottinger, Erwin P.; Murgia, Federico; Rivadeneira, Fernando; Ernst, Florian; Kronenberg, Florian; Hu, Frank B.; Navis, Gerjan J.; Curhan, Gary C.; Ehret, George B.; Homuth, Georg; Coassin, Stefan; Thun, Gian-Andri; Pistis, Giorgio; Gambaro, Giovanni; Malerba, Giovanni; Montgomery, Grant W.; Eiriksdottir, Gudny; Jacobs, Gunnar; Li, Guo; Wichmann, H-Erich; Campbell, Harry; Schmidt, Helena; Wallaschofski, Henri; Völzke, Henry; Brenner, Hermann; Kroemer, Heyo K.; Kramer, Holly; Lin, Honghuang; Leach, I. Mateo; Ford, Ian; Guessous, Idris; Rudan, Igor; Prokopenko, Inga; Borecki, Ingrid; Heid, Iris M.; Kolcic, Ivana; Persico, Ivana; Jukema, J. Wouter; Wilson, James F.; Felix, Janine F.; Divers, Jasmin; Lambert, Jean-Charles; Stafford, Jeanette M.; Gaspoz, Jean-Michel; Smith, Jennifer A.; Faul, Jessica D.; Wang, Jie Jin; Ding, Jingzhong; Hirschhorn, Joel N.; Attia, John; Whitfield, John B.; Chalmers, John; Viikari, Jorma; Coresh, Josef; Denny, Joshua C.; Karjalainen, Juha; Fernandes, Jyotika K.; Endlich, Karlhans; Butterbach, Katja; Keene, Keith L.; Lohman, Kurt; Portas, Laura; Launer, Lenore J.; Lyytikäinen, Leo-Pekka; Yengo, Loic; Franke, Lude; Ferrucci, Luigi; Rose, Lynda M.; Kedenko, Lyudmyla; Rao, Madhumathi; Struchalin, Maksim; Kleber, Marcus E.; Cavalieri, Margherita; Haun, Margot; Cornelis, Marilyn C.; Ciullo, Marina; Pirastu, Mario; de Andrade, Mariza; McEvoy, Mark A.; Woodward, Mark; Adam, Martin; Cocca, Massimiliano; Nauck, Matthias; Imboden, Medea; Waldenberger, Melanie; Pruijm, Menno; Metzger, Marie; Stumvoll, Michael; Evans, Michele K.; Sale, Michele M.; Kähönen, Mika; Boban, Mladen; Bochud, Murielle; Rheinberger, Myriam; Verweij, Niek; Bouatia-Naji, Nabila; Martin, Nicholas G.; Hastie, Nick; Probst-Hensch, Nicole; Soranzo, Nicole; Devuyst, Olivier; Raitakari, Olli; Gottesman, Omri; Franco, Oscar H.; Polasek, Ozren; Gasparini, Paolo; Munroe, Patricia B.; Ridker, Paul M.; Mitchell, Paul; Muntner, Paul; Meisinger, Christa; Smit, Johannes H.; Abecasis, Goncalo R.; Adair, Linda S.; Alexander, Myriam; Altshuler, David; Amin, Najaf; Arking, Dan E.; Arora, Pankaj; Aulchenko, Yurii; Bakker, Stephan J. L.; Bandinelli, Stefania; Barroso, Ines; Beckmann, Jacques S.; Beilby, John P.; Bergman, Richard N.; Bergmann, Sven; Bis, Joshua C.; Boehnke, Michael; Bonnycastle, Lori L.; Bornstein, Stefan R.; Bots, Michiel L.; Bragg-Gresham, Jennifer L.; Brand, Stefan-Martin; Brand, Eva; Braund, Peter S.; Brown, Morris J.; Burton, Paul R.; Casas, Juan P.; Caulfield, Mark J.; Chakravarti, Aravinda; Chambers, John C.; Chandak, Giriraj R.; Chang, Yen-Pei C.; Charchar, Fadi J.; Chaturvedi, Nish; Shin Cho, Yoon; Clarke, Robert; Collins, Francis S.; Collins, Rory; Connell, John M.; Cooper, Jackie A.; Cooper, Matthew N.; Cooper, Richard S.; Corsi, Anna Maria; Dörr, Marcus; Dahgam, Santosh; Danesh, John; Smith, George Davey; Day, Ian N. M.; Deloukas, Panos; Denniff, Matthew; Dominiczak, Anna F.; Dong, Yanbin; Doumatey, Ayo; Elliott, Paul; Elosua, Roberto; Erdmann, Jeanette; Eyheramendy, Susana; Farrall, Martin; Fava, Cristiano; Forrester, Terrence; Fowkes, F. Gerald R.; Fox, Ervin R.; Frayling, Timothy M.; Galan, Pilar; Ganesh, Santhi K.; Garcia, Melissa; Gaunt, Tom R.; Glazer, Nicole L.; Go, Min Jin; Goel, Anuj; Grässler, Jürgen; Grobbee, Diederick E.; Groop, Leif; Guarrera, Simonetta; Guo, Xiuqing; Hadley, David; Hamsten, Anders; Han, Bok-Ghee; Hardy, Rebecca; Hartikainen, Anna-Liisa; Heath, Simon; Heckbert, Susan R.; Hedblad, Bo; Hercberg, Serge; Hernandez, Dena; Hicks, Andrew A.; Hilton, Gina; Hingorani, Aroon D.; Bolton, Judith A Hoffman; Hopewell, Jemma C.; Howard, Philip; Humphries, Steve E.; Hunt, Steven C.; Hveem, Kristian; Ikram, M. Arfan; Islam, Muhammad; Iwai, Naoharu; Jarvelin, Marjo-Riitta; Jackson, Anne U.; Jafar, Tazeen H.; Janipalli, Charles S.; Johnson, Toby; Kathiresan, Sekar; Khaw, Kay-Tee; Kim, Hyung-Lae; Kinra, Sanjay; Kita, Yoshikuni; Kivimaki, Mika; Kooner, Jaspal S.; Kumar, M. J. Kranthi; Kuh, Diana; Kulkarni, Smita R.; Kumari, Meena; Kuusisto, Johanna; Kuznetsova, Tatiana; Laakso, Markku; Laan, Maris; Laitinen, Jaana; Lakatta, Edward G.; Langefeld, Carl D.; Larson, Martin G.; Lathrop, Mark; Lawlor, Debbie A.; Lawrence, Robert W.; Lee, Jong-Young; Lee, Nanette R.; Levy, Daniel; Li, Yali; Longstreth, Will T.; Luan, Jian'an; Lucas, Gavin; Ludwig, Barbara; Mangino, Massimo; Mani, K. Radha; Marmot, Michael G.; Mattace-Raso, Francesco U. S.; Matullo, Giuseppe; McArdle, Wendy L.; McKenzie, Colin A.; Meitinger, Thomas; Melander, Olle; Meneton, Pierre; Meschia, James F.; Miki, Tetsuro; Milaneschi, Yuri; Mohlke, Karen L.; Mooser, Vincent; Morken, Mario A.; Morris, Richard W.; Mosley, Thomas H.; Najjar, Samer; Narisu, Narisu; Newton-Cheh, Christopher; Nguyen, Khanh-Dung Hoang; Nilsson, Peter; Nyberg, Fredrik; O'Donnell, Christopher J.; Ogihara, Toshio; Ohkubo, Takayoshi; Okamura, Tomonori; Ong, RickTwee-Hee; Ongen, Halit; Onland-Moret, N. Charlotte; O'Reilly, Paul F.; Org, Elin; Orru, Marco; Palmas, Walter; Palmen, Jutta; Palmer, Lyle J.; Palmer, Nicholette D.; Parker, Alex N.; Peden, John F.; Peltonen, Leena; Perola, Markus; Pihur, Vasyl; Platou, Carl G. P.; Plump, Andrew; Prabhakaran, Dorairajan; Psaty, Bruce M.; Raffel, Leslie J.; Rao, Dabeeru C.; Rasheed, Asif; Ricceri, Fulvio; Rice, Kenneth M.; Rosengren, Annika; Rotter, Jerome I.; Rudock, Megan E.; Sõber, Siim; Salako, Tunde; Saleheen, Danish; Salomaa, Veikko; Samani, Nilesh J.; Schwartz, Steven M.; Schwarz, Peter E. H.; Scott, Laura J.; Scott, James; Scuteri, Angelo; Sehmi, Joban S.; Seielstad, Mark; Seshadri, Sudha; Sharma, Pankaj; Shaw-Hawkins, Sue; Shi, Gang; Shrine, Nick R. G.; Sijbrands, Eric J. G.; Sim, Xueling; Singleton, Andrew; Sjögren, Marketa; Smith, Nicholas L.; Artigas, Maria Soler; Spector, Tim D.; Staessen, Jan A.; Stancakova, Alena; Steinle, Nanette I.; Strachan, David P.; Stringham, Heather M.; Sun, Yan V.; Swift, Amy J.; Tabara, Yasuharu; Tai, E-Shyong; Talmud, Philippa J.; Taylor, Andrew; Terzic, Janos; Thelle, Dag S.; Tobin, Martin D.; Tomaszewski, Maciej; Tripathy, Vikal; Tuomilehto, Jaakko; Tzoulaki, Ioanna; Uda, Manuela; Ueshima, Hirotsugu; Uiterwaal, Cuno S. P. M.; Umemura, Satoshi; van der Harst, Pim; van der Schouw, Yvonne T.; van Gilst, Wiek H.; Vartiainen, Erkki; Vasan, Ramachandran S.; Veldre, Gudrun; Verwoert, Germaine C.; Viigimaa, Margus; Vinay, D. G.; Vineis, Paolo; Voight, Benjamin F.; Vollenweider, Peter; Wagenknecht, Lynne E.; Wain, Louise V.; Wang, Xiaoling; Wang, Thomas J.; Wareham, Nicholas J.; Watkins, Hugh; Weder, Alan B.; Whincup, Peter H.; Wiggins, Kerri L.; Witteman, Jacqueline C. M.; Wong, Andrew; Wu, Ying; Yajnik, Chittaranjan S.; Yao, Jie; Young, J. H.; Zelenika, Diana; Zhai, Guangju; Zhang, Weihua; Zhang, Feng; Zhao, Jing Hua; Zhu, Haidong; Zhu, Xiaofeng; Zitting, Paavo; Zukowska-Szczechowska, Ewa; Okada, Yukinori; Wu, Jer-Yuarn; Gu, Dongfeng; Takeuchi, Fumihiko; Takahashi, Atsushi; Maeda, Shiro; Tsunoda, Tatsuhiko; Chen, Peng; Lim, Su-Chi; Wong, Tien-Yin; Liu, Jianjun; Young, Terri L.; Aung, Tin; Teo, Yik-Ying; Kim, Young Jin; Kang, Daehee; Chen, Chien-Hsiun; Tsai, Fuu-Jen; Chang, Li-Ching; Fann, S. -J. Cathy; Mei, Hao; Hixson, James E.; Chen, Shufeng; Katsuya, Tomohiro; Isono, Masato; Albrecht, Eva; Yamamoto, Kazuhiko; Kubo, Michiaki; Nakamura, Yusuke; Kamatani, Naoyuki; Kato, Norihiro; He, Jiang; Chen, Yuan-Tsong; Tanaka, Toshihiro; Reilly, Muredach P; Schunkert, Heribert; Assimes, Themistocles L.; Hall, Alistair; Hengstenberg, Christian; König, Inke R.; Laaksonen, Reijo; McPherson, Ruth; Thompson, John R.; Thorsteinsdottir, Unnur; Ziegler, Andreas; Absher, Devin; Chen, Li; Cupples13, L. Adrienne; Halperin, Eran; Li, Mingyao; Musunuru, Kiran; Preuss, Michael; Schillert, Arne; Thorleifsson, Gudmar; Wells, George A.; Holm, Hilma; Roberts, Robert; Stewart, Alexandre F. R.; Fortmann, Stephen; Go, Alan; Hlatky, Mark; Iribarren, Carlos; Knowles, Joshua; Myers, Richard; Quertermous, Thomas; Sidney, Steven; Risch, Neil; Tang, Hua; Blankenberg, Stefan; Schnabel, Renate; Sinning, Christoph; Lackner, Karl J.; Tiret, Laurence; Nicaud, Viviane; Cambien, Francois; Bickel, Christoph; Rupprecht, Hans J.; Perret, Claire; Proust, Carole; Münzel, Thomas F.; Barbalic, Maja; Chen, Ida Yii-Der; Demissie-Banjaw, Serkalem; Folsom, Aaron; Lumley, Thomas; Marciante, Kristin; Taylor, Kent D.; Volcik, Kelly; Gretarsdottir, Solveig; Gulcher, Jeffrey R.; Kong, Augustine; Stefansson, Kari; Thorgeirsson, Gudmundur; Andersen, Karl; Fischer, Marcus; Grosshennig, Anika; Linsel-Nitschke, Patrick; Stark, Klaus; Schreiber, Stefan; Aherrahrou, Zouhair; Bruse, Petra; Doering, Angela; Klopp, Norman; Diemert, Patrick; Loley, Christina; Medack, Anja; Nahrstedt, Janja; Peters, Annette; Wagner, Arnika K.; Willenborg, Christina; Böhm, Bernhard O.; Dobnig, Harald; Grammer, Tanja B.; Hoffmann, Michael M.; Meinitzer, Andreas; Winkelmann, Bernhard R.; Pilz, Stefan; Renner, Wilfried; Scharnagl, Hubert; Stojakovic, Tatjana; Tomaschitz, Andreas; Winkler, Karl; Guiducci, Candace; Burtt, Noel; Gabriel, Stacey B.; Dandona, Sonny; Jarinova, Olga; Qu, Liming; Wilensky, Robert; Matthai, William; Hakonarson, Hakon H.; Devaney, Joe; Burnett, Mary Susan; Pichard, Augusto D.; Kent, Kenneth M.; Satler, Lowell; Lindsay, Joseph M.; Waksman, Ron; Knouff, Christopher W.; Waterworth, Dawn M.; Walker, Max C.; Epstein, Stephen E.; Rader, Daniel J.; Nelson, Christopher P.; Wright, Benjamin J.; Balmforth, Anthony J.; Ball, Stephen G.; Loehr, Laura R.; Rosamond, Wayne D.; Benjamin, Emelia; Haritunians, Talin; Couper, David; Murabito, Joanne; Wang, Ying A.; Stricker, Bruno H.; Chang, Patricia P.; Willerson, James T.; Felix, Stephan B.; Watzinger, Norbert; Aragam, Jayashri; Zweiker, Robert; Lind, Lars; Rodeheffer, Richard J.; Greiser, Karin Halina; Deckers, Jaap W.; Stritzke, Jan; Ingelsson, Erik; Kullo, Iftikhar; Haerting, Johannes; Reffelmann, Thorsten; Redfield, Margaret M.; Werdan, Karl; Mitchell, Gary F.; Arnett, Donna K.; Gottdiener, John S.; Blettner, Maria; Friedrich, Nele; Kovacs, Peter; Wild, Philipp S.; Froguel, Philippe; Rettig, Rainer; Mägi, Reedik; Biffar, Reiner; Schmidt, Reinhold; Middelberg, Rita P. S.; Carroll, Robert J.; Penninx, Brenda W.; Scott, Rodney J.; Katz, Ronit; Sedaghat, Sanaz; Wild, Sarah H.; Kardia, Sharon L. R.; Ulivi, Sheila; Hwang, Shih-Jen; Enroth, Stefan; Kloiber, Stefan; Trompet, Stella; Stengel, Benedicte; Hancock, Stephen J.; Turner, Stephen T.; Rosas, Sylvia E.; Stracke, Sylvia; Harris, Tamara B.; Zeller, Tanja; Zemunik, Tatijana; Lehtimäki, Terho; Illig, Thomas; Aspelund, Thor; Nikopensius, Tiit; Esko, Tonu; Tanaka, Toshiko; Gyllensten, Ulf; Völker, Uwe; Emilsson, Valur; Vitart, Veronique; Aalto, Ville; Gudnason, Vilmundur; Chouraki, Vincent; Chen, Wei-Min; Igl, Wilmar; März, Winfried; Koenig, Wolfgang; Lieb, Wolfgang; Loos, Ruth J. F.; Liu, Yongmei; Snieder, Harold; Pramstaller, Peter P.; Parsa, Afshin; O'Connell, Jeffrey R.; Susztak, Katalin; Hamet, Pavel; Tremblay, Johanne; de Boer, Ian H.; Böger, Carsten A.; Goessling, Wolfram; Chasman, Daniel I.; Köttgen, Anna; Kao, W. H. Linda; Fox, Caroline S.

2016-01-01

Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways. PMID:26831199

Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

PubMed

Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J; Nauta, Arjen; Geurts, Jan M W

2014-01-01

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

Ventral aspect of the visual form pathway is not critical for the perception of biological motion

PubMed Central

Gilaie-Dotan, Sharon; Saygin, Ayse Pinar; Lorenzi, Lauren J.; Rees, Geraint; Behrmann, Marlene

2015-01-01

Identifying the movements of those around us is fundamental for many daily activities, such as recognizing actions, detecting predators, and interacting with others socially. A key question concerns the neurobiological substrates underlying biological motion perception. Although the ventral “form” visual cortex is standardly activated by biologically moving stimuli, whether these activations are functionally critical for biological motion perception or are epiphenomenal remains unknown. To address this question, we examined whether focal damage to regions of the ventral visual cortex, resulting in significant deficits in form perception, adversely affects biological motion perception. Six patients with damage to the ventral cortex were tested with sensitive point-light display paradigms. All patients were able to recognize unmasked point-light displays and their perceptual thresholds were not significantly different from those of three different control groups, one of which comprised brain-damaged patients with spared ventral cortex (n > 50). Importantly, these six patients performed significantly better than patients with damage to regions critical for biological motion perception. To assess the necessary contribution of different regions in the ventral pathway to biological motion perception, we complement the behavioral findings with a fine-grained comparison between the lesion location and extent, and the cortical regions standardly implicated in biological motion processing. This analysis revealed that the ventral aspects of the form pathway (e.g., fusiform regions, ventral extrastriate body area) are not critical for biological motion perception. We hypothesize that the role of these ventral regions is to provide enhanced multiview/posture representations of the moving person rather than to represent biological motion perception per se. PMID:25583504

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).

A Web Tool for Generating High Quality Machine-readable Biological Pathways.

PubMed

Ramirez-Gaona, Miguel; Marcu, Ana; Pon, Allison; Grant, Jason; Wu, Anthony; Wishart, David S

2017-02-08

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological information. The pathways generated by this online application are machine-readable and fully compatible with essentially all web-browsers and computer operating systems. It uses a specially developed, web-enabled pathway drawing interface that permits the selection and placement of different combinations of pre-drawn biological or biochemical entities to depict reactions, interactions, transport processes and binding events. This palette of entities consists of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs. All of the visual elements in it can be interactively adjusted and customized. Furthermore, because this tool is a web server, all pathways and pathway elements are publicly accessible. This kind of pathway "crowd sourcing" means that PathWhiz already contains a large and rapidly growing collection of previously drawn pathways and pathway elements. Here we describe a protocol for the quick and easy creation of new pathways and the alteration of existing pathways. To further facilitate pathway editing and creation, the tool contains replication and propagation functions. The replication function allows existing pathways to be used as templates to create or edit new pathways. The propagation function allows one to take an existing pathway and automatically propagate it across different species. Pathways created with this tool can be "re-styled" into different formats (KEGG-like or text-book like), colored with different backgrounds, exported to BioPAX, SBGN-ML, SBML, or PWML data exchange formats, and downloaded as PNG or SVG images. The pathways can easily be incorporated into online databases, integrated into presentations, posters or publications, or used exclusively for online visualization and exploration. This protocol has been successfully applied to

Transcriptome profiling identified differentially expressed genes and pathways associated with tamoxifen resistance in human breast cancer

PubMed Central

Men, Xin; Ma, Jun; Wu, Tong; Pu, Junyi; Wen, Shaojia; Shen, Jianfeng; Wang, Xun; Wang, Yamin; Chen, Chao; Dai, Penggao

2018-01-01

Tamoxifen (TAM) resistance is an important clinical problem in the treatment of breast cancer. In order to identify the mechanism of TAM resistance for estrogen receptor (ER)-positive breast cancer, we screened the transcriptome using RNA-seq and compared the gene expression profiles between the MCF-7 mamma carcinoma cell line and the TAM-resistant cell line TAMR/MCF-7, 52 significant differential expression genes (DEGs) were identified including SLIT2, ROBO, LHX, KLF, VEGFC, BAMBI, LAMA1, FLT4, PNMT, DHRS2, MAOA and ALDH. The DEGs were annotated in the GO, COG and KEGG databases. Annotation of the function of the DEGs in the KEGG database revealed the top three pathways enriched with the most DEGs, including pathways in cancer, the PI3K-AKT pathway, and focal adhesion. Then we compared the gene expression profiles between the Clinical progressive disease (PD) and the complete response (CR) from the cancer genome altas (TCGA). 10 common DEGs were identified through combining the clinical and cellular analysis results. Protein-protein interaction network was applied to analyze the association of ER signal pathway with the 10 DEGs. 3 significant genes (GFRA3, NPY1R and PTPRN2) were closely related to ER related pathway. These significant DEGs regulated many biological activities such as cell proliferation and survival, motility and migration, and tumor cell invasion. The interactions between these DEGs and drug resistance phenomenon need to be further elucidated at a functional level in further studies. Based on our findings, we believed that these DEGs could be therapeutic targets, which can be explored to develop new treatment options. PMID:29423105

A systems biology pipeline identifies new immune and disease related molecular signatures and networks in human cells during microgravity exposure

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sayak; Saha, Rohini; Palanisamy, Anbarasi; Ghosh, Madhurima; Biswas, Anupriya; Roy, Saheli; Pal, Arijit; Sarkar, Kathakali; Bagh, Sangram

2016-05-01

Microgravity is a prominent health hazard for astronauts, yet we understand little about its effect at the molecular systems level. In this study, we have integrated a set of systems-biology tools and databases and have analysed more than 8000 molecular pathways on published global gene expression datasets of human cells in microgravity. Hundreds of new pathways have been identified with statistical confidence for each dataset and despite the difference in cell types and experiments, around 100 of the new pathways are appeared common across the datasets. They are related to reduced inflammation, autoimmunity, diabetes and asthma. We have identified downregulation of NfκB pathway via Notch1 signalling as new pathway for reduced immunity in microgravity. Induction of few cancer types including liver cancer and leukaemia and increased drug response to cancer in microgravity are also found. Increase in olfactory signal transduction is also identified. Genes, based on their expression pattern, are clustered and mathematically stable clusters are identified. The network mapping of genes within a cluster indicates the plausible functional connections in microgravity. This pipeline gives a new systems level picture of human cells under microgravity, generates testable hypothesis and may help estimating risk and developing medicine for space missions.

A systems biology pipeline identifies new immune and disease related molecular signatures and networks in human cells during microgravity exposure.

PubMed

Mukhopadhyay, Sayak; Saha, Rohini; Palanisamy, Anbarasi; Ghosh, Madhurima; Biswas, Anupriya; Roy, Saheli; Pal, Arijit; Sarkar, Kathakali; Bagh, Sangram

2016-05-17

Microgravity is a prominent health hazard for astronauts, yet we understand little about its effect at the molecular systems level. In this study, we have integrated a set of systems-biology tools and databases and have analysed more than 8000 molecular pathways on published global gene expression datasets of human cells in microgravity. Hundreds of new pathways have been identified with statistical confidence for each dataset and despite the difference in cell types and experiments, around 100 of the new pathways are appeared common across the datasets. They are related to reduced inflammation, autoimmunity, diabetes and asthma. We have identified downregulation of NfκB pathway via Notch1 signalling as new pathway for reduced immunity in microgravity. Induction of few cancer types including liver cancer and leukaemia and increased drug response to cancer in microgravity are also found. Increase in olfactory signal transduction is also identified. Genes, based on their expression pattern, are clustered and mathematically stable clusters are identified. The network mapping of genes within a cluster indicates the plausible functional connections in microgravity. This pipeline gives a new systems level picture of human cells under microgravity, generates testable hypothesis and may help estimating risk and developing medicine for space missions.

The Pathway Coexpression Network: Revealing pathway relationships

PubMed Central

Tanzi, Rudolph E.

2018-01-01

A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer’s Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of

Biological pathways and genetic mechanisms involved in social functioning.

PubMed

Ordoñana, Juan R; Bartels, Meike; Boomsma, Dorret I; Cella, David; Mosing, Miriam; Oliveira, Joao R; Patrick, Donald L; Veenhoven, Ruut; Wagner, Gert G; Sprangers, Mirjam A G

2013-08-01

To describe the major findings in the literature regarding associations between biological and genetic factors and social functioning, paying special attention to: (1) heritability studies on social functioning and related concepts; (2) hypothesized biological pathways and genetic variants that could be involved in social functioning, and (3) the implications of these results for quality-of-life research. A search of Web of Science and PubMed databases was conducted using combinations of the following keywords: genetics, twins, heritability, social functioning, social adjustment, social interaction, and social dysfunction. Variability in the definitions and measures of social functioning was extensive. Moderate to high heritability was reported for social functioning and related concepts, including prosocial behavior, loneliness, and extraversion. Disorders characterized by impairments in social functioning also show substantial heritability. Genetic variants hypothesized to be involved in social functioning are related to the network of brain structures and processes that are known to affect social cognition and behavior. Better knowledge and understanding about the impact of genetic factors on social functioning is needed to help us to attain a more comprehensive view of health-related quality-of-life (HRQOL) and will ultimately enhance our ability to identify those patients who are vulnerable to poor social functioning.

Immediate Early Genes Anchor a Biological Pathway of Proteins Required for Memory Formation, Long-Term Depression and Risk for Schizophrenia

PubMed Central

Marballi, Ketan K.; Gallitano, Amelia L.

2018-01-01

While the causes of myriad medical and infectious illnesses have been identified, the etiologies of neuropsychiatric illnesses remain elusive. This is due to two major obstacles. First, the risk for neuropsychiatric disorders, such as schizophrenia, is determined by both genetic and environmental factors. Second, numerous genes influence susceptibility for these illnesses. Genome-wide association studies have identified at least 108 genomic loci for schizophrenia, and more are expected to be published shortly. In addition, numerous biological processes contribute to the neuropathology underlying schizophrenia. These include immune dysfunction, synaptic and myelination deficits, vascular abnormalities, growth factor disruption, and N-methyl-D-aspartate receptor (NMDAR) hypofunction. However, the field of psychiatric genetics lacks a unifying model to explain how environment may interact with numerous genes to influence these various biological processes and cause schizophrenia. Here we describe a biological cascade of proteins that are activated in response to environmental stimuli such as stress, a schizophrenia risk factor. The central proteins in this pathway are critical mediators of memory formation and a particular form of hippocampal synaptic plasticity, long-term depression (LTD). Each of these proteins is also implicated in schizophrenia risk. In fact, the pathway includes four genes that map to the 108 loci associated with schizophrenia: GRIN2A, nuclear factor of activated T-cells (NFATc3), early growth response 1 (EGR1) and NGFI-A Binding Protein 2 (NAB2); each of which contains the “Index single nucleotide polymorphism (SNP)” (most SNP) at its respective locus. Environmental stimuli activate this biological pathway in neurons, resulting in induction of EGR immediate early genes: EGR1, EGR3 and NAB2. We hypothesize that dysfunction in any of the genes in this pathway disrupts the normal activation of Egrs in response to stress. This may result in

Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts

PubMed Central

Silver, Matt; Chen, Peng; Li, Ruoying; Cheng, Ching-Yu; Wong, Tien-Yin; Tai, E-Shyong; Teo, Yik-Ying; Montana, Giovanni

2013-01-01

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune

Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts.

PubMed

Silver, Matt; Chen, Peng; Li, Ruoying; Cheng, Ching-Yu; Wong, Tien-Yin; Tai, E-Shyong; Teo, Yik-Ying; Montana, Giovanni

2013-11-01

Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune

A systems biology strategy to identify molecular mechanisms of action and protein indicators of traumatic brain injury.

PubMed

Yu, Chenggang; Boutté, Angela; Yu, Xueping; Dutta, Bhaskar; Feala, Jacob D; Schmid, Kara; Dave, Jitendra; Tawa, Gregory J; Wallqvist, Anders; Reifman, Jaques

2015-02-01

The multifactorial nature of traumatic brain injury (TBI), especially the complex secondary tissue injury involving intertwined networks of molecular pathways that mediate cellular behavior, has confounded attempts to elucidate the pathology underlying the progression of TBI. Here, systems biology strategies are exploited to identify novel molecular mechanisms and protein indicators of brain injury. To this end, we performed a meta-analysis of four distinct high-throughput gene expression studies involving different animal models of TBI. By using canonical pathways and a large human protein-interaction network as a scaffold, we separately overlaid the gene expression data from each study to identify molecular signatures that were conserved across the different studies. At 24 hr after injury, the significantly activated molecular signatures were nonspecific to TBI, whereas the significantly suppressed molecular signatures were specific to the nervous system. In particular, we identified a suppressed subnetwork consisting of 58 highly interacting, coregulated proteins associated with synaptic function. We selected three proteins from this subnetwork, postsynaptic density protein 95, nitric oxide synthase 1, and disrupted in schizophrenia 1, and hypothesized that their abundance would be significantly reduced after TBI. In a penetrating ballistic-like brain injury rat model of severe TBI, Western blot analysis confirmed our hypothesis. In addition, our analysis recovered 12 previously identified protein biomarkers of TBI. The results suggest that systems biology may provide an efficient, high-yield approach to generate testable hypotheses that can be experimentally validated to identify novel mechanisms of action and molecular indicators of TBI. © 2014 The Authors. Journal of Neuroscience Research Published by Wiley Periodicals, Inc.

The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways

PubMed Central

Rizzetto, Lisa; Guedez, Damariz Rivero; Donato, Michele; Romualdi, Chiara; Draghici, Sorin; Cavalieri, Duccio

2011-01-01

Motivation: Many models and analysis of signaling pathways have been proposed. However, neither of them takes into account that a biological pathway is not a fixed system, but instead it depends on the organism, tissue and cell type as well as on physiological, pathological and experimental conditions. Results: The Biological Connection Markup Language (BCML) is a format to describe, annotate and visualize pathways. BCML is able to store multiple information, permitting a selective view of the pathway as it exists and/or behave in specific organisms, tissues and cells. Furthermore, BCML can be automatically converted into data formats suitable for analysis and into a fully SBGN-compliant graphical representation, making it an important tool that can be used by both computational biologists and ‘wet lab’ scientists. Availability and implementation: The XML schema and the BCML software suite are freely available under the LGPL for download at http://bcml.dc-atlas.net. They are implemented in Java and supported on MS Windows, Linux and OS X. Contact: duccio.cavalieri@unifi.it; sorin@wayne.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21653523

Mapping the patent landscape of synthetic biology for fine chemical production pathways.

PubMed

Carbonell, Pablo; Gök, Abdullah; Shapira, Philip; Faulon, Jean-Loup

2016-09-01

A goal of synthetic biology bio-foundries is to innovate through an iterative design/build/test/learn pipeline. In assessing the value of new chemical production routes, the intellectual property (IP) novelty of the pathway is important. Exploratory studies can be carried using knowledge of the patent/IP landscape for synthetic biology and metabolic engineering. In this paper, we perform an assessment of pathways as potential targets for chemical production across the full catalogue of reachable chemicals in the extended metabolic space of chassis organisms, as computed by the retrosynthesis-based algorithm RetroPath. Our database for reactions processed by sequences in heterologous pathways was screened against the PatSeq database, a comprehensive collection of more than 150M sequences present in patent grants and applications. We also examine related patent families using Derwent Innovations. This large-scale computational study provides useful insights into the IP landscape of synthetic biology for fine and specialty chemicals production. © 2016 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Hundreds of variants clustered in genomic loci and biological pathways affect human height

PubMed Central

Lango Allen, Hana; Estrada, Karol; Lettre, Guillaume; Berndt, Sonja I.; Weedon, Michael N.; Rivadeneira, Fernando; Willer, Cristen J.; Jackson, Anne U.; Vedantam, Sailaja; Raychaudhuri, Soumya; Ferreira, Teresa; Wood, Andrew R.; Weyant, Robert J.; Segrè, Ayellet V.; Speliotes, Elizabeth K.; Wheeler, Eleanor; Soranzo, Nicole; Park, Ju-Hyun; Yang, Jian; Gudbjartsson, Daniel; Heard-Costa, Nancy L.; Randall, Joshua C.; Qi, Lu; Smith, Albert Vernon; Mägi, Reedik; Pastinen, Tomi; Liang, Liming; Heid, Iris M.; Luan, Jian'an; Thorleifsson, Gudmar; Winkler, Thomas W.; Goddard, Michael E.; Lo, Ken Sin; Palmer, Cameron; Workalemahu, Tsegaselassie; Aulchenko, Yurii S.; Johansson, Åsa; Zillikens, M.Carola; Feitosa, Mary F.; Esko, Tõnu; Johnson, Toby; Ketkar, Shamika; Kraft, Peter; Mangino, Massimo; Prokopenko, Inga; Absher, Devin; Albrecht, Eva; Ernst, Florian; Glazer, Nicole L.; Hayward, Caroline; Hottenga, Jouke-Jan; Jacobs, Kevin B.; Knowles, Joshua W.; Kutalik, Zoltán; Monda, Keri L.; Polasek, Ozren; Preuss, Michael; Rayner, Nigel W.; Robertson, Neil R.; Steinthorsdottir, Valgerdur; Tyrer, Jonathan P.; Voight, Benjamin F.; Wiklund, Fredrik; Xu, Jianfeng; Zhao, Jing Hua; Nyholt, Dale R.; Pellikka, Niina; Perola, Markus; Perry, John R.B.; Surakka, Ida; Tammesoo, Mari-Liis; Altmaier, Elizabeth L.; Amin, Najaf; Aspelund, Thor; Bhangale, Tushar; Boucher, Gabrielle; Chasman, Daniel I.; Chen, Constance; Coin, Lachlan; Cooper, Matthew N.; Dixon, Anna L.; Gibson, Quince; Grundberg, Elin; Hao, Ke; Junttila, M. Juhani; Kaplan, Lee M.; Kettunen, Johannes; König, Inke R.; Kwan, Tony; Lawrence, Robert W.; Levinson, Douglas F.; Lorentzon, Mattias; McKnight, Barbara; Morris, Andrew P.; Müller, Martina; Ngwa, Julius Suh; Purcell, Shaun; Rafelt, Suzanne; Salem, Rany M.; Salvi, Erika; Sanna, Serena; Shi, Jianxin; Sovio, Ulla; Thompson, John R.; Turchin, Michael C.; Vandenput, Liesbeth; Verlaan, Dominique J.; Vitart, Veronique; White, Charles C.; Ziegler, Andreas; Almgren, Peter; Balmforth, Anthony J.; Campbell, Harry; Citterio, Lorena; De Grandi, Alessandro; Dominiczak, Anna; Duan, Jubao; Elliott, Paul; Elosua, Roberto; Eriksson, Johan G.; Freimer, Nelson B.; Geus, Eco J.C.; Glorioso, Nicola; Haiqing, Shen; Hartikainen, Anna-Liisa; Havulinna, Aki S.; Hicks, Andrew A.; Hui, Jennie; Igl, Wilmar; Illig, Thomas; Jula, Antti; Kajantie, Eero; Kilpeläinen, Tuomas O.; Koiranen, Markku; Kolcic, Ivana; Koskinen, Seppo; Kovacs, Peter; Laitinen, Jaana; Liu, Jianjun; Lokki, Marja-Liisa; Marusic, Ana; Maschio, Andrea; Meitinger, Thomas; Mulas, Antonella; Paré, Guillaume; Parker, Alex N.; Peden, John F.; Petersmann, Astrid; Pichler, Irene; Pietiläinen, Kirsi H.; Pouta, Anneli; Ridderstråle, Martin; Rotter, Jerome I.; Sambrook, Jennifer G.; Sanders, Alan R.; Schmidt, Carsten Oliver; Sinisalo, Juha; Smit, Jan H.; Stringham, Heather M.; Walters, G.Bragi; Widen, Elisabeth; Wild, Sarah H.; Willemsen, Gonneke; Zagato, Laura; Zgaga, Lina; Zitting, Paavo; Alavere, Helene; Farrall, Martin; McArdle, Wendy L.; Nelis, Mari; Peters, Marjolein J.; Ripatti, Samuli; van Meurs, Joyce B.J.; Aben, Katja K.; Ardlie, Kristin G; Beckmann, Jacques S.; Beilby, John P.; Bergman, Richard N.; Bergmann, Sven; Collins, Francis S.; Cusi, Daniele; den Heijer, Martin; Eiriksdottir, Gudny; Gejman, Pablo V.; Hall, Alistair S.; Hamsten, Anders; Huikuri, Heikki V.; Iribarren, Carlos; Kähönen, Mika; Kaprio, Jaakko; Kathiresan, Sekar; Kiemeney, Lambertus; Kocher, Thomas; Launer, Lenore J.; Lehtimäki, Terho; Melander, Olle; Mosley, Tom H.; Musk, Arthur W.; Nieminen, Markku S.; O'Donnell, Christopher J.; Ohlsson, Claes; Oostra, Ben; Palmer, Lyle J.; Raitakari, Olli; Ridker, Paul M.; Rioux, John D.; Rissanen, Aila; Rivolta, Carlo; Schunkert, Heribert; Shuldiner, Alan R.; Siscovick, David S.; Stumvoll, Michael; Tönjes, Anke; Tuomilehto, Jaakko; van Ommen, Gert-Jan; Viikari, Jorma; Heath, Andrew C.; Martin, Nicholas G.; Montgomery, Grant W.; Province, Michael A.; Kayser, Manfred; Arnold, Alice M.; Atwood, Larry D.; Boerwinkle, Eric; Chanock, Stephen J.; Deloukas, Panos; Gieger, Christian; Grönberg, Henrik; Hall, Per; Hattersley, Andrew T.; Hengstenberg, Christian; Hoffman, Wolfgang; Lathrop, G.Mark; Salomaa, Veikko; Schreiber, Stefan; Uda, Manuela; Waterworth, Dawn; Wright, Alan F.; Assimes, Themistocles L.; Barroso, Inês; Hofman, Albert; Mohlke, Karen L.; Boomsma, Dorret I.; Caulfield, Mark J.; Cupples, L.Adrienne; Erdmann, Jeanette; Fox, Caroline S.; Gudnason, Vilmundur; Gyllensten, Ulf; Harris, Tamara B.; Hayes, Richard B.; Jarvelin, Marjo-Riitta; Mooser, Vincent; Munroe, Patricia B.; Ouwehand, Willem H.; Penninx, Brenda W.; Pramstaller, Peter P.; Quertermous, Thomas; Rudan, Igor; Samani, Nilesh J.; Spector, Timothy D.; Völzke, Henry; Watkins, Hugh; Wilson, James F.; Groop, Leif C.; Haritunians, Talin; Hu, Frank B.; Kaplan, Robert C.; Metspalu, Andres; North, Kari E.; Schlessinger, David; Wareham, Nicholas J.; Hunter, David J.; O'Connell, Jeffrey R.; Strachan, David P.; Wichmann, H.-Erich; Borecki, Ingrid B.; van Duijn, Cornelia M.; Schadt, Eric E.; Thorsteinsdottir, Unnur; Peltonen, Leena; Uitterlinden, André; Visscher, Peter M.; Chatterjee, Nilanjan; Loos, Ruth J.F.; Boehnke, Michael; McCarthy, Mark I.; Ingelsson, Erik; Lindgren, Cecilia M.; Abecasis, Gonçalo R.; Stefansson, Kari; Frayling, Timothy M.; Hirschhorn, Joel N

2010-01-01

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence phenotype. Genome-wide association (GWA) studies have identified >600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the utility of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P=0.016), and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants, and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented amongst variants that alter amino acid structure of proteins and expression levels of nearby genes. Our data explain ∼10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ∼16% of phenotypic variation (∼20% of heritable variation). Although additional approaches are needed to fully dissect the genetic architecture of polygenic human traits, our findings indicate that GWA studies can identify large numbers of loci that

Emergent biological properties of arrestin pathway-selective biased agonism.

PubMed

Appleton, Kathryn M; Luttrell, Louis M

2013-06-01

Our growing appreciation of the pluridimensionality of G protein-coupled receptor (GPCR) signaling, combined with the phenomenon of orthosteric ligand "bias", has created the possibility of drugs that selectively modulate different aspects of GPCR function for therapeutic benefit. When viewed from the short-term perspective, e.g. changes in receptor conformation, effector coupling or second messenger generation, biased ligands appear to activate a subset of the response profile produced by a conventional agonist. Yet when examined in vivo, the limited data available suggest that biased ligand effects can diverge from their conventional counterparts in ways that cannot be predicted from their in vitro efficacy profile. What is currently missing, at least with respect to G protein and arrestin pathway-selective ligands, is a rational framework for relating the in vitro efficacy of a "biased" agonist to its in vivo actions that will enable drug screening programs to identify ligands with the desired biological effects.

The merged basins of signal transduction pathways in spatiotemporal cell biology.

PubMed

Hou, Yingchun; Hou, Yang; He, Siyu; Ma, Caixia; Sun, Mengyao; He, Huimin; Gao, Ning

2014-03-01

Numerous evidences have indicated that a signal system is composed by signal pathways, each pathway is composed by sub-pathways, and the sub-pathway is composed by the original signal terminals initiated with a protein/gene. We infer the terminal signals merged signal transduction system as "signal basin". In this article, we discussed the composition and regulation of signal basins, and the relationship between the signal basin control and triple W of spatiotemporal cell biology. Finally, we evaluated the importance of the systemic regulation to gene expression by signal basins under triple W. We hope our discussion will be the beginning to cause the attention for this area from the scientists of life science. © 2013 Wiley Periodicals, Inc.

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

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery.

PubMed

Bosl, William J

2007-02-15

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without

Using Bioinformatic Approaches to Identify Pathways Targeted by Human Leukemogens

PubMed Central

Thomas, Reuben; Phuong, Jimmy; McHale, Cliona M.; Zhang, Luoping

2012-01-01

We have applied bioinformatic approaches to identify pathways common to chemical leukemogens and to determine whether leukemogens could be distinguished from non-leukemogenic carcinogens. From all known and probable carcinogens classified by IARC and NTP, we identified 35 carcinogens that were associated with leukemia risk in human studies and 16 non-leukemogenic carcinogens. Using data on gene/protein targets available in the Comparative Toxicogenomics Database (CTD) for 29 of the leukemogens and 11 of the non-leukemogenic carcinogens, we analyzed for enrichment of all 250 human biochemical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The top pathways targeted by the leukemogens included metabolism of xenobiotics by cytochrome P450, glutathione metabolism, neurotrophin signaling pathway, apoptosis, MAPK signaling, Toll-like receptor signaling and various cancer pathways. The 29 leukemogens formed 18 distinct clusters comprising 1 to 3 chemicals that did not correlate with known mechanism of action or with structural similarity as determined by 2D Tanimoto coefficients in the PubChem database. Unsupervised clustering and one-class support vector machines, based on the pathway data, were unable to distinguish the 29 leukemogens from 11 non-leukemogenic known and probable IARC carcinogens. However, using two-class random forests to estimate leukemogen and non-leukemogen patterns, we estimated a 76% chance of distinguishing a random leukemogen/non-leukemogen pair from each other. PMID:22851955

A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs.

PubMed

Gondret, Florence; Vincent, Annie; Houée-Bigot, Magalie; Siegel, Anne; Lagarrigue, Sandrine; Causeur, David; Gilbert, Hélène; Louveau, Isabelle

2017-03-21

Animal's efficiency in converting feed into lean gain is a critical issue for the profitability of meat industries. This study aimed to describe shared and specific molecular responses in different tissues of pigs divergently selected over eight generations for residual feed intake (RFI). Pigs from the low RFI line had an improved gain-to-feed ratio during the test period and displayed higher leanness but similar adiposity when compared with pigs from the high RFI line at 132 days of age. Transcriptomics data were generated from longissimus muscle, liver and two adipose tissues using a porcine microarray and analyzed for the line effect (n = 24 pigs per line). The most apparent effect of the line was seen in muscle, whereas subcutaneous adipose tissue was the less affected tissue. Molecular data were analyzed by bioinformatics and subjected to multidimensional statistics to identify common biological processes across tissues and key genes participating to differences in the genetics of feed efficiency. Immune response, response to oxidative stress and protein metabolism were the main biological pathways shared by the four tissues that distinguished pigs from the low or high RFI lines. Many immune genes were under-expressed in the four tissues of the most efficient pigs. The main genes contributing to difference between pigs from the low vs high RFI lines were CD40, CTSC and NTN1. Different genes associated with energy use were modulated in a tissue-specific manner between the two lines. The gene expression program related to glycogen utilization was specifically up-regulated in muscle of pigs from the low RFI line (more efficient). Genes involved in fatty acid oxidation were down-regulated in muscle but were promoted in adipose tissues of the same pigs when compared with pigs from the high RFI line (less efficient). This underlined opposite line-associated strategies for energy use in skeletal muscle and adipose tissue. Genes related to cholesterol synthesis

miR2Pathway: A novel analytical method to discover MicroRNA-mediated dysregulated pathways involved in hepatocellular carcinoma.

PubMed

Li, Chaoxing; Dinu, Valentin

2018-05-01

MicroRNAs (miRNAs) are small, non-coding RNAs involved in the regulation of gene expression at a post-transcriptional level. Recent studies have shown miRNAs as key regulators of a variety of biological processes, such as proliferation, differentiation, apoptosis, metabolism, etc. Aberrantly expressed miRNAs influence individual gene expression level, but rewired miRNA-mRNA connections can influence the activity of biological pathways. Here, we define rewired miRNA-mRNA connections as the differential (rewiring) effects on the activity of biological pathways between hepatocellular carcinoma (HCC) and normal phenotypes. Our work presented here uses a PageRank-based approach to measure the degree of miRNA-mediated dysregulation of biological pathways between HCC and normal samples based on rewired miRNA-mRNA connections. In our study, we regard the degree of miRNA-mediated dysregulation of biological pathways as disease risk of biological pathways. Therefore, we propose a new method, miR2Pathway, to measure and rank the degree of miRNA-mediated dysregulation of biological pathways by measuring the total differential influence of miRNAs on the activity of pathways between HCC and normal states. miR2Pathway proposed here systematically shows the first evidence for a mechanism of biological pathways being dysregulated by rewired miRNA-mRNA connections, and provides new insight into exploring mechanisms behind HCC. Thus, miR2Pathway is a novel method to identify and rank miRNA-dysregulated pathways in HCC. Copyright © 2018 Elsevier Inc. All rights reserved.

Xtalk: a path-based approach for identifying crosstalk between signaling pathways

PubMed Central

Tegge, Allison N.; Sharp, Nicholas; Murali, T. M.

2016-01-01

Motivation: Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics. Results: We present Xtalk, a path-based approach for identifying pairs of pathways that may crosstalk. Xtalk computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, Xtalk reports the precise interactions and mechanisms that support the identified crosstalk. We applied Xtalk to signaling pathways in the KEGG and NCI-PID databases. We manually curated a gold standard set of 132 crosstalking pathway pairs and a set of 140 pairs that did not crosstalk, for which Xtalk achieved an area under the receiver operator characteristic curve of 0.65, a 12% improvement over the closest competing approach. The area under the receiver operator characteristic curve varied with the pathway, suggesting that crosstalk should be evaluated on a pathway-by-pathway level. We also analyzed an extended set of 658 pathway pairs in KEGG and to a set of more than 7000 pathway pairs in NCI-PID. For the top-ranking pairs, we found substantial support in the literature (81% for KEGG and 78% for NCI-PID). We provide examples of networks computed by Xtalk that accurately recovered known mechanisms of crosstalk. Availability and implementation: The XTALK software is available at http://bioinformatics.cs.vt.edu/~murali/software. Crosstalk networks are available at http://graphspace.org/graphs?tags=2015-bioinformatics-xtalk. Contact: ategge@vt.edu, murali@cs.vt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26400040

THE ADVERSE OUTCOME PATHWAY (AOP) FRAMEWORK: A FRAMEWORK FOR ORGANIZING BIOLOGICAL KNOWLEDGE LEADING TO HEALTH RISKS.

EPA Science Inventory

An Adverse Outcome Pathway (AOP) represents the organization of current and newly acquired knowledge of biological pathways. These pathways contain a series of nodes (Key Events, KEs) that when sufficiently altered influence the next node on the pathway, beginning from an Molecul...

A guide for building biological pathways along with two case studies: hair and breast development.

PubMed

Trindade, Daniel; Orsine, Lissur A; Barbosa-Silva, Adriano; Donnard, Elisa R; Ortega, J Miguel

2015-03-01

Genomic information is being underlined in the format of biological pathways. Building these biological pathways is an ongoing demand and benefits from methods for extracting information from biomedical literature with the aid of text-mining tools. Here we hopefully guide you in the attempt of building a customized pathway or chart representation of a system. Our manual is based on a group of software designed to look at biointeractions in a set of abstracts retrieved from PubMed. However, they aim to support the work of someone with biological background, who does not need to be an expert on the subject and will play the role of manual curator while designing the representation of the system, the pathway. We therefore illustrate with two challenging case studies: hair and breast development. They were chosen for focusing on recent acquisitions of human evolution. We produced sub-pathways for each study, representing different phases of development. Differently from most charts present in current databases, we present detailed descriptions, which will additionally guide PESCADOR users along the process. The implementation as a web interface makes PESCADOR a unique tool for guiding the user along the biointeractions, which will constitute a novel pathway. Copyright © 2014 Elsevier Inc. All rights reserved.

Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue.

PubMed

Kerkentzes, Konstantinos; Lagani, Vincenzo; Tsamardinos, Ioannis; Vyberg, Mogens; Røe, Oluf Dimitri

2014-01-01

Novel statistical methods and increasingly more accurate gene annotations can transform "old" biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches. The dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways. Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93-100% (AUC = 0.93-1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue. Reanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.

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

HIF Oxygen Sensing Pathways in Lung Biology.

PubMed

Urrutia, Andrés A; Aragonés, Julián

2018-06-06

Cellular responses to oxygen fluctuations are largely mediated by hypoxia-inducible factors (HIFs). Upon inhalation, the first organ inspired oxygen comes into contact with is the lungs, but the understanding of the pulmonary HIF oxygen-sensing pathway is still limited. In this review we will focus on the role of HIF1α and HIF2α isoforms in lung responses to oxygen insufficiency. In particular, we will discuss novel findings regarding their role in the biology of smooth muscle cells and endothelial cells in the context of hypoxia-induced pulmonary vasoconstriction. Moreover, we will also discuss recent studies into HIF-dependent responses in the airway epithelium, which have been even less studied than the HIF-dependent vascular responses in the lungs. In summary, we will review the biological functions executed by HIF1 or HIF2 in the pulmonary vessels and epithelium to control lung responses to oxygen fluctuations as well as their pathological consequences in the hypoxic lung.

Dissection of Biological Property of Chinese Acupuncture Point Zusanli Based on Long-Term Treatment via Modulating Multiple Metabolic Pathways.

PubMed

Yan, Guangli; Zhang, Aihua; Sun, Hui; Cheng, Weiping; Meng, Xiangcai; Liu, Li; Zhang, Yingzhi; Xie, Ning; Wang, Xijun

2013-01-01

Acupuncture has a history of over 3000 years and is a traditional Chinese medical therapy that uses hair-thin metal needles to puncture the skin at specific points on the body to promote wellbeing, while its molecular mechanism and ideal biological pathways are still not clear. High-throughput metabolomics is the global assessment of endogenous metabolites within a biologic system and can potentially provide a more accurate snap shot of the actual physiological state. We hypothesize that acupuncture-treated human would produce unique characterization of metabolic phenotypes. In this study, UPLC/ESI-HDMS coupled with pattern recognition methods and system analysis were carried out to investigate the mechanism and metabolite biomarkers for acupuncture treatment at "Zusanli" acupoint (ST-36) as a case study. The top 5 canonical pathways including alpha-linolenic acid metabolism, d-glutamine and d-glutamate metabolism, citrate cycle, alanine, aspartate, and glutamate metabolism, and vitamin B6 metabolism pathways were acutely perturbed, and 53 differential metabolites were identified by chemical profiling and may be useful to clarify the physiological basis and mechanism of ST-36. More importantly, network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Urine metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture.

The use of functional chemical-protein associations to identify multi-pathway renoprotectants.

PubMed

Xu, Jia; Meng, Kexin; Zhang, Rui; Yang, He; Liao, Chang; Zhu, Wenliang; Jiao, Jundong

2014-01-01

Typically, most nephropathies can be categorized as complex human diseases in which the cumulative effect of multiple minor genes, combined with environmental and lifestyle factors, determines the disease phenotype. Thus, multi-target drugs would be more likely to facilitate comprehensive renoprotection than single-target agents. In this study, functional chemical-protein association analysis was performed to retrieve multi-target drugs of high pathway wideness from the STITCH 3.1 database. Pathway wideness of a drug evaluated the efficiency of regulation of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in quantity. We identified nine experimentally validated renoprotectants that exerted remarkable impact on KEGG pathways by targeting a limited number of proteins. We selected curcumin as an illustrative compound to display the advantage of multi-pathway drugs on renoprotection. We compared curcumin with hemin, an agonist of heme oxygenase-1 (HO-1), which significantly affects only one KEGG pathway, porphyrin and chlorophyll metabolism (adjusted p = 1.5×10-5). At the same concentration (10 µM), both curcumin and hemin equivalently mitigated oxidative stress in H2O2-treated glomerular mesangial cells. The benefit of using hemin was derived from its agonistic effect on HO-1, providing relief from oxidative stress. Selective inhibition of HO-1 completely blocked the action of hemin but not that of curcumin, suggesting simultaneous multi-pathway intervention by curcumin. Curcumin also increased cellular autophagy levels, enhancing its protective effect; however, hemin had no effects. Based on the fact that the dysregulation of multiple pathways is implicated in the etiology of complex diseases, we proposed a feasible method for identifying multi-pathway drugs from compounds with validated targets. Our efforts will help identify multi-pathway agents capable of providing comprehensive protection against renal injuries.

Extensive cargo identification reveals distinct biological roles of the 12 importin pathways.

PubMed

Kimura, Makoto; Morinaka, Yuriko; Imai, Kenichiro; Kose, Shingo; Horton, Paul; Imamoto, Naoko

2017-01-24

Vast numbers of proteins are transported into and out of the nuclei by approximately 20 species of importin-β family nucleocytoplasmic transport receptors. However, the significance of the multiple parallel transport pathways that the receptors constitute is poorly understood because only limited numbers of cargo proteins have been reported. Here, we identified cargo proteins specific to the 12 species of human import receptors with a high-throughput method that employs stable isotope labeling with amino acids in cell culture, an in vitro reconstituted transport system, and quantitative mass spectrometry. The identified cargoes illuminated the manner of cargo allocation to the receptors. The redundancies of the receptors vary widely depending on the cargo protein. Cargoes of the same receptor are functionally related to one another, and the predominant protein groups in the cargo cohorts differ among the receptors. Thus, the receptors are linked to distinct biological processes by the nature of their cargoes.

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.

Methods and approaches in the topology-based analysis of biological pathways

PubMed Central

Mitrea, Cristina; Taghavi, Zeinab; Bokanizad, Behzad; Hanoudi, Samer; Tagett, Rebecca; Donato, Michele; Voichiţa, Călin; Drăghici, Sorin

2013-01-01

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as “third generation,” have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity. PMID:24133454

The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways.

PubMed

Sun, Yahui; Ma, Chenkai; Halgamuge, Saman

2017-12-28

Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer. We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.

Structural Identifiability of Dynamic Systems Biology Models

PubMed Central

Villaverde, Alejandro F.

2016-01-01

A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas. PMID:27792726

Metabolomics identifies a biological response to chronic low-dose natural uranium contamination in urine samples.

PubMed

Grison, Stéphane; Favé, Gaëlle; Maillot, Matthieu; Manens, Line; Delissen, Olivia; Blanchardon, Eric; Banzet, Nathalie; Defoort, Catherine; Bott, Romain; Dublineau, Isabelle; Aigueperse, Jocelyne; Gourmelon, Patrick; Martin, Jean-Charles; Souidi, Maâmar

2013-01-01

Because uranium is a natural element present in the earth's crust, the population may be chronically exposed to low doses of it through drinking water. Additionally, the military and civil uses of uranium can also lead to environmental dispersion that can result in high or low doses of acute or chronic exposure. Recent experimental data suggest this might lead to relatively innocuous biological reactions. The aim of this study was to assess the biological changes in rats caused by ingestion of natural uranium in drinking water with a mean daily intake of 2.7 mg/kg for 9 months and to identify potential biomarkers related to such a contamination. Subsequently, we observed no pathology and standard clinical tests were unable to distinguish between treated and untreated animals. Conversely, LC-MS metabolomics identified urine as an appropriate biofluid for discriminating the experimental groups. Of the 1,376 features detected in urine, the most discriminant were metabolites involved in tryptophan, nicotinate, and nicotinamide metabolic pathways. In particular, N -methylnicotinamide, which was found at a level seven times higher in untreated than in contaminated rats, had the greatest discriminating power. These novel results establish a proof of principle for using metabolomics to address chronic low-dose uranium contamination. They open interesting perspectives for understanding the underlying biological mechanisms and designing a diagnostic test of exposure.

Biological pathways involved in the development of inflammatory bowel disease.

PubMed

Zemljic, Mateja; Pejkovic, Bozena; Krajnc, Ivan; Lipovsek, Saska

2014-10-01

Apoptosis, autophagy and necrosis are three distinct functional types of the mammalian cell death network. All of them are characterized by a number of cell's morphological changes. The inappropriate induction of cell death is involved in the pathogenesis of a number of diseases.Pathogenesis of inflammatory bowel diseases (ulcerative colitis, Crohn's disease) includes an abnormal immunological response to disturbed intestinal microflora. One of the most important reason in pathogenesis of chronic inflammatory disease and subsequent multiple organ pathology is a barrier function of the gut, regulating cellular viability. Recent findings have begun to explain the mechanisms by which intestinal epithelial cells are able to survive in such an environment and how loss of normal regulatory processes may lead to inflammatory bowel disease (IBD).This review focuses on the regulation of biological pathways in development and homeostasis in IBD. Better understanding of the physiological functions of biological pathways and their influence on inflammation, immunity, and barrier function will simplify our expertice of homeostasis in the gastrointestinal tract and in upgrading diagnosis and treatment.

Genome-Wide siRNA-Based Functional Genomics of Pigmentation Identifies Novel Genes and Pathways That Impact Melanogenesis in Human Cells

PubMed Central

Bodemann, Brian; Petersen, Sean; Aruri, Jayavani; Koshy, Shiney; Richardson, Zachary; Le, Lu Q.; Krasieva, Tatiana; Roth, Michael G.; Farmer, Pat; White, Michael A.

2008-01-01

Melanin protects the skin and eyes from the harmful effects of UV irradiation, protects neural cells from toxic insults, and is required for sound conduction in the inner ear. Aberrant regulation of melanogenesis underlies skin disorders (melasma and vitiligo), neurologic disorders (Parkinson's disease), auditory disorders (Waardenburg's syndrome), and opthalmologic disorders (age related macular degeneration). Much of the core synthetic machinery driving melanin production has been identified; however, the spectrum of gene products participating in melanogenesis in different physiological niches is poorly understood. Functional genomics based on RNA-mediated interference (RNAi) provides the opportunity to derive unbiased comprehensive collections of pharmaceutically tractable single gene targets supporting melanin production. In this study, we have combined a high-throughput, cell-based, one-well/one-gene screening platform with a genome-wide arrayed synthetic library of chemically synthesized, small interfering RNAs to identify novel biological pathways that govern melanin biogenesis in human melanocytes. Ninety-two novel genes that support pigment production were identified with a low false discovery rate. Secondary validation and preliminary mechanistic studies identified a large panel of targets that converge on tyrosinase expression and stability. Small molecule inhibition of a family of gene products in this class was sufficient to impair chronic tyrosinase expression in pigmented melanoma cells and UV-induced tyrosinase expression in primary melanocytes. Isolation of molecular machinery known to support autophagosome biosynthesis from this screen, together with in vitro and in vivo validation, exposed a close functional relationship between melanogenesis and autophagy. In summary, these studies illustrate the power of RNAi-based functional genomics to identify novel genes, pathways, and pharmacologic agents that impact a biological phenotype and operate

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

An integrative data mining approach to identifying adverse outcome pathway signatures.

PubMed

Oki, Noffisat O; Edwards, Stephen W

2016-03-28

The Adverse Outcome Pathway (AOP) framework is a tool for making biological connections and summarizing key information across different levels of biological organization to connect biological perturbations at the molecular level to adverse outcomes for an individual or population. Computational approaches to explore and determine these connections can accelerate the assembly of AOPs. By leveraging the wealth of publicly available data covering chemical effects on biological systems, computationally-predicted AOPs (cpAOPs) were assembled via data mining of high-throughput screening (HTS) in vitro data, in vivo data and other disease phenotype information. Frequent Itemset Mining (FIM) was used to find associations between the gene targets of ToxCast HTS assays and disease data from Comparative Toxicogenomics Database (CTD) by using the chemicals as the common aggregators between datasets. The method was also used to map gene expression data to disease data from CTD. A cpAOP network was defined by considering genes and diseases as nodes and FIM associations as edges. This network contained 18,283 gene to disease associations for the ToxCast data and 110,253 for CTD gene expression. Two case studies show the value of the cpAOP network by extracting subnetworks focused either on fatty liver disease or the Aryl Hydrocarbon Receptor (AHR). The subnetwork surrounding fatty liver disease included many genes known to play a role in this disease. When querying the cpAOP network with the AHR gene, an interesting subnetwork including glaucoma was identified. While substantial literature exists to support the potential for AHR ligands to elicit glaucoma, it was not explicitly captured in the public annotation information in CTD. The subnetwork from this analysis suggests a cpAOP that includes changes in CYP1B1 expression, which has been previously established in the literature as a primary cause of glaucoma. These case studies highlight the value in integrating multiple data

Metabolomic profiling and genomic analysis of wheat aneuploid lines to identify genes controlling biochemical pathways in mature grain.

PubMed

Francki, Michael G; Hayton, Sarah; Gummer, Joel P A; Rawlinson, Catherine; Trengove, Robert D

2016-02-01

Metabolomics is becoming an increasingly important tool in plant genomics to decipher the function of genes controlling biochemical pathways responsible for trait variation. Although theoretical models can integrate genes and metabolites for trait variation, biological networks require validation using appropriate experimental genetic systems. In this study, we applied an untargeted metabolite analysis to mature grain of wheat homoeologous group 3 ditelosomic lines, selected compounds that showed significant variation between wheat lines Chinese Spring and at least one ditelosomic line, tracked the genes encoding enzymes of their biochemical pathway using the wheat genome survey sequence and determined the genetic components underlying metabolite variation. A total of 412 analytes were resolved in the wheat grain metabolome, and principal component analysis indicated significant differences in metabolite profiles between Chinese Spring and each ditelosomic lines. The grain metabolome identified 55 compounds positively matched against a mass spectral library where the majority showed significant differences between Chinese Spring and at least one ditelosomic line. Trehalose and branched-chain amino acids were selected for detailed investigation, and it was expected that if genes encoding enzymes directly related to their biochemical pathways were located on homoeologous group 3 chromosomes, then corresponding ditelosomic lines would have a significant reduction in metabolites compared with Chinese Spring. Although a proportion showed a reduction, some lines showed significant increases in metabolites, indicating that genes directly and indirectly involved in biosynthetic pathways likely regulate the metabolome. Therefore, this study demonstrated that wheat aneuploid lines are suitable experimental genetic system to validate metabolomics-genomics networks. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

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

Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers

PubMed Central

2013-01-01

Background Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer. Results We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis. Conclusions In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/. PMID:23822816

Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk.

PubMed

Warren, Helen R; Evangelou, Evangelos; Cabrera, Claudia P; Gao, He; Ren, Meixia; Mifsud, Borbala; Ntalla, Ioanna; Surendran, Praveen; Liu, Chunyu; Cook, James P; Kraja, Aldi T; Drenos, Fotios; Loh, Marie; Verweij, Niek; Marten, Jonathan; Karaman, Ibrahim; Lepe, Marcelo P Segura; O'Reilly, Paul F; Knight, Joanne; Snieder, Harold; Kato, Norihiro; He, Jiang; Tai, E Shyong; Said, M Abdullah; Porteous, David; Alver, Maris; Poulter, Neil; Farrall, Martin; Gansevoort, Ron T; Padmanabhan, Sandosh; Mägi, Reedik; Stanton, Alice; Connell, John; Bakker, Stephan J L; Metspalu, Andres; Shields, Denis C; Thom, Simon; Brown, Morris; Sever, Peter; Esko, Tõnu; Hayward, Caroline; van der Harst, Pim; Saleheen, Danish; Chowdhury, Rajiv; Chambers, John C; Chasman, Daniel I; Chakravarti, Aravinda; Newton-Cheh, Christopher; Lindgren, Cecilia M; Levy, Daniel; Kooner, Jaspal S; Keavney, Bernard; Tomaszewski, Maciej; Samani, Nilesh J; Howson, Joanna M M; Tobin, Martin D; Munroe, Patricia B; Ehret, Georg B; Wain, Louise V

2017-03-01

Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide. We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK Biobank participants of European ancestry with independent replication in other cohorts, and robust validation of 107 independent loci. We also identify new independent variants at 11 previously reported blood pressure loci. In combination with results from a range of in silico functional analyses and wet bench experiments, our findings highlight new biological pathways for blood pressure regulation enriched for genes expressed in vascular tissues and identify potential therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure-raising genetic variants on future cardiovascular disease risk.

Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk

PubMed Central

Ntalla, Ioanna; Surendran, Praveen; Liu, Chunyu; Cook, James P; Kraja, Aldi T; Drenos, Fotios; Loh, Marie; Verweij, Niek; Marten, Jonathan; Karaman, Ibrahim; Segura Lepe, Marcelo P; O’Reilly, Paul F; Knight, Joanne; Snieder, Harold; Kato, Norihiro; He, Jiang; Tai, E Shyong; Said, M Abdullah; Porteous, David; Alver, Maris; Poulter, Neil; Farrall, Martin; Gansevoort, Ron T; Padmanabhan, Sandosh; Mägi, Reedik; Stanton, Alice; Connell, John; Bakker, Stephan J L; Metspalu, Andres; Shields, Denis C; Thom, Simon; Brown, Morris; Sever, Peter; Esko, Tõnu; Hayward, Caroline; van der Harst, Pim; Saleheen, Danish; Chowdhury, Rajiv; Chambers, John C; Chasman, Daniel I; Chakravarti, Aravinda; Newton-Cheh, Christopher; Lindgren, Cecilia M; Levy, Daniel; Kooner, Jaspal S; Keavney, Bernard; Tomaszewski, Maciej; Samani, Nilesh J; Howson, Joanna M M; Tobin, Martin D; Munroe, Patricia B; Ehret, Georg B; Wain, Louise V

2017-01-01

Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide. We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK Biobank participants of European ancestry with independent replication in other cohorts, and robust validation of 107 independent loci. We also identify new independent variants at 11 previously reported blood pressure loci. Combined with results from a range of in silico functional analyses and wet bench experiments, our findings highlight new biological pathways for blood pressure regulation enriched for genes expressed in vascular tissues and identify potential therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure raising genetic variants on future cardiovascular disease risk. PMID:28135244

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

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

A Combined Pathway and Regional Heritability Analysis Indicates NETRIN1 Pathway Is Associated With Major Depressive Disorder.

PubMed

Zeng, Yanni; Navarro, Pau; Fernandez-Pujals, Ana M; Hall, Lynsey S; Clarke, Toni-Kim; Thomson, Pippa A; Smith, Blair H; Hocking, Lynne J; Padmanabhan, Sandosh; Hayward, Caroline; MacIntyre, Donald J; Wray, Naomi R; Deary, Ian J; Porteous, David J; Haley, Chris S; McIntosh, Andrew M

2017-02-15

Genome-wide association studies (GWASs) of major depressive disorder (MDD) have identified few significant associations. Testing the aggregation of genetic variants, in particular biological pathways, may be more powerful. Regional heritability analysis can be used to detect genomic regions that contribute to disease risk. We integrated pathway analysis and multilevel regional heritability analyses in a pipeline designed to identify MDD-associated pathways. The pipeline was applied to two independent GWAS samples [Generation Scotland: The Scottish Family Health Study (GS:SFHS, N = 6455) and Psychiatric Genomics Consortium (PGC:MDD) (N = 18,759)]. A polygenic risk score (PRS) composed of single nucleotide polymorphisms from the pathway most consistently associated with MDD was created, and its accuracy to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested. In GS:SFHS, four pathways were significantly associated with MDD, and two of these explained a significant amount of pathway-level regional heritability. In PGC:MDD, one pathway was significantly associated with MDD. Pathway-level regional heritability was significant in this pathway in one subset of PGC:MDD. For both samples the regional heritabilities were further localized to the gene and subregion levels. The NETRIN1 signaling pathway showed the most consistent association with MDD across the two samples. PRSs from this pathway showed competitive predictive accuracy compared with the whole-genome PRSs when using area under the curve statistics, logistic regression, and linear mixed model. These post-GWAS analyses highlight the value of combining multiple methods on multiple GWAS data for the identification of risk pathways for MDD. The NETRIN1 signaling pathway is identified as a candidate pathway for MDD and should be explored in further large population studies. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights

Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways

EPA Science Inventory

Confidence in the quantitative prediction of risk is increased when the prediction is based to as great an extent as possible on the relevant biological factors that constitute the pathway from exposure to adverse outcome. With the first examples now over 40 years old, physiologi...

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

PubMed Central

2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

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.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

PubMed

Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville-Petteri; Yang, Xia

2016-09-09

Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators

An ontology-driven semantic mashup of gene and biological pathway information: application to the domain of nicotine dependence.

PubMed

Sahoo, Satya S; Bodenreider, Olivier; Rutter, Joni L; Skinner, Karen J; Sheth, Amit P

2008-10-01

This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. RESOURCE PAGE: http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/

ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis

PubMed Central

Han, Junwei; Shi, Xinrui; Zhang, Yunpeng; Xu, Yanjun; Jiang, Ying; Zhang, Chunlong; Feng, Li; Yang, Haixiu; Shang, Desi; Sun, Zeguo; Su, Fei; Li, Chunquan; Li, Xia

2015-01-01

Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther). PMID:26267116

Systems biology of the modified branched Entner-Doudoroff pathway in Sulfolobus solfataricus

PubMed Central

Figueiredo, Ana Sofia; Esser, Dominik; Haferkamp, Patrick; Wieloch, Patricia; Schomburg, Dietmar; Siebers, Bettina; Schaber, Jörg

2017-01-01

Sulfolobus solfataricus is a thermoacidophilic Archaeon that thrives in terrestrial hot springs (solfatares) with optimal growth at 80°C and pH 2–4. It catabolizes specific carbon sources, such as D-glucose, to pyruvate via the modified Entner-Doudoroff (ED) pathway. This pathway has two parallel branches, the semi-phosphorylative and the non-phosphorylative. However, the strategy of S.solfataricus to endure in such an extreme environment in terms of robustness and adaptation is not yet completely understood. Here, we present the first dynamic mathematical model of the ED pathway parameterized with quantitative experimental data. These data consist of enzyme activities of the branched pathway at 70°C and 80°C and of metabolomics data at the same temperatures for the wild type and for a metabolic engineered knockout of the semi-phosphorylative branch. We use the validated model to address two questions: 1. Is this system more robust to perturbations at its optimal growth temperature? 2. Is the ED robust to deletion and perturbations? We employed a systems biology approach to answer these questions and to gain further knowledge on the emergent properties of this biological system. Specifically, we applied deterministic and stochastic approaches to study the sensitivity and robustness of the system, respectively. The mathematical model we present here, shows that: 1. Steady state metabolite concentrations of the ED pathway are consistently more robust to stochastic internal perturbations at 80°C than at 70°C; 2. These metabolite concentrations are highly robust when faced with the knockout of either branch. Connected with this observation, these two branches show different properties at the level of metabolite production and flux control. These new results reveal how enzyme kinetics and metabolomics synergizes with mathematical modelling to unveil new systemic properties of the ED pathway in S.solfataricus in terms of its adaptation and robustness. PMID

Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer.

PubMed

Wei, Runmin; De Vivo, Immaculata; Huang, Sijia; Zhu, Xun; Risch, Harvey; Moore, Jason H; Yu, Herbert; Garmire, Lana X

2016-08-23

Endometrial Cancer (EC) is one of the most common female cancers. Genome-wide association studies (GWAS) have been investigated to identify genetic polymorphisms that are predictive of EC risks. Here we utilized a meta-dimensional integrative approach to seek genetically susceptible pathways that may be associated with tumorigenesis and progression of EC. We analyzed GWAS data obtained from Connecticut Endometrial Cancer Study (CECS) and identified the top 20 EC susceptible pathways. To further verify the significance of top 20 EC susceptible pathways, we conducted pathway-level multi-omics analyses using EC exome-Seq, RNA-Seq and survival data, all based on The Cancer Genome Atlas (TCGA) samples. We measured the overall consistent rankings of these pathways in all four data types. Some well-studied pathways, such as p53 signaling and cell cycle pathways, show consistently high rankings across different analyses. Additionally, other cell signaling pathways (e.g. IGF-1/mTOR, rac-1 and IL-5 pathway), genetic information processing pathway (e.g. homologous recombination) and metabolism pathway (e.g. sphingolipid metabolism) are also highly associated with EC risks, diagnosis and prognosis. In conclusion, the meta-dimensional integration of EC cohorts has suggested some common pathways that may be associated from predisposition, tumorigenesis to progression.

Predicting hepatocellular carcinoma through cross-talk genes identified by risk pathways

PubMed Central

Shao, Zhuo; Huo, Diwei; Zhang, Denan; Xie, Hongbo; Yang, Jingbo; Liu, Qiuqi; Chen, Xiujie

2018-01-01

Hepatocellular carcinoma (HCC) is the most frequent type of liver cancer with poor survival rate and high mortality. Despite efforts on the mechanism of HCC, new molecular markers are needed for exact diagnosis, evaluation and treatment. Here, we combined transcriptome of HCC with networks and pathways to identify reliable molecular markers. Through integrating 249 differentially expressed genes with syncretic protein interaction networks, we constructed a HCC-specific network, from which we further extracted 480 pivotal genes. Based on the cross-talk between the enriched pathways of the pivotal genes, we finally identified a HCC signature of 45 genes, which could accurately distinguish HCC patients with normal individuals and reveal the prognosis of HCC patients. Among these 45 genes, 15 showed dysregulated expression patterns and a part have been reported to be associated with HCC and/or other cancers. These findings suggested that our identified 45 gene signature could be potential and valuable molecular markers for diagnosis and evaluation of HCC. PMID:29765536

CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

PubMed Central

Li, Yang; Liu, Jun S.; Mootha, Vamsi K.

2017-01-01

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active. PMID:28719601

A probabilistic framework for identifying biosignatures using Pathway Complexity

NASA Astrophysics Data System (ADS)

Marshall, Stuart M.; Murray, Alastair R. G.; Cronin, Leroy

2017-11-01

One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can easily be distinguished from non-living material assemblies. Many complex artefacts, from ordinary bioproducts to human tools, though they are not living things, are ultimately produced by biological processes-whether those processes occur at the scale of cells or societies, they are the consequences of living systems. While these objects are not living, they cannot randomly form, as they are the product of a biological organism and hence are either technological or cultural biosignatures. A generalized approach that aims to evaluate complex objects as possible biosignatures could be useful to explore the cosmos for new life forms. However, it is not obvious how it might be possible to create such a self-contained approach. This would require us to prove rigorously that a given artefact is too complex to have formed by chance. In this paper, we present a new type of complexity measure, which we call `Pathway Complexity', that allows us not only to threshold the abiotic-biotic divide, but also to demonstrate a probabilistic approach based on object abundance and complexity which can be used to unambiguously assign complex objects as biosignatures. We hope that this approach will not only open up the search for biosignatures beyond the Earth, but also allow us to explore the Earth for new types of biology, and to determine when a complex chemical system discovered in the laboratory could be considered alive. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways.

PubMed

Gu, Xiang; Liu, Cong-Jian; Wei, Jian-Jie

2017-11-13

Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.

Obesity and psychiatric disorders: commonalities in dysregulated biological pathways and their implications for treatment.

PubMed

Lopresti, Adrian L; Drummond, Peter D

2013-08-01

Rates of obesity are higher than normal across a range of psychiatric disorders, including major depressive disorder, bipolar disorder, schizophrenia and anxiety disorders. While the problem of obesity is generally acknowledged in mental health research and treatment, an understanding of their bi-directional relationship is still developing. In this review the association between obesity and psychiatric disorders is summarised, with a specific emphasis on similarities in their disturbed biological pathways; namely neurotransmitter imbalances, hypothalamus-pituitary-adrenal axis disturbances, dysregulated inflammatory pathways, increased oxidative and nitrosative stress, mitochondrial disturbances, and neuroprogression. The applicability and effectiveness of weight-loss interventions in psychiatric populations are reviewed along with their potential efficacy in ameliorating disturbed biological pathways, particularly those mediating inflammation and oxidative stress. It is proposed that weight loss may not only be an effective intervention to enhance physical health but may also improve mental health outcomes and slow the rate of neuroprogressive disturbances in psychiatric disorders. Areas of future research to help expand our understanding of the relationship between obesity and psychiatric disorders are also outlined. Copyright © 2013 Elsevier Inc. All rights reserved.

Optical techniques for biological triggers and identifiers

NASA Astrophysics Data System (ADS)

Grant, Bruce A. C.

2004-12-01

Optical techniques for the classification and identification of biological particles provide a number of advantages over traditional 'Wet Chemistry" methods, amongst which are speed of response and the reduction/elimination of consumables. These techniques can be employed in both 'Trigger" and 'Identifier" systems. Trigger systems monitor environmental particulates with the aim of detecting 'unusual" changes in the overall environmental composition and providing an indication of threat. At the present time there is no single optical measurement that can distinguish between benign and hostile events. Therefore, in order to distinguish between these 2 classifications, a number of different measurements must be effected and a decision made on the basis of the 'integrated" data. Smiths Detection have developed a data gathering platform capable of measuring multiple optical, physical and electrical parameters of individual airborne biological particles. The data from all these measurements are combined in a hazard classification algorithm based on Bayesian Inference techniques. Identifier systems give a greater level of information and confidence than triggers, -- although they require reagents and are therefore much more expensive to operate -- and typically take upwards of 20 minutes to respond. Ideally, in a continuous flow mode, identifier systems would respond in real-time, and identify a range of pathogens specifically and simultaneously. The results of recent development work -- carried out by Smiths Detection and its collaborators -- to develop an optical device that meets most of these requirements, and has the stretch potential to meet all of the requirements in a 3-5 year time frame will be presented. This technology enables continuous stand-alone operation for both civil and military defense applications and significant miniaturisation can be achieved with further development.

An ontology-driven semantic mash-up of gene and biological pathway information: Application to the domain of nicotine dependence

PubMed Central

Sahoo, Satya S.; Bodenreider, Olivier; Rutter, Joni L.; Skinner, Karen J.; Sheth, Amit P.

2008-01-01

Objectives This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. Methods We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Results Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Conclusion Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. Resource page http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/ PMID:18395495

An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types

PubMed Central

Park, Sunho; Kim, Seung-Jun; Yu, Donghyeon; Peña-Llopis, Samuel; Gao, Jianjiong; Park, Jin Suk; Chen, Beibei; Norris, Jessie; Wang, Xinlei; Chen, Min; Kim, Minsoo; Yong, Jeongsik; Wardak, Zabi; Choe, Kevin; Story, Michael; Starr, Timothy; Cheong, Jae-Ho; Hwang, Tae Hyun

2016-01-01

Motivation: Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovative approach to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers. Results: We applied our approach to The Cancer Genome Atlas (TCGA) dataset of somatic mutations in 4790 cancer patients with 19 different types of tumors. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and targets of currently available drugs. To investigate the clinical significance of these altered pathways, we performed consensus clustering for patient stratification using member genes in the altered pathways coupled with gene expression datasets from 4870 patients from TCGA, and multiple independent cohorts confirmed that the altered pathways could be used to stratify patients into subgroups with significantly different clinical outcomes. Of particular significance, certain patient subpopulations with poor prognosis were identified because they had specific altered pathways for which there are available targeted therapies. These findings could be used to tailor and intensify therapy in these patients, for whom current therapy is suboptimal. Availability and implementation: The code is available at: http://www.taehyunlab.org. Contact: jhcheong@yuhs.ac or taehyun.hwang@utsouthwestern.edu or taehyun.cs@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26635139

Transcriptional Profiling of Hypoxic Neural Stem Cells Identifies Calcineurin-NFATc4 Signaling as a Major Regulator of Neural Stem Cell Biology

PubMed Central

Moreno, Marta; Fernández, Virginia; Monllau, Josep M.; Borrell, Víctor; Lerin, Carles; de la Iglesia, Núria

2015-01-01

Summary Neural stem cells (NSCs) reside in a hypoxic microenvironment within the brain. However, the crucial transcription factors (TFs) that regulate NSC biology under physiologic hypoxia are poorly understood. Here we have performed gene set enrichment analysis (GSEA) of microarray datasets from hypoxic versus normoxic NSCs with the aim of identifying pathways and TFs that are activated under oxygen concentrations mimicking normal brain tissue microenvironment. Integration of TF target (TFT) and pathway enrichment analysis identified the calcium-regulated TF NFATc4 as a major candidate to regulate hypoxic NSC functions. Nfatc4 expression was coordinately upregulated by top hypoxia-activated TFs, while NFATc4 target genes were enriched in hypoxic NSCs. Loss-of-function analyses further revealed that the calcineurin-NFATc4 signaling axis acts as a major regulator of NSC self-renewal and proliferation in vitro and in vivo by promoting the expression of TFs, including Id2, that contribute to the maintenance of the NSC state. PMID:26235896

Estimating Toxicity-Related Biological Pathway Altering Doses for High-Throughput Chemical Risk Assessment

EPA Science Inventory

We describe a framework for estimating the human dose at which a chemical significantly alters a biological pathway in vivo, making use of in vitro assay data and an in vitro derived pharmacokinetic model, coupled with estimates of population variability and uncertainty. The q...

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

A strategy for evaluating pathway analysis methods.

PubMed

Yu, Chenggang; Woo, Hyung Jun; Yu, Xueping; Oyama, Tatsuya; Wallqvist, Anders; Reifman, Jaques

2017-10-13

Researchers have previously developed a multitude of methods designed to identify biological pathways associated with specific clinical or experimental conditions of interest, with the aim of facilitating biological interpretation of high-throughput data. Before practically applying such pathway analysis (PA) methods, we must first evaluate their performance and reliability, using datasets where the pathways perturbed by the conditions of interest have been well characterized in advance. However, such 'ground truths' (or gold standards) are often unavailable. Furthermore, previous evaluation strategies that have focused on defining 'true answers' are unable to systematically and objectively assess PA methods under a wide range of conditions. In this work, we propose a novel strategy for evaluating PA methods independently of any gold standard, either established or assumed. The strategy involves the use of two mutually complementary metrics, recall and discrimination. Recall measures the consistency of the perturbed pathways identified by applying a particular analysis method to an original large dataset and those identified by the same method to a sub-dataset of the original dataset. In contrast, discrimination measures specificity-the degree to which the perturbed pathways identified by a particular method to a dataset from one experiment differ from those identifying by the same method to a dataset from a different experiment. We used these metrics and 24 datasets to evaluate six widely used PA methods. The results highlighted the common challenge in reliably identifying significant pathways from small datasets. Importantly, we confirmed the effectiveness of our proposed dual-metric strategy by showing that previous comparative studies corroborate the performance evaluations of the six methods obtained by our strategy. Unlike any previously proposed strategy for evaluating the performance of PA methods, our dual-metric strategy does not rely on any ground truth

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

Recursive random forest algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways.

PubMed

Deng, Wenping; Zhang, Kui; Busov, Victor; Wei, Hairong

2017-01-01

Present knowledge indicates a multilayered hierarchical gene regulatory network (ML-hGRN) often operates above a biological pathway. Although the ML-hGRN is very important for understanding how a pathway is regulated, there is almost no computational algorithm for directly constructing ML-hGRNs. A backward elimination random forest (BWERF) algorithm was developed for constructing the ML-hGRN operating above a biological pathway. For each pathway gene, the BWERF used a random forest model to calculate the importance values of all transcription factors (TFs) to this pathway gene recursively with a portion (e.g. 1/10) of least important TFs being excluded in each round of modeling, during which, the importance values of all TFs to the pathway gene were updated and ranked until only one TF was remained in the list. The above procedure, termed BWERF. After that, the importance values of a TF to all pathway genes were aggregated and fitted to a Gaussian mixture model to determine the TF retention for the regulatory layer immediately above the pathway layer. The acquired TFs at the secondary layer were then set to be the new bottom layer to infer the next upper layer, and this process was repeated until a ML-hGRN with the expected layers was obtained. BWERF improved the accuracy for constructing ML-hGRNs because it used backward elimination to exclude the noise genes, and aggregated the individual importance values for determining the TFs retention. We validated the BWERF by using it for constructing ML-hGRNs operating above mouse pluripotency maintenance pathway and Arabidopsis lignocellulosic pathway. Compared to GENIE3, BWERF showed an improvement in recognizing authentic TFs regulating a pathway. Compared to the bottom-up Gaussian graphical model algorithm we developed for constructing ML-hGRNs, the BWERF can construct ML-hGRNs with significantly reduced edges that enable biologists to choose the implicit edges for experimental validation.

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

PubMed

Scott, Robert A; Lagou, Vasiliki; Welch, Ryan P; Wheeler, Eleanor; Montasser, May E; Luan, Jian'an; Mägi, Reedik; Strawbridge, Rona J; Rehnberg, Emil; Gustafsson, Stefan; Kanoni, Stavroula; Rasmussen-Torvik, Laura J; Yengo, Loïc; Lecoeur, Cecile; Shungin, Dmitry; Sanna, Serena; Sidore, Carlo; Johnson, Paul C D; Jukema, J Wouter; Johnson, Toby; Mahajan, Anubha; Verweij, Niek; Thorleifsson, Gudmar; Hottenga, Jouke-Jan; Shah, Sonia; Smith, Albert V; Sennblad, Bengt; Gieger, Christian; Salo, Perttu; Perola, Markus; Timpson, Nicholas J; Evans, David M; Pourcain, Beate St; Wu, Ying; Andrews, Jeanette S; Hui, Jennie; Bielak, Lawrence F; Zhao, Wei; Horikoshi, Momoko; Navarro, Pau; Isaacs, Aaron; O'Connell, Jeffrey R; Stirrups, Kathleen; Vitart, Veronique; Hayward, Caroline; Esko, Tõnu; Mihailov, Evelin; Fraser, Ross M; Fall, Tove; Voight, Benjamin F; Raychaudhuri, Soumya; Chen, Han; Lindgren, Cecilia M; Morris, Andrew P; Rayner, Nigel W; Robertson, Neil; Rybin, Denis; Liu, Ching-Ti; Beckmann, Jacques S; Willems, Sara M; Chines, Peter S; Jackson, Anne U; Kang, Hyun Min; Stringham, Heather M; Song, Kijoung; Tanaka, Toshiko; Peden, John F; Goel, Anuj; Hicks, Andrew A; An, Ping; Müller-Nurasyid, Martina; Franco-Cereceda, Anders; Folkersen, Lasse; Marullo, Letizia; Jansen, Hanneke; Oldehinkel, Albertine J; Bruinenberg, Marcel; Pankow, James S; North, Kari E; Forouhi, Nita G; Loos, Ruth J F; Edkins, Sarah; Varga, Tibor V; Hallmans, Göran; Oksa, Heikki; Antonella, Mulas; Nagaraja, Ramaiah; Trompet, Stella; Ford, Ian; Bakker, Stephan J L; Kong, Augustine; Kumari, Meena; Gigante, Bruna; Herder, Christian; Munroe, Patricia B; Caulfield, Mark; Antti, Jula; Mangino, Massimo; Small, Kerrin; Miljkovic, Iva; Liu, Yongmei; Atalay, Mustafa; Kiess, Wieland; James, Alan L; Rivadeneira, Fernando; Uitterlinden, Andre G; Palmer, Colin N A; Doney, Alex S F; Willemsen, Gonneke; Smit, Johannes H; Campbell, Susan; Polasek, Ozren; Bonnycastle, Lori L; Hercberg, Serge; Dimitriou, Maria; Bolton, Jennifer L; Fowkes, Gerard R; Kovacs, Peter; Lindström, Jaana; Zemunik, Tatijana; Bandinelli, Stefania; Wild, Sarah H; Basart, Hanneke V; Rathmann, Wolfgang; Grallert, Harald; Maerz, Winfried; Kleber, Marcus E; Boehm, Bernhard O; Peters, Annette; Pramstaller, Peter P; Province, Michael A; Borecki, Ingrid B; Hastie, Nicholas D; Rudan, Igor; Campbell, Harry; Watkins, Hugh; Farrall, Martin; Stumvoll, Michael; Ferrucci, Luigi; Waterworth, Dawn M; Bergman, Richard N; Collins, Francis S; Tuomilehto, Jaakko; Watanabe, Richard M; de Geus, Eco J C; Penninx, Brenda W; Hofman, Albert; Oostra, Ben A; Psaty, Bruce M; Vollenweider, Peter; Wilson, James F; Wright, Alan F; Hovingh, G Kees; Metspalu, Andres; Uusitupa, Matti; Magnusson, Patrik K E; Kyvik, Kirsten O; Kaprio, Jaakko; Price, Jackie F; Dedoussis, George V; Deloukas, Panos; Meneton, Pierre; Lind, Lars; Boehnke, Michael; Shuldiner, Alan R; van Duijn, Cornelia M; Morris, Andrew D; Toenjes, Anke; Peyser, Patricia A; Beilby, John P; Körner, Antje; Kuusisto, Johanna; Laakso, Markku; Bornstein, Stefan R; Schwarz, Peter E H; Lakka, Timo A; Rauramaa, Rainer; Adair, Linda S; Smith, George Davey; Spector, Tim D; Illig, Thomas; de Faire, Ulf; Hamsten, Anders; Gudnason, Vilmundur; Kivimaki, Mika; Hingorani, Aroon; Keinanen-Kiukaanniemi, Sirkka M; Saaristo, Timo E; Boomsma, Dorret I; Stefansson, Kari; van der Harst, Pim; Dupuis, Josée; Pedersen, Nancy L; Sattar, Naveed; Harris, Tamara B; Cucca, Francesco; Ripatti, Samuli; Salomaa, Veikko; Mohlke, Karen L; Balkau, Beverley; Froguel, Philippe; Pouta, Anneli; Jarvelin, Marjo-Riitta; Wareham, Nicholas J; Bouatia-Naji, Nabila; McCarthy, Mark I; Franks, Paul W; Meigs, James B; Teslovich, Tanya M; Florez, Jose C; Langenberg, Claudia; Ingelsson, Erik; Prokopenko, Inga; Barroso, Inês

2012-09-01

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components

PubMed Central

Wang, Min; Kornblau, Steven M; Coombes, Kevin R

2018-01-01

Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises 2 challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure is competitive with the best methods when considering both accuracy and speed and is the most accurate when the number of objects is small compared with the number of attributes. We applied the method to a proteomics data set from patients with acute myeloid leukemia. Proteins in the apoptosis pathway could be explained using 6 PCs. By clustering the proteins in PC space, we were able to replace the PCs by 6 “biological components,” 3 of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable. PMID:29881252

Integrated Proteomic and Transcriptomic-Based Approaches to Identifying Signature Biomarkers and Pathways for Elucidation of Daoy and UW228 Subtypes.

PubMed

Higdon, Roger; Kala, Jessie; Wilkins, Devan; Yan, Julia Fangfei; Sethi, Manveen K; Lin, Liang; Liu, Siqi; Montague, Elizabeth; Janko, Imre; Choiniere, John; Kolker, Natali; Hancock, William S; Kolker, Eugene; Fanayan, Susan

2017-02-03

Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Patient survival has remained largely the same for the past 20 years, with therapies causing significant health, cognitive, behavioral and developmental complications for those who survive the tumor. In this study, we profiled the total transcriptome and proteome of two established MB cell lines, Daoy and UW228, using high-throughput RNA sequencing (RNA-Seq) and label-free nano-LC-MS/MS-based quantitative proteomics, coupled with advanced pathway analysis. While Daoy has been suggested to belong to the sonic hedgehog (SHH) subtype, the exact UW228 subtype is not yet clearly established. Thus, a goal of this study was to identify protein markers and pathways that would help elucidate their subtype classification. A number of differentially expressed genes and proteins, including a number of adhesion, cytoskeletal and signaling molecules, were observed between the two cell lines. While several cancer-associated genes/proteins exhibited similar expression across the two cell lines, upregulation of a number of signature proteins and enrichment of key components of SHH and WNT signaling pathways were uniquely observed in Daoy and UW228, respectively. The novel information on differentially expressed genes/proteins and enriched pathways provide insights into the biology of MB, which could help elucidate their subtype classification.

Identifying biologically relevant differences between metagenomic communities.

PubMed

Parks, Donovan H; Beiko, Robert G

2010-03-15

Metagenomics is the study of genetic material recovered directly from environmental samples. Taxonomic and functional differences between metagenomic samples can highlight the influence of ecological factors on patterns of microbial life in a wide range of habitats. Statistical hypothesis tests can help us distinguish ecological influences from sampling artifacts, but knowledge of only the P-value from a statistical hypothesis test is insufficient to make inferences about biological relevance. Current reporting practices for pairwise comparative metagenomics are inadequate, and better tools are needed for comparative metagenomic analysis. We have developed a new software package, STAMP, for comparative metagenomics that supports best practices in analysis and reporting. Examination of a pair of iron mine metagenomes demonstrates that deeper biological insights can be gained using statistical techniques available in our software. An analysis of the functional potential of 'Candidatus Accumulibacter phosphatis' in two enhanced biological phosphorus removal metagenomes identified several subsystems that differ between the A.phosphatis stains in these related communities, including phosphate metabolism, secretion and metal transport. Python source code and binaries are freely available from our website at http://kiwi.cs.dal.ca/Software/STAMP CONTACT: beiko@cs.dal.ca Supplementary data are available at Bioinformatics online.

PathScore: a web tool for identifying altered pathways in cancer data.

PubMed

Gaffney, Stephen G; Townsend, Jeffrey P

2016-12-01

PathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects. Web application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at: github.com/sggaffney/pathscore with a GPLv3 license. stephen.gaffney@yale.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Metabolic pathways for the whole community.

PubMed

Hanson, Niels W; Konwar, Kishori M; Hawley, Alyse K; Altman, Tomer; Karp, Peter D; Hallam, Steven J

2014-07-22

A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical levels from genomes to biomes. Holistic understanding of biological systems requires agile software tools that permit comparative analyses across multiple information levels (DNA, RNA, protein, and metabolites) to identify emergent properties, diagnose system states, or predict responses to environmental change. Here we adopt the MetaPathways annotation and analysis pipeline and Pathway Tools to construct environmental pathway/genome databases (ePGDBs) that describe microbial community metabolism using MetaCyc, a highly curated database of metabolic pathways and components covering all domains of life. We evaluate Pathway Tools' performance on three datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series. We define accuracy and sensitivity relationships between read length, coverage and pathway recovery and evaluate the impact of taxonomic pruning on ePGDB construction and interpretation. Resulting ePGDBs provide interactive metabolic maps, predict emergent metabolic pathways associated with biosynthesis and energy production and differentiate between genomic potential and phenotypic expression across defined environmental gradients. This multi-tiered analysis provides the user community with specific operating guidelines, performance metrics and prediction hazards for more reliable ePGDB construction and interpretation. Moreover, it demonstrates the power of Pathway Tools in predicting metabolic interactions in natural and engineered ecosystems.

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

PubMed Central

Scott, Robert A; Lagou, Vasiliki; Welch, Ryan P; Wheeler, Eleanor; Montasser, May E; Luan, Jian’an; Mägi, Reedik; Strawbridge, Rona J; Rehnberg, Emil; Gustafsson, Stefan; Kanoni, Stavroula; Rasmussen-Torvik, Laura J; Yengo, Loïc; Lecoeur, Cecile; Shungin, Dmitry; Sanna, Serena; Sidore, Carlo; Johnson, Paul C D; Jukema, J Wouter; Johnson, Toby; Mahajan, Anubha; Verweij, Niek; Thorleifsson, Gudmar; Hottenga, Jouke-Jan; Shah, Sonia; Smith, Albert V; Sennblad, Bengt; Gieger, Christian; Salo, Perttu; Perola, Markus; Timpson, Nicholas J; Evans, David M; Pourcain, Beate St; Wu, Ying; Andrews, Jeanette S; Hui, Jennie; Bielak, Lawrence F; Zhao, Wei; Horikoshi, Momoko; Navarro, Pau; Isaacs, Aaron; O’Connell, Jeffrey R; Stirrups, Kathleen; Vitart, Veronique; Hayward, Caroline; Esko, Tönu; Mihailov, Evelin; Fraser, Ross M; Fall, Tove; Voight, Benjamin F; Raychaudhuri, Soumya; Chen, Han; Lindgren, Cecilia M; Morris, Andrew P; Rayner, Nigel W; Robertson, Neil; Rybin, Denis; Liu, Ching-Ti; Beckmann, Jacques S; Willems, Sara M; Chines, Peter S; Jackson, Anne U; Kang, Hyun Min; Stringham, Heather M; Song, Kijoung; Tanaka, Toshiko; Peden, John F; Goel, Anuj; Hicks, Andrew A; An, Ping; Müller-Nurasyid, Martina; Franco-Cereceda, Anders; Folkersen, Lasse; Marullo, Letizia; Jansen, Hanneke; Oldehinkel, Albertine J; Bruinenberg, Marcel; Pankow, James S; North, Kari E; Forouhi, Nita G; Loos, Ruth J F; Edkins, Sarah; Varga, Tibor V; Hallmans, Göran; Oksa, Heikki; Antonella, Mulas; Nagaraja, Ramaiah; Trompet, Stella; Ford, Ian; Bakker, Stephan J L; Kong, Augustine; Kumari, Meena; Gigante, Bruna; Herder, Christian; Munroe, Patricia B; Caulfield, Mark; Antti, Jula; Mangino, Massimo; Small, Kerrin; Miljkovic, Iva; Liu, Yongmei; Atalay, Mustafa; Kiess, Wieland; James, Alan L; Rivadeneira, Fernando; Uitterlinden, Andre G; Palmer, Colin N A; Doney, Alex S F; Willemsen, Gonneke; Smit, Johannes H; Campbell, Susan; Polasek, Ozren; Bonnycastle, Lori L; Hercberg, Serge; Dimitriou, Maria; Bolton, Jennifer L; Fowkes, Gerard R; Kovacs, Peter; Lindström, Jaana; Zemunik, Tatijana; Bandinelli, Stefania; Wild, Sarah H; Basart, Hanneke V; Rathmann, Wolfgang; Grallert, Harald; Maerz, Winfried; Kleber, Marcus E; Boehm, Bernhard O; Peters, Annette; Pramstaller, Peter P; Province, Michael A; Borecki, Ingrid B; Hastie, Nicholas D; Rudan, Igor; Campbell, Harry; Watkins, Hugh; Farrall, Martin; Stumvoll, Michael; Ferrucci, Luigi; Waterworth, Dawn M; Bergman, Richard N; Collins, Francis S; Tuomilehto, Jaakko; Watanabe, Richard M; de Geus, Eco J C; Penninx, Brenda W; Hofman, Albert; Oostra, Ben A; Psaty, Bruce M; Vollenweider, Peter; Wilson, James F; Wright, Alan F; Hovingh, G Kees; Metspalu, Andres; Uusitupa, Matti; Magnusson, Patrik K E; Kyvik, Kirsten O; Kaprio, Jaakko; Price, Jackie F; Dedoussis, George V; Deloukas, Panos; Meneton, Pierre; Lind, Lars; Boehnke, Michael; Shuldiner, Alan R; van Duijn, Cornelia M; Morris, Andrew D; Toenjes, Anke; Peyser, Patricia A; Beilby, John P; Körner, Antje; Kuusisto, Johanna; Laakso, Markku; Bornstein, Stefan R; Schwarz, Peter E H; Lakka, Timo A; Rauramaa, Rainer; Adair, Linda S; Smith, George Davey; Spector, Tim D; Illig, Thomas; de Faire, Ulf; Hamsten, Anders; Gudnason, Vilmundur; Kivimaki, Mika; Hingorani, Aroon; Keinanen-Kiukaanniemi, Sirkka M; Saaristo, Timo E; Boomsma, Dorret I; Stefansson, Kari; van der Harst, Pim; Dupuis, Josée; Pedersen, Nancy L; Sattar, Naveed; Harris, Tamara B; Cucca, Francesco; Ripatti, Samuli; Salomaa, Veikko; Mohlke, Karen L; Balkau, Beverley; Froguel, Philippe; Pouta, Anneli; Jarvelin, Marjo-Riitta; Wareham, Nicholas J; Bouatia-Naji, Nabila; McCarthy, Mark I; Franks, Paul W; Meigs, James B; Teslovich, Tanya M; Florez, Jose C; Langenberg, Claudia; Ingelsson, Erik; Prokopenko, Inga; Barroso, Inês

2012-01-01

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have raised the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional follow-up of these newly discovered loci will further improve our understanding of glycemic control. PMID:22885924

Identifying niche-mediated regulatory factors of stem cell phenotypic state: a systems biology approach.

PubMed

Ravichandran, Srikanth; Del Sol, Antonio

2017-02-01

Understanding how the cellular niche controls the stem cell phenotype is often hampered due to the complexity of variegated niche composition, its dynamics, and nonlinear stem cell-niche interactions. Here, we propose a systems biology view that considers stem cell-niche interactions as a many-body problem amenable to simplification by the concept of mean field approximation. This enables approximation of the niche effect on stem cells as a constant field that induces sustained activation/inhibition of specific stem cell signaling pathways in all stem cells within heterogeneous populations exhibiting the same phenotype (niche determinants). This view offers a new basis for the development of single cell-based computational approaches for identifying niche determinants, which has potential applications in regenerative medicine and tissue engineering. © 2017 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

Gene Expression Profiling Identifies Downregulation of the Neurotrophin-MAPK Signaling Pathway in Female Diabetic Peripheral Neuropathy Patients.

PubMed

Luo, Lin; Zhou, Wen-Hua; Cai, Jiang-Jia; Feng, Mei; Zhou, Mi; Hu, Su-Pei; Xu, Jin; Ji, Lin-Dan

2017-01-01

Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). It is not diagnosed or managed properly in the majority of patients because its pathogenesis remains controversial. In this study, human whole genome microarrays identified 2898 and 4493 differentially expressed genes (DEGs) in DM and DPN patients, respectively. A further KEGG pathway analysis indicated that DPN and DM share four pathways, including apoptosis, B cell receptor signaling pathway, endocytosis, and Toll-like receptor signaling pathway. The DEGs identified through comparison of DPN and DM were significantly enriched in MAPK signaling pathway, NOD-like receptor signaling pathway, and neurotrophin signaling pathway, while the "neurotrophin-MAPK signaling pathway" was notably downregulated. Seven DEGs from the neurotrophin-MAPK signaling pathway were validated in additional 78 samples, and the results confirmed the initial microarray findings. These findings demonstrated that downregulation of the neurotrophin-MAPK signaling pathway may be the major mechanism of DPN pathogenesis, thus providing a potential approach for DPN treatment.

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

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

-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

Heritable temperament pathways to early callous-unemotional behaviour.

PubMed

Waller, Rebecca; Trentacosta, Christopher J; Shaw, Daniel S; Neiderhiser, Jenae M; Ganiban, Jody M; Reiss, David; Leve, Leslie D; Hyde, Luke W

2016-12-01

Early callous-unemotional behaviours identify children at risk for antisocial behaviour. Recent work suggests that the high heritability of callous-unemotional behaviours is qualified by interactions with positive parenting. To examine whether heritable temperament dimensions of fearlessness and low affiliative behaviour are associated with early callous-unemotional behaviours and whether parenting moderates these associations. Using an adoption sample (n = 561), we examined pathways from biological mother self-reported fearlessness and affiliative behaviour to child callous-unemotional behaviours via observed child fearlessness and affiliative behaviour, and whether adoptive parent observed positive parenting moderated pathways. Biological mother fearlessness predicted child callous-unemotional behaviours via earlier child fearlessness. Biological mother low affiliative behaviour predicted child callous-unemotional behaviours, although not via child affiliative behaviours. Adoptive mother positive parenting moderated the fearlessness to callous-unemotional behaviour pathway. Heritable fearlessness and low interpersonal affiliation traits contribute to the development of callous-unemotional behaviours. Positive parenting can buffer these risky pathways. © The Royal College of Psychiatrists 2016.

Heritable temperament pathways to early callous–unemotional behaviour

PubMed Central

Waller, Rebecca; Trentacosta, Christopher J.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Ganiban, Jody M.; Reiss, David; Leve, Leslie D.; Hyde, Luke W.

2016-01-01

Background Early callous–unemotional behaviours identify children at risk for antisocial behaviour. Recent work suggests that the high heritability of callous–unemotional behaviours is qualified by interactions with positive parenting. Aims To examine whether heritable temperament dimensions of fearlessness and low affiliative behaviour are associated with early callous–unemotional behaviours and whether parenting moderates these associations. Method Using an adoption sample (n = 561), we examined pathways from biological mother self-reported fearlessness and affiliative behaviour to child callous–unemotional behaviours via observed child fearlessness and affiliative behaviour, and whether adoptive parent observed positive parenting moderated pathways. Results Biological mother fearlessness predicted child callous–unemotional behaviours via earlier child fearlessness. Biological mother low affiliative behaviour predicted child callous–unemotional behaviours, although not via child affiliative behaviours. Adoptive mother positive parenting moderated the fearlessness to callous–unemotional behaviour pathway. Conclusions Heritable fearlessness and low interpersonal affiliation traits contribute to the development of callous–unemotional behaviours. Positive parenting can buffer these risky pathways. PMID:27765772

[Non-nitrification pathway for NH4+ -N removal in pilot-scale drinking water biological processes].

PubMed

Yu, Xin; Ye, Lin; Li, Xu-dong; Zhang, Xiao-jian; Shi, Xu; Liu, Bo; Li, Rui-hua

2008-04-01

The non-nitrification pathway for NH4+ -N removal in pilot-scale drinking water biological treatment processes and its possible mechanism were investigated through calculating N and DO stoichiometric balance. With more than 2 mg/L NH4+ -N in the influent, for the fluidized bed bioreactor (FBBR), the total of NH4+ -N, NO2(-) -N, NO3(-) -N in the influent was 0.91 mg/L higher than that in the effluent, and for the biofilter, its DO consumption was 2.90 mg/L less than the stoichiometric amount. The results suggested that nitrogen loss occurred in both reactors and a part of NH4+ -N was removed through non-nitrification pathway. Because the utilization of phosphorus and organic matters was independent of nitrogen loss, the assimilation and denitrification could be excluded from the possible mechanisms. Because the very low C/N in the influent and the accumulation of NO2(-) -N in the reactors were similar with the wastewater biological processes, the "autotrophic removal of nitrogen" was regarded as the most probable non-nitrification pathway. In this mechanism, the couple of short-cut nitrification and ANAMMOX (or OLAND) leading to the transformation of NH4+ -N and NO2(-) -N into gaseous N2 was responsible for the nitrogen loss in drinking water biological processes.

Adverse Outcome Pathway (AOP) Network Development for ...

EPA Pesticide Factsheets

Adverse outcome pathways (AOPs) are descriptive biological sequences that start from a molecular initiating event (MIE) and end with an adverse health outcome. AOPs provide biological context for high throughput chemical testing and further prioritize environmental health risk research. According to the Organization for Economic Co-operation and Development guidelines, AOPs are pathways with one MIE anchored to an adverse outcome (AO) by key events (KEs) and key event relationships (KERs). However, this approach does not always capture the cumulative impacts of multiple MIEs on the AO. For example, hepatic lipid flux due to chemical-induced toxicity initiates from multiple ligand-activated receptors and signaling pathways that cascade across biology to converge upon a common fatty liver (FL, also known as steatosis) outcome. To capture this complexity, a top-down strategy was used to develop a FL AOP network (AOPnet). Literature was queried based on the terms steatosis, fatty liver, cirrhosis, and hepatocellular carcinoma. Search results were analyzed for physiological and pathophysiological organ level, cellular and molecular processes, as well as pathway intermediates, to identify potential KEs and MIEs that are key for hepatic lipid metabolism, maintenance, and dysregulation. The analysis identified four apical KE nodes (hepatic fatty acid uptake, de novo fatty acid and lipid synthesis, fatty acid oxidation, and lipid efflux) juxtaposed to the FL AO. The apic

Phosphoproteomic Analysis Identifies Signaling Pathways Regulated by Curcumin in Human Colon Cancer Cells.

PubMed

Sato, Tatsuhiro; Higuchi, Yutaka; Shibagaki, Yoshio; Hattori, Seisuke

2017-09-01

Curcumin, a major polyphenol of the spice turmeric, acts as a potent chemopreventive and chemotherapeutic agent in several cancer types, including colon cancer. Although various proteins have been shown to be affected by curcumin, how curcumin exerts its anticancer activity is not fully understood. Phosphoproteomic analyses were performed using SW480 and SW620 human colon cancer cells to identify curcumin-affected signaling pathways. Curcumin inhibited the growth of the two cell lines in a dose-dependent manner. Thirty-nine curcumin-regulated phosphoproteins were identified, five of which are involved in cancer signaling pathways. Detailed analyses revealed that the mTORC1 and p53 signaling pathways are main targets of curcumin. Our results provide insight into the molecular mechanisms of the anticancer activities of curcumin and future molecular targets for its clinical application. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Biased and unbiased strategies to identify biologically active small molecules.

PubMed

Abet, Valentina; Mariani, Angelica; Truscott, Fiona R; Britton, Sébastien; Rodriguez, Raphaël

2014-08-15

Small molecules are central players in chemical biology studies. They promote the perturbation of cellular processes underlying diseases and enable the identification of biological targets that can be validated for therapeutic intervention. Small molecules have been shown to accurately tune a single function of pluripotent proteins in a reversible manner with exceptional temporal resolution. The identification of molecular probes and drugs remains a worthy challenge that can be addressed by the use of biased and unbiased strategies. Hypothesis-driven methodologies employs a known biological target to synthesize complementary hits while discovery-driven strategies offer the additional means of identifying previously unanticipated biological targets. This review article provides a general overview of recent synthetic frameworks that gave rise to an impressive arsenal of biologically active small molecules with unprecedented cellular mechanisms. Copyright © 2014. Published by Elsevier Ltd.

Gene Expression Profiling Identifies Downregulation of the Neurotrophin-MAPK Signaling Pathway in Female Diabetic Peripheral Neuropathy Patients

PubMed Central

Luo, Lin; Zhou, Wen-Hua; Cai, Jiang-Jia; Feng, Mei; Zhou, Mi; Hu, Su-Pei

2017-01-01

Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). It is not diagnosed or managed properly in the majority of patients because its pathogenesis remains controversial. In this study, human whole genome microarrays identified 2898 and 4493 differentially expressed genes (DEGs) in DM and DPN patients, respectively. A further KEGG pathway analysis indicated that DPN and DM share four pathways, including apoptosis, B cell receptor signaling pathway, endocytosis, and Toll-like receptor signaling pathway. The DEGs identified through comparison of DPN and DM were significantly enriched in MAPK signaling pathway, NOD-like receptor signaling pathway, and neurotrophin signaling pathway, while the “neurotrophin-MAPK signaling pathway” was notably downregulated. Seven DEGs from the neurotrophin-MAPK signaling pathway were validated in additional 78 samples, and the results confirmed the initial microarray findings. These findings demonstrated that downregulation of the neurotrophin-MAPK signaling pathway may be the major mechanism of DPN pathogenesis, thus providing a potential approach for DPN treatment. PMID:28900628

Using Ambystoma mexicanum (Mexican Axolotl) Embryos, Chemical Genetics, and Microarray Analysis to Identify Signaling Pathways Associated with Tissue Regeneration

PubMed Central

Ponomareva, Larissa V.; Athippozhy, Antony; Thorson, Jon S.; Voss, S. Randal

2015-01-01

Amphibian vertebrates are important models in regenerative biology because they present exceptional regenerative capabilities throughout life. However, it takes considerable effort to rear amphibians to juvenile and adult stages for regeneration studies and the relatively large sizes that frogs and salamanders achieve during development make them difficult to use in chemical screens. Here we introduce a new tail regeneration model using late stage Mexican axolotl embryos. We show that axolotl embryos completely regenerate amputated tails in 7 days before they exhaust their yolk supply and begin to feed. Further, we show that axolotl embryos can be efficiently reared in microtiter plates to achieve moderate throughput screening of soluble chemicals to investigate toxicity and identify molecules that alter regenerative outcome. As proof of principle, we identified integration 1 / wingless (Wnt), transforming growth factor beta (Tgf-β), and fibroblast growth factor (Fgf) pathway antagonists that completely block tail regeneration and additional chemicals that significantly affected tail outgrowth. Furthermore, we used microarray analysis to show that inhibition of Wnt signaling broadly affects transcription of genes associated with Wnt, Fgf, Tgf-β, epidermal growth factor (Egf), Notch, nerve growth factor (Ngf), homeotic gene (Hox), rat sarcoma/mitogen-activated protein kinase (Ras/Mapk), myelocytomatosis viral oncogene (Myc), tumor protein 53 (p53), and retinoic acid (RA) pathways. Punctuated changes in the expression of genes known to regulate vertebrate development were observed; this suggests the tail regeneration transcriptional program is hierarchically structured and temporally ordered. Our study establishes the axolotl as a chemical screening model to investigate signaling pathways associated with tissue regeneration. PMID:26092703

Identifiability and estimation of multiple transmission pathways in cholera and waterborne disease.

PubMed

Eisenberg, Marisa C; Robertson, Suzanne L; Tien, Joseph H

2013-05-07

Cholera and many waterborne diseases exhibit multiple characteristic timescales or pathways of infection, which can be modeled as direct and indirect transmission. A major public health issue for waterborne diseases involves understanding the modes of transmission in order to improve control and prevention strategies. An important epidemiological question is: given data for an outbreak, can we determine the role and relative importance of direct vs. environmental/waterborne routes of transmission? We examine whether parameters for a differential equation model of waterborne disease transmission dynamics can be identified, both in the ideal setting of noise-free data (structural identifiability) and in the more realistic setting in the presence of noise (practical identifiability). We used a differential algebra approach together with several numerical approaches, with a particular emphasis on identifiability of the transmission rates. To examine these issues in a practical public health context, we apply the model to a recent cholera outbreak in Angola (2006). Our results show that the model parameters-including both water and person-to-person transmission routes-are globally structurally identifiable, although they become unidentifiable when the environmental transmission timescale is fast. Even for water dynamics within the identifiable range, when noisy data are considered, only a combination of the water transmission parameters can practically be estimated. This makes the waterborne transmission parameters difficult to estimate, leading to inaccurate estimates of important epidemiological parameters such as the basic reproduction number (R0). However, measurements of pathogen persistence time in environmental water sources or measurements of pathogen concentration in the water can improve model identifiability and allow for more accurate estimation of waterborne transmission pathway parameters as well as R0. Parameter estimates for the Angola outbreak suggest

Hydrograph Separations can Identify Contaminant-Specific Pathways for Conservation Targeting in a Tile-Drained Watershed

USDA-ARS?s Scientific Manuscript database

Water quality issues continue to vex agriculture. Understanding contaminant-specific pathways could help clarify effective water quality management strategies in watersheds. Hypothesis: If conducted at nested scales, hydrograph separation techniques can identify contaminant-specific pathways that co...

Simulation and estimation of gene number in a biological pathway using almost complete saturation mutagenesis screening of haploid mouse cells.

PubMed

Tokunaga, Masahiro; Kokubu, Chikara; Maeda, Yusuke; Sese, Jun; Horie, Kyoji; Sugimoto, Nakaba; Kinoshita, Taroh; Yusa, Kosuke; Takeda, Junji

2014-11-24

Genome-wide saturation mutagenesis and subsequent phenotype-driven screening has been central to a comprehensive understanding of complex biological processes in classical model organisms such as flies, nematodes, and plants. The degree of "saturation" (i.e., the fraction of possible target genes identified) has been shown to be a critical parameter in determining all relevant genes involved in a biological function, without prior knowledge of their products. In mammalian model systems, however, the relatively large scale and labor intensity of experiments have hampered the achievement of actual saturation mutagenesis, especially for recessive traits that require biallelic mutations to manifest detectable phenotypes. By exploiting the recently established haploid mouse embryonic stem cells (ESCs), we present an implementation of almost complete saturation mutagenesis in a mammalian system. The haploid ESCs were mutagenized with the chemical mutagen N-ethyl-N-nitrosourea (ENU) and processed for the screening of mutants defective in various steps of the glycosylphosphatidylinositol-anchor biosynthetic pathway. The resulting 114 independent mutant clones were characterized by a functional complementation assay, and were shown to be defective in any of 20 genes among all 22 known genes essential for this well-characterized pathway. Ten mutants were further validated by whole-exome sequencing. The predominant generation of single-nucleotide substitutions by ENU resulted in a gene mutation rate proportional to the length of the coding sequence, which facilitated the experimental design of saturation mutagenesis screening with the aid of computational simulation. Our study enables mammalian saturation mutagenesis to become a realistic proposition. Computational simulation, combined with a pilot mutagenesis experiment, could serve as a tool for the estimation of the number of genes essential for biological processes such as drug target pathways when a positive selection of

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

Exposure pathways and biological receptors: baseline data for the canyon uranium mine, Coconino County, Arizona

USGS Publications Warehouse

Hinck, Jo E.; Linder, Greg L.; Darrah, Abigail J.; Drost, Charles A.; Duniway, Michael C.; Johnson, Matthew J.; Méndez-Harclerode, Francisca M.; Nowak, Erika M.; Valdez, Ernest W.; van Riper, Charles; Wolff, S.W.

2014-01-01

Recent restrictions on uranium mining within the Grand Canyon watershed have drawn attention to scientific data gaps in evaluating the possible effects of ore extraction to human populations as well as wildlife communities in the area. Tissue contaminant concentrations, one of the most basic data requirements to determine exposure, are not available for biota from any historical or active uranium mines in the region. The Canyon Uranium Mine is under development, providing a unique opportunity to characterize concentrations of uranium and other trace elements, as well as radiation levels in biota, found in the vicinity of the mine before ore extraction begins. Our study objectives were to identify contaminants of potential concern and critical contaminant exposure pathways for ecological receptors; conduct biological surveys to understand the local food web and refine the list of target species (ecological receptors) for contaminant analysis; and collect target species for contaminant analysis prior to the initiation of active mining. Contaminants of potential concern were identified as arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium, thallium, uranium, and zinc for chemical toxicity and uranium and associated radionuclides for radiation. The conceptual exposure model identified ingestion, inhalation, absorption, and dietary transfer (bioaccumulation or bioconcentration) as critical contaminant exposure pathways. The biological survey of plants, invertebrates, amphibians, reptiles, birds, and small mammals is the first to document and provide ecological information on .200 species in and around the mine site; this study also provides critical baseline information about the local food web. Most of the species documented at the mine are common to ponderosa pine Pinus ponderosa and pinyon–juniper Pinus–Juniperus spp. forests in northern Arizona and are not considered to have special conservation status by state or federal agencies; exceptions

Identifying Ant-Mirid Spatial Interactions to Improve Biological Control in Cacao-Based Agroforestry System.

PubMed

Bagny Beilhe, Leïla; Piou, Cyril; Tadu, Zéphirin; Babin, Régis

2018-06-06

The use of ants for biological control of insect pests was the first reported case of conservation biological control. Direct and indirect community interactions between ants and pests lead to differential spatial pattern. We investigated spatial interactions between mirids, the major cocoa pest in West Africa and numerically dominant ant species, using bivariate point pattern analysis to identify potential biological control agents. We assume that potential biological control agents should display negative spatial interactions with mirids considering their niche overlap. The mirid/ant data were collected in complex cacao-based agroforestry systems sampled in three agroecological areas over a forest-savannah gradient in Cameroon. Three species, Crematogaster striatula Emery (Hymenoptera: Formicidae), Crematogaster clariventris Mayr (Hymenoptera: Formicidae), and Oecophylla longinoda Latreille (Hymenoptera: Formicidae) with high predator and aggressive behaviors were identified as dominant and showed negative spatial relationships with mirids. The weaver ant, O. longinoda was identified as the only potential biological control agent, considering its ubiquity in the plots, the similarity in niche requirements, and the spatial segregation with mirids resulting probably from exclusion mechanisms. Combining bivariate point pattern analysis to good knowledge of insect ecology was an effective method to identify a potentially good biological control agent.

Identifying biological concepts from a protein-related corpus with a probabilistic topic model

PubMed Central

Zheng, Bin; McLean, David C; Lu, Xinghua

2006-01-01

Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text. PMID:16466569

PathwayAccess: CellDesigner plugins for pathway databases.

PubMed

Van Hemert, John L; Dickerson, Julie A

2010-09-15

CellDesigner provides a user-friendly interface for graphical biochemical pathway description. Many pathway databases are not directly exportable to CellDesigner models. PathwayAccess is an extensible suite of CellDesigner plugins, which connect CellDesigner directly to pathway databases using respective Java application programming interfaces. The process is streamlined for creating new PathwayAccess plugins for specific pathway databases. Three PathwayAccess plugins, MetNetAccess, BioCycAccess and ReactomeAccess, directly connect CellDesigner to the pathway databases MetNetDB, BioCyc and Reactome. PathwayAccess plugins enable CellDesigner users to expose pathway data to analytical CellDesigner functions, curate their pathway databases and visually integrate pathway data from different databases using standard Systems Biology Markup Language and Systems Biology Graphical Notation. Implemented in Java, PathwayAccess plugins run with CellDesigner version 4.0.1 and were tested on Ubuntu Linux, Windows XP and 7, and MacOSX. Source code, binaries, documentation and video walkthroughs are freely available at http://vrac.iastate.edu/~jlv.

Microbial production of natural and non-natural flavonoids: Pathway engineering, directed evolution and systems/synthetic biology.

PubMed

Pandey, Ramesh Prasad; Parajuli, Prakash; Koffas, Mattheos A G; Sohng, Jae Kyung

2016-01-01

In this review, we address recent advances made in pathway engineering, directed evolution, and systems/synthetic biology approaches employed in the production and modification of flavonoids from microbial cells. The review is divided into two major parts. In the first, various metabolic engineering and system/synthetic biology approaches used for production of flavonoids and derivatives are discussed broadly. All the manipulations/engineering accomplished on the microorganisms since 2000 are described in detail along with the biosynthetic pathway enzymes, their sources, structures of the compounds, and yield of each product. In the second part of the review, post-modifications of flavonoids by four major reactions, namely glycosylations, methylations, hydroxylations and prenylations using recombinant strains are described. Copyright © 2016 Elsevier Inc. All rights reserved.

Genetic Insights Into ADHD Biology.

PubMed

Hayman, Victoria; Fernandez, Thomas V

2018-01-01

ADHD is a neurobiological disorder with a large worldwide prevalence causing significant impairment in children, adolescents, and adults. While there is general agreement about genetic contributions toward the disorder, progress in leveraging genetics to learn more about the biology and risk factors for ADHD has been limited. In this perspective, we identified 105 genes from the literature showing at least nominal statistical significance in association with ADHD. We analyzed these genes for enrichment in biological pathways and in known interacting biological networks. We also analyzed the expression patterns of candidate genes across brain regions and across periods of human development. From our analysis, we identify 14 genes that cluster within an interactive gene network, with enrichment in nitric oxide synthase and alpha-1 adrenergic pathways. Furthermore, these genes show enrichment for expression in the cerebellum during childhood through young adulthood, and in the cortex in adolescence and young adulthood. Gene discovery holds great potential for elucidating the unknown biological underpinnings of ADHD. Genome-wide sequencing efforts are underway and are likely to provide important insights that can be leveraged for new treatments and interventions.

Systems Biology and Birth Defects Prevention: Blockade of the Glucocorticoid Receptor Prevents Arsenic-Induced Birth Defects

PubMed Central

Ahir, Bhavesh K.; Sanders, Alison P.; Rager, Julia E.

2013-01-01

Background: The biological mechanisms by which environmental metals are associated with birth defects are largely unknown. Systems biology–based approaches may help to identify key pathways that mediate metal-induced birth defects as well as potential targets for prevention. Objectives: First, we applied a novel computational approach to identify a prioritized biological pathway that associates metals with birth defects. Second, in a laboratory setting, we sought to determine whether inhibition of the identified pathway prevents developmental defects. Methods: Seven environmental metals were selected for inclusion in the computational analysis: arsenic, cadmium, chromium, lead, mercury, nickel, and selenium. We used an in silico strategy to predict genes and pathways associated with both metal exposure and developmental defects. The most significant pathway was identified and tested using an in ovo whole chick embryo culture assay. We further evaluated the role of the pathway as a mediator of metal-induced toxicity using the in vitro midbrain micromass culture assay. Results: The glucocorticoid receptor pathway was computationally predicted to be a key mediator of multiple metal-induced birth defects. In the chick embryo model, structural malformations induced by inorganic arsenic (iAs) were prevented when signaling of the glucocorticoid receptor pathway was inhibited. Further, glucocorticoid receptor inhibition demonstrated partial to complete protection from both iAs- and cadmium-induced neurodevelopmental toxicity in vitro. Conclusions: Our findings highlight a novel approach to computationally identify a targeted biological pathway for examining birth defects prevention. PMID:23458687

LncSubpathway: a novel approach for identifying dysfunctional subpathways associated with risk lncRNAs by integrating lncRNA and mRNA expression profiles and pathway topologies.

PubMed

Xu, Yanjun; Li, Feng; Wu, Tan; Xu, Yingqi; Yang, Haixiu; Dong, Qun; Zheng, Meiyu; Shang, Desi; Zhang, Chunlong; Zhang, Yunpeng; Li, Xia

2017-02-28

Long non-coding RNAs (lncRNAs) play important roles in various biological processes, including the development of many diseases. Pathway analysis is a valuable aid for understanding the cellular functions of these transcripts. We have developed and characterized LncSubpathway, a novel method that integrates lncRNA and protein coding gene (PCG) expression with interactome data to identify disease risk subpathways that functionally associated with risk lncRNAs. LncSubpathway identifies the most relevance regions which are related with risk lncRNA set and implicated with study conditions through simultaneously considering the dysregulation extent of lncRNAs, PCGs and their correlations. Simulation studies demonstrated that the sensitivity and false positive rates of LncSubpathway were within acceptable ranges, and that LncSubpathway could accurately identify dysregulated regions that related with disease risk lncRNAs within pathways. When LncSubpathway was applied to colorectal carcinoma and breast cancer subtype datasets, it identified cancer type- and breast cancer subtype-related meaningful subpathways. Further, analysis of its robustness and reproducibility indicated that LncSubpathway was a reliable means of identifying subpathways that functionally associated with lncRNAs. LncSubpathway is freely available at http://www.bio-bigdata.com/lncSubpathway/.

LncSubpathway: a novel approach for identifying dysfunctional subpathways associated with risk lncRNAs by integrating lncRNA and mRNA expression profiles and pathway topologies

PubMed Central

Wu, Tan; Xu, Yingqi; Yang, Haixiu; Dong, Qun; Zheng, Meiyu; Shang, Desi; Zhang, Chunlong; Zhang, Yunpeng; Li, Xia

2017-01-01

Long non-coding RNAs (lncRNAs) play important roles in various biological processes, including the development of many diseases. Pathway analysis is a valuable aid for understanding the cellular functions of these transcripts. We have developed and characterized LncSubpathway, a novel method that integrates lncRNA and protein coding gene (PCG) expression with interactome data to identify disease risk subpathways that functionally associated with risk lncRNAs. LncSubpathway identifies the most relevance regions which are related with risk lncRNA set and implicated with study conditions through simultaneously considering the dysregulation extent of lncRNAs, PCGs and their correlations. Simulation studies demonstrated that the sensitivity and false positive rates of LncSubpathway were within acceptable ranges, and that LncSubpathway could accurately identify dysregulated regions that related with disease risk lncRNAs within pathways. When LncSubpathway was applied to colorectal carcinoma and breast cancer subtype datasets, it identified cancer type- and breast cancer subtype-related meaningful subpathways. Further, analysis of its robustness and reproducibility indicated that LncSubpathway was a reliable means of identifying subpathways that functionally associated with lncRNAs. LncSubpathway is freely available at http://www.bio-bigdata.com/lncSubpathway/. PMID:28152521

Quantitative Proteomics Identifies Activation of Hallmark Pathways of Cancer in Patient Melanoma.

PubMed

Byrum, Stephanie D; Larson, Signe K; Avaritt, Nathan L; Moreland, Linley E; Mackintosh, Samuel G; Cheung, Wang L; Tackett, Alan J

2013-03-01

Molecular pathways regulating melanoma initiation and progression are potential targets of therapeutic development for this aggressive cancer. Identification and molecular analysis of these pathways in patients has been primarily restricted to targeted studies on individual proteins. Here, we report the most comprehensive analysis of formalin-fixed paraffin-embedded human melanoma tissues using quantitative proteomics. From 61 patient samples, we identified 171 proteins varying in abundance among benign nevi, primary melanoma, and metastatic melanoma. Seventy-three percent of these proteins were validated by immunohistochemistry staining of malignant melanoma tissues from the Human Protein Atlas database. Our results reveal that molecular pathways involved with tumor cell proliferation, motility, and apoptosis are mis-regulated in melanoma. These data provide the most comprehensive proteome resource on patient melanoma and reveal insight into the molecular mechanisms driving melanoma progression.

The HEART Pathway randomized trial: identifying emergency department patients with acute chest pain for early discharge.

PubMed

Mahler, Simon A; Riley, Robert F; Hiestand, Brian C; Russell, Gregory B; Hoekstra, James W; Lefebvre, Cedric W; Nicks, Bret A; Cline, David M; Askew, Kim L; Elliott, Stephanie B; Herrington, David M; Burke, Gregory L; Miller, Chadwick D

2015-03-01

The HEART Pathway is a decision aid designed to identify emergency department patients with acute chest pain for early discharge. No randomized trials have compared the HEART Pathway with usual care. Adult emergency department patients with symptoms related to acute coronary syndrome without ST-elevation on ECG (n=282) were randomized to the HEART Pathway or usual care. In the HEART Pathway arm, emergency department providers used the HEART score, a validated decision aid, and troponin measures at 0 and 3 hours to identify patients for early discharge. Usual care was based on American College of Cardiology/American Heart Association guidelines. The primary outcome, objective cardiac testing (stress testing or angiography), and secondary outcomes, index length of stay, early discharge, and major adverse cardiac events (death, myocardial infarction, or coronary revascularization), were assessed at 30 days by phone interview and record review. Participants had a mean age of 53 years, 16% had previous myocardial infarction, and 6% (95% confidence interval, 3.6%-9.5%) had major adverse cardiac events within 30 days of randomization. Compared with usual care, use of the HEART Pathway decreased objective cardiac testing at 30 days by 12.1% (68.8% versus 56.7%; P=0.048) and length of stay by 12 hours (9.9 versus 21.9 hours; P=0.013) and increased early discharges by 21.3% (39.7% versus 18.4%; P<0.001). No patients identified for early discharge had major adverse cardiac events within 30 days. The HEART Pathway reduces objective cardiac testing during 30 days, shortens length of stay, and increases early discharges. These important efficiency gains occurred without any patients identified for early discharge suffering MACE at 30 days. URL: http://www.clinicaltrials.gov. Unique Identifier: NCT01665521. © 2015 American Heart Association, Inc.

Associations of genetic risk scores based on adult adiposity pathways with childhood growth and adiposity measures.

PubMed

Monnereau, Claire; Vogelezang, Suzanne; Kruithof, Claudia J; Jaddoe, Vincent W V; Felix, Janine F

2016-08-18

Results from genome-wide association studies (GWAS) identified many loci and biological pathways that influence adult body mass index (BMI). We aimed to identify if biological pathways related to adult BMI also affect infant growth and childhood adiposity measures. We used data from a population-based prospective cohort study among 3,975 children with a mean age of 6 years. Genetic risk scores were constructed based on the 97 SNPs associated with adult BMI previously identified with GWAS and on 28 BMI related biological pathways based on subsets of these 97 SNPs. Outcomes were infant peak weight velocity, BMI at adiposity peak and age at adiposity peak, and childhood BMI, total fat mass percentage, android/gynoid fat ratio, and preperitoneal fat area. Analyses were performed using linear regression models. A higher overall adult BMI risk score was associated with infant BMI at adiposity peak and childhood BMI, total fat mass, android/gynoid fat ratio, and preperitoneal fat area (all p-values < 0.05). Analyses focused on specific biological pathways showed that the membrane proteins genetic risk score was associated with infant peak weight velocity, and the genetic risk scores related to neuronal developmental processes, hypothalamic processes, cyclicAMP, WNT-signaling, membrane proteins, monogenic obesity and/or energy homeostasis, glucose homeostasis, cell cycle, and muscle biology pathways were associated with childhood adiposity measures (all p-values <0.05). None of the pathways were associated with childhood preperitoneal fat area. A genetic risk score based on 97 SNPs related to adult BMI was associated with peak weight velocity during infancy and general and abdominal fat measurements at the age of 6 years. Risk scores based on genetic variants linked to specific biological pathways, including central nervous system and hypothalamic processes, influence body fat development from early life onwards.

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

A Western blot-based investigation of the yeast secretory pathway designed for an intermediate-level undergraduate cell biology laboratory.

PubMed

Hood-Degrenier, Jennifer K

2008-01-01

The movement of newly synthesized proteins through the endomembrane system of eukaryotic cells, often referred to generally as the secretory pathway, is a topic covered in most intermediate-level undergraduate cell biology courses. An article previously published in this journal described a laboratory exercise in which yeast mutants defective in two distinct steps of protein secretion were differentiated using a genetic reporter designed specifically to identify defects in the first step of the pathway, the insertion of proteins into the endoplasmic reticulum (Vallen, 2002). We have developed two versions of a Western blotting assay that serves as a second way of distinguishing the two secretory mutants, which we pair with the genetic assay in a 3-wk laboratory module. A quiz administered before and after students participated in the lab activities revealed significant postlab gains in their understanding of the secretory pathway and experimental techniques used to study it. A second survey administered at the end of the lab module assessed student perceptions of the efficacy of the lab activities; the results of this survey indicated that the experiments were successful in meeting a set of educational goals defined by the instructor.

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...

Using Ambystoma mexicanum (Mexican axolotl) embryos, chemical genetics, and microarray analysis to identify signaling pathways associated with tissue regeneration.

PubMed

Ponomareva, Larissa V; Athippozhy, Antony; Thorson, Jon S; Voss, S Randal

2015-12-01

Amphibian vertebrates are important models in regenerative biology because they present exceptional regenerative capabilities throughout life. However, it takes considerable effort to rear amphibians to juvenile and adult stages for regeneration studies, and the relatively large sizes that frogs and salamanders achieve during development make them difficult to use in chemical screens. Here, we introduce a new tail regeneration model using late stage Mexican axolotl embryos. We show that axolotl embryos completely regenerate amputated tails in 7days before they exhaust their yolk supply and begin to feed. Further, we show that axolotl embryos can be efficiently reared in microtiter plates to achieve moderate throughput screening of soluble chemicals to investigate toxicity and identify molecules that alter regenerative outcome. As proof of principle, we identified integration 1 / wingless (Wnt), transforming growth factor beta (Tgf-β), and fibroblast growth factor (Fgf) pathway antagonists that completely block tail regeneration and additional chemicals that significantly affected tail outgrowth. Furthermore, we used microarray analysis to show that inhibition of Wnt signaling broadly affects transcription of genes associated with Wnt, Fgf, Tgf-β, epidermal growth factor (Egf), Notch, nerve growth factor (Ngf), homeotic gene (Hox), rat sarcoma/mitogen-activated protein kinase (Ras/Mapk), myelocytomatosis viral oncogene (Myc), tumor protein 53 (p53), and retinoic acid (RA) pathways. Punctuated changes in the expression of genes known to regulate vertebrate development were observed; this suggests the tail regeneration transcriptional program is hierarchically structured and temporally ordered. Our study establishes the axolotl as a chemical screening model to investigate signaling pathways associated with tissue regeneration. Copyright © 2015 Elsevier Inc. All rights reserved.

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation

PubMed Central

2017-01-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability. PMID:29186132

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

PubMed

Villaverde, Alejandro F; Banga, Julio R

2017-11-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability.

Applicability of a high-throughput shotgun plasma protein screening approach in understanding maternal biological pathways relevant to infant birth weight outcome.

PubMed

Kumarathasan, P; Vincent, R; Das, D; Mohottalage, S; Blais, E; Blank, K; Karthikeyan, S; Vuong, N Q; Arbuckle, T E; Fraser, W D

2014-04-04

There are reports linking maternal nutritional status, smoking and environmental chemical exposures to adverse pregnancy outcomes. However, biological bases for association between some of these factors and birth outcomes are yet to be established. The objective of this preliminary work is to test the capability of a new high-throughput shotgun plasma proteomic screening in identifying maternal changes relevant to pregnancy outcome. A subset of third trimester plasma samples (N=12) associated with normal and low-birth weight infants were fractionated, tryptic-digested and analyzed for global proteomic changes using a MALDI-TOF-TOF-MS methodology. Mass spectral data were mined for candidate biomarkers using bioinformatic and statistical tools. Maternal plasma profiles of cytokines (e.g. IL8, TNF-α), chemokines (e.g. MCP-1) and cardiovascular endpoints (e.g. ET-1, MMP-9) were analyzed by a targeted approach using multiplex protein array and HPLC-Fluorescence methods. Target and global plasma proteomic markers were used to identify protein interaction networks and maternal biological pathways relevant to low infant birth weight. Our results exhibited the potential to discriminate specific maternal physiologies relevant to risk of adverse birth outcomes. This proteomic approach can be valuable in understanding the impacts of maternal factors such as environmental contaminant exposures and nutrition on birth outcomes in future work. We demonstrate here the fitness of mass spectrometry-based shot-gun proteomics for surveillance of biological changes in mothers, and for adverse pathway analysis in combination with target biomarker information. This approach has potential for enabling early detection of mothers at risk for low infant birth weight and preterm birth, and thus early intervention for mitigation and prevention of adverse pregnancy outcomes. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes? Copyright

Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia.

PubMed

van Uitert, Miranda; Moerland, Perry D; Enquobahrie, Daniel A; Laivuori, Hannele; van der Post, Joris A M; Ris-Stalpers, Carrie; Afink, Gijs B

2015-01-01

Studies using the placental transcriptome to identify key molecules relevant for preeclampsia are hampered by a relatively small sample size. In addition, they use a variety of bioinformatics and statistical methods, making comparison of findings challenging. To generate a more robust preeclampsia gene expression signature, we performed a meta-analysis on the original data of 11 placenta RNA microarray experiments, representing 139 normotensive and 116 preeclamptic pregnancies. Microarray data were pre-processed and analyzed using standardized bioinformatics and statistical procedures and the effect sizes were combined using an inverse-variance random-effects model. Interactions between genes in the resulting gene expression signature were identified by pathway analysis (Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, Graphite) and protein-protein associations (STRING). This approach has resulted in a comprehensive list of differentially expressed genes that led to a 388-gene meta-signature of preeclamptic placenta. Pathway analysis highlights the involvement of the previously identified hypoxia/HIF1A pathway in the establishment of the preeclamptic gene expression profile, while analysis of protein interaction networks indicates CREBBP/EP300 as a novel element central to the preeclamptic placental transcriptome. In addition, there is an apparent high incidence of preeclampsia in women carrying a child with a mutation in CREBBP/EP300 (Rubinstein-Taybi Syndrome). The 388-gene preeclampsia meta-signature offers a vital starting point for further studies into the relevance of these genes (in particular CREBBP/EP300) and their concomitant pathways as biomarkers or functional molecules in preeclampsia. This will result in a better understanding of the molecular basis of this disease and opens up the opportunity to develop rational therapies targeting the placental dysfunction causal to preeclampsia.

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.

Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression

PubMed Central

Poole, William; Leinonen, Kalle; Shmulevich, Ilya

2017-01-01

Cancer researchers have long recognized that somatic mutations are not uniformly distributed within genes. However, most approaches for identifying cancer mutations focus on either the entire-gene or single amino-acid level. We have bridged these two methodologies with a multiscale mutation clustering algorithm that identifies variable length mutation clusters in cancer genes. We ran our algorithm on 539 genes using the combined mutation data in 23 cancer types from The Cancer Genome Atlas (TCGA) and identified 1295 mutation clusters. The resulting mutation clusters cover a wide range of scales and often overlap with many kinds of protein features including structured domains, phosphorylation sites, and known single nucleotide variants. We statistically associated these multiscale clusters with gene expression and drug response data to illuminate the functional and clinical consequences of mutations in our clusters. Interestingly, we find multiple clusters within individual genes that have differential functional associations: these include PTEN, FUBP1, and CDH1. This methodology has potential implications in identifying protein regions for drug targets, understanding the biological underpinnings of cancer, and personalizing cancer treatments. Toward this end, we have made the mutation clusters and the clustering algorithm available to the public. Clusters and pathway associations can be interactively browsed at m2c.systemsbiology.net. The multiscale mutation clustering algorithm is available at https://github.com/IlyaLab/M2C. PMID:28170390

Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression.

PubMed

Poole, William; Leinonen, Kalle; Shmulevich, Ilya; Knijnenburg, Theo A; Bernard, Brady

2017-02-01

Cancer researchers have long recognized that somatic mutations are not uniformly distributed within genes. However, most approaches for identifying cancer mutations focus on either the entire-gene or single amino-acid level. We have bridged these two methodologies with a multiscale mutation clustering algorithm that identifies variable length mutation clusters in cancer genes. We ran our algorithm on 539 genes using the combined mutation data in 23 cancer types from The Cancer Genome Atlas (TCGA) and identified 1295 mutation clusters. The resulting mutation clusters cover a wide range of scales and often overlap with many kinds of protein features including structured domains, phosphorylation sites, and known single nucleotide variants. We statistically associated these multiscale clusters with gene expression and drug response data to illuminate the functional and clinical consequences of mutations in our clusters. Interestingly, we find multiple clusters within individual genes that have differential functional associations: these include PTEN, FUBP1, and CDH1. This methodology has potential implications in identifying protein regions for drug targets, understanding the biological underpinnings of cancer, and personalizing cancer treatments. Toward this end, we have made the mutation clusters and the clustering algorithm available to the public. Clusters and pathway associations can be interactively browsed at m2c.systemsbiology.net. The multiscale mutation clustering algorithm is available at https://github.com/IlyaLab/M2C.

Identification of personalized dysregulated pathways in hepatocellular carcinoma.

PubMed

Li, Hong; Jiang, Xiumei; Zhu, Shengjie; Sui, Lihong

2017-04-01

Hepatocellular carcinoma (HCC) is the most common liver malignancy, and ranks the fifth most prevalent malignant tumors worldwide. In general, HCC are detected until the disease is at an advanced stage and may miss the best chance for treatment. Thus, elucidating the molecular mechanisms is critical to clinical diagnosis and treatment for HCC. The purpose of this study was to identify dysregulated pathways of great potential functional relevance in the progression of HCC. Microarray data of 72 pairs of tumor and matched non-tumor surrounding tissues of HCC were transformed to gene expression data. Differentially expressed genes (DEG) between patients and normal controls were identified using Linear Models for Microarray Analysis. Personalized dysregulated pathways were identified using individualized pathway aberrance score module. 169 differentially expressed genes (DEG) were obtained with |logFC|≥1.5 and P≤0.01. 749 dysregulated pathways were obtained with P≤0.01 in pathway statistics, and there were 93 DEG overlapped in the dysregulated pathways. After performing normal distribution analysis, 302 pathways with the aberrance probability≥0.5 were identified. By ranking pathway with aberrance probability, the top 20 pathways were obtained. Only three DEGs (TUBA1C, TPR, CDC20) were involved in the top 20 pathways. These personalized dysregulated pathways and overlapped genes may give new insights into the underlying biological mechanisms in the progression of HCC. Particular attention can be focused on them for further research. Copyright © 2017 Elsevier GmbH. All rights reserved.

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.

Systems Biology to Support Nanomaterial Grouping.

PubMed

Riebeling, Christian; Jungnickel, Harald; Luch, Andreas; Haase, Andrea

2017-01-01

The assessment of potential health risks of engineered nanomaterials (ENMs) is a challenging task due to the high number and great variety of already existing and newly emerging ENMs. Reliable grouping or categorization of ENMs with respect to hazards could help to facilitate prioritization and decision making for regulatory purposes. The development of grouping criteria, however, requires a broad and comprehensive data basis. A promising platform addressing this challenge is the systems biology approach. The different areas of systems biology, most prominently transcriptomics, proteomics and metabolomics, each of which provide a wealth of data that can be used to reveal novel biomarkers and biological pathways involved in the mode-of-action of ENMs. Combining such data with classical toxicological data would enable a more comprehensive understanding and hence might lead to more powerful and reliable prediction models. Physico-chemical data provide crucial information on the ENMs and need to be integrated, too. Overall statistical analysis should reveal robust grouping and categorization criteria and may ultimately help to identify meaningful biomarkers and biological pathways that sufficiently characterize the corresponding ENM subgroups. This chapter aims to give an overview on the different systems biology technologies and their current applications in the field of nanotoxicology, as well as to identify the existing challenges.

Biologics that inhibit the Th17 pathway and related cytokines to treat inflammatory disorders.

PubMed

Balato, Anna; Scala, Emanuele; Balato, Nicola; Caiazzo, Giuseppina; Di Caprio, Roberta; Monfrecola, Giuseppe; Raimondo, Annunziata; Lembo, Serena; Ayala, Fabio

2017-11-01

Advances in the understanding of TNF-α and IL-17 synergistic functions have recently led to the concept that patients who do not respond or who respond inadequately to TNF-α inhibitors may have IL-17-driven diseases, opening up the way for a new class of therapeutic development: Th17-inhibitors. Areas covered: In this review, the authors discuss the central role that the IL-23/Th17 axis plays in the pathogenesis of several inflammatory diseases, such as psoriasis, highlighting its position as a relevant therapeutic target. In particular, the authors start by giving a brief historical excursus on biologic agent development up until the success of TNF-α inhibitors, and continue with an overview of IL12/23 pathway inhibition. Next, they describe Th17 cell biology, focusing on the role of IL-17 in host defense and in human immune-inflammatory diseases, discussing the use and side effects of IL-17 inhibitors. Expert opinion: The IL-23/Th17 signaling pathway plays a central role in the pathogenesis of several inflammatory diseases, such as psoriasis. Recent data has demonstrated that biologics neutralizing IL-17 (ixekizumab, secukinumab) or its receptor (brodalumab) are highly effective with a positive safety profile in treating moderate to severe psoriasis, offering new treatment possibilities, especially for patients who do not respond adequately to anti-TNF-α therapies.

A Whole-Cell Phenotypic Screening Platform for Identifying Methylerythritol Phosphate Pathway-Selective Inhibitors as Novel Antibacterial Agents

PubMed Central

Johnson, L. Jeffrey

2012-01-01

Isoprenoid biosynthesis is essential for survival of all living organisms. More than 50,000 unique isoprenoids occur naturally, with each constructed from two simple five-carbon precursors: isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). Two pathways for the biosynthesis of IPP and DMAPP are found in nature. Humans exclusively use the mevalonate (MVA) pathway, while most bacteria, including all Gram-negative and many Gram-positive species, use the unrelated methylerythritol phosphate (MEP) pathway. Here we report the development of a novel, whole-cell phenotypic screening platform to identify compounds that selectively inhibit the MEP pathway. Strains of Salmonella enterica serovar Typhimurium were engineered to have separately inducible MEP (native) and MVA (nonnative) pathways. These strains, RMC26 and CT31-7d, were then used to differentiate MVA pathway- and MEP pathway-specific perturbation. Compounds that inhibit MEP pathway-dependent bacterial growth but leave MVA-dependent growth unaffected represent MEP pathway-selective antibacterials. This screening platform offers three significant results. First, the compound is antibacterial and is therefore cell permeant, enabling access to the intracellular target. Second, the compound inhibits one or more MEP pathway enzymes. Third, the MVA pathway is unaffected, suggesting selectivity for targeting the bacterial versus host pathway. The cell lines also display increased sensitivity to two reported MEP pathway-specific inhibitors, further biasing the platform toward inhibitors selective for the MEP pathway. We demonstrate development of a robust, high-throughput screening platform that combines phenotypic and target-based screening that can identify MEP pathway-selective antibacterials simply by monitoring optical density as the readout for cell growth/inhibition. PMID:22777049

Development of a pluripotent stem cell derived neuronal model to identify chemically induced pathway perturbations in relation to neurotoxicity: Effects of CREB pathway inhibition

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

Pistollato, Francesca; Louisse, Jochem; Scelfo, Bibiana

2014-10-15

According to the advocated paradigm shift in toxicology, acquisition of knowledge on the mechanisms underlying the toxicity of chemicals, such as perturbations of biological pathways, is of primary interest. Pluripotent stem cells (PSCs), such as human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs), offer a unique opportunity to derive physiologically relevant human cell types to measure molecular and cellular effects of such pathway modulations. Here we compared the neuronal differentiation propensity of hESCs and hiPSCs with the aim to develop novel hiPSC-based tools for measuring pathway perturbation in relation to molecular and cellular effects in vitro.more » Among other fundamental pathways, also, the cAMP responsive element binding protein (CREB) pathway was activated in our neuronal models and gave us the opportunity to study time-dependent effects elicited by chemical perturbations of the CREB pathway in relation to cellular effects. We show that the inhibition of the CREB pathway, using 2-naphthol-AS-E-phosphate (KG-501), induced an inhibition of neurite outgrowth and synaptogenesis, as well as a decrease of MAP2{sup +} neuronal cells. These data indicate that a CREB pathway inhibition can be related to molecular and cellular effects that may be relevant for neurotoxicity testing, and, thus, qualify the use of our hiPSC-derived neuronal model for studying chemical-induced neurotoxicity resulting from pathway perturbations. - Highlights: • HESCs derived neuronal cells serve as benchmark for iPSC based neuronal toxicity test development. • Comparisons between hESCs and hiPSCs demonstrated variability of the epigenetic state • CREB pathway modulation have been explored in relation to the neurotoxicant exposure KG-501 • hiPSC might be promising tools to translate theoretical AoPs into toxicological in vitro tests.« less

Beacon Editor: Capturing Signal Transduction Pathways Using the Systems Biology Graphical Notation Activity Flow Language.

PubMed

Elmarakeby, Haitham; Arefiyan, Mostafa; Myers, Elijah; Li, Song; Grene, Ruth; Heath, Lenwood S

2017-12-01

The Beacon Editor is a cross-platform desktop application for the creation and modification of signal transduction pathways using the Systems Biology Graphical Notation Activity Flow (SBGN-AF) language. Prompted by biologists' requests for enhancements, the Beacon Editor includes numerous powerful features for the benefit of creation and presentation.

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

A data mining paradigm for identifying key factors in biological processes using gene expression data.

PubMed

Li, Jin; Zheng, Le; Uchiyama, Akihiko; Bin, Lianghua; Mauro, Theodora M; Elias, Peter M; Pawelczyk, Tadeusz; Sakowicz-Burkiewicz, Monika; Trzeciak, Magdalena; Leung, Donald Y M; Morasso, Maria I; Yu, Peng

2018-06-13

A large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain- or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.

Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer’s disease

PubMed Central

Li, Xinzhong; Long, Jintao; He, Taigang; Belshaw, Robert; Scott, James

2015-01-01

Previous studies have evaluated gene expression in Alzheimer’s disease (AD) brains to identify mechanistic processes, but have been limited by the size of the datasets studied. Here we have implemented a novel meta-analysis approach to identify differentially expressed genes (DEGs) in published datasets comprising 450 late onset AD (LOAD) brains and 212 controls. We found 3124 DEGs, many of which were highly correlated with Braak stage and cerebral atrophy. Pathway Analysis revealed the most perturbed pathways to be (a) nitric oxide and reactive oxygen species in macrophages (NOROS), (b) NFkB and (c) mitochondrial dysfunction. NOROS was also up-regulated, and mitochondrial dysfunction down-regulated, in healthy ageing subjects. Upstream regulator analysis predicted the TLR4 ligands, STAT3 and NFKBIA, for activated pathways and RICTOR for mitochondrial genes. Protein-protein interaction network analysis emphasised the role of NFKB; identified a key interaction of CLU with complement; and linked TYROBP, TREM2 and DOK3 to modulation of LPS signalling through TLR4 and to phosphatidylinositol metabolism. We suggest that NEUROD6, ZCCHC17, PPEF1 and MANBAL are potentially implicated in LOAD, with predicted links to calcium signalling and protein mannosylation. Our study demonstrates a highly injurious combination of TLR4-mediated NFKB signalling, NOROS inflammatory pathway activation, and mitochondrial dysfunction in LOAD. PMID:26202100

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

PubMed

Chasman, Daniel I; Fuchsberger, Christian; Pattaro, Cristian; Teumer, Alexander; Böger, Carsten A; Endlich, Karlhans; Olden, Matthias; Chen, Ming-Huei; Tin, Adrienne; Taliun, Daniel; Li, Man; Gao, Xiaoyi; Gorski, Mathias; Yang, Qiong; Hundertmark, Claudia; Foster, Meredith C; O'Seaghdha, Conall M; Glazer, Nicole; Isaacs, Aaron; Liu, Ching-Ti; Smith, Albert V; O'Connell, Jeffrey R; Struchalin, Maksim; Tanaka, Toshiko; Li, Guo; Johnson, Andrew D; Gierman, Hinco J; Feitosa, Mary F; Hwang, Shih-Jen; Atkinson, Elizabeth J; Lohman, Kurt; Cornelis, Marilyn C; Johansson, Asa; Tönjes, Anke; Dehghan, Abbas; Lambert, Jean-Charles; Holliday, Elizabeth G; Sorice, Rossella; Kutalik, Zoltan; Lehtimäki, Terho; Esko, Tõnu; Deshmukh, Harshal; Ulivi, Sheila; Chu, Audrey Y; Murgia, Federico; Trompet, Stella; Imboden, Medea; Coassin, Stefan; Pistis, Giorgio; Harris, Tamara B; Launer, Lenore J; Aspelund, Thor; Eiriksdottir, Gudny; Mitchell, Braxton D; Boerwinkle, Eric; Schmidt, Helena; Cavalieri, Margherita; Rao, Madhumathi; Hu, Frank; Demirkan, Ayse; Oostra, Ben A; de Andrade, Mariza; Turner, Stephen T; Ding, Jingzhong; Andrews, Jeanette S; Freedman, Barry I; Giulianini, Franco; Koenig, Wolfgang; Illig, Thomas; Meisinger, Christa; Gieger, Christian; Zgaga, Lina; Zemunik, Tatijana; Boban, Mladen; Minelli, Cosetta; Wheeler, Heather E; Igl, Wilmar; Zaboli, Ghazal; Wild, Sarah H; Wright, Alan F; Campbell, Harry; Ellinghaus, David; Nöthlings, Ute; Jacobs, Gunnar; Biffar, Reiner; Ernst, Florian; Homuth, Georg; Kroemer, Heyo K; Nauck, Matthias; Stracke, Sylvia; Völker, Uwe; Völzke, Henry; Kovacs, Peter; Stumvoll, Michael; Mägi, Reedik; Hofman, Albert; Uitterlinden, Andre G; Rivadeneira, Fernando; Aulchenko, Yurii S; Polasek, Ozren; Hastie, Nick; Vitart, Veronique; Helmer, Catherine; Wang, Jie Jin; Stengel, Bénédicte; Ruggiero, Daniela; Bergmann, Sven; Kähönen, Mika; Viikari, Jorma; Nikopensius, Tiit; Province, Michael; Ketkar, Shamika; Colhoun, Helen; Doney, Alex; Robino, Antonietta; Krämer, Bernhard K; Portas, Laura; Ford, Ian; Buckley, Brendan M; Adam, Martin; Thun, Gian-Andri; Paulweber, Bernhard; Haun, Margot; Sala, Cinzia; Mitchell, Paul; Ciullo, Marina; Kim, Stuart K; Vollenweider, Peter; Raitakari, Olli; Metspalu, Andres; Palmer, Colin; Gasparini, Paolo; Pirastu, Mario; Jukema, J Wouter; Probst-Hensch, Nicole M; Kronenberg, Florian; Toniolo, Daniela; Gudnason, Vilmundur; Shuldiner, Alan R; Coresh, Josef; Schmidt, Reinhold; Ferrucci, Luigi; Siscovick, David S; van Duijn, Cornelia M; Borecki, Ingrid B; Kardia, Sharon L R; Liu, Yongmei; Curhan, Gary C; Rudan, Igor; Gyllensten, Ulf; Wilson, James F; Franke, Andre; Pramstaller, Peter P; Rettig, Rainer; Prokopenko, Inga; Witteman, Jacqueline; Hayward, Caroline; Ridker, Paul M; Parsa, Afshin; Bochud, Murielle; Heid, Iris M; Kao, W H Linda; Fox, Caroline S; Köttgen, Anna

2012-12-15

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function

PubMed Central

Chasman, Daniel I.; Fuchsberger, Christian; Pattaro, Cristian; Teumer, Alexander; Böger, Carsten A.; Endlich, Karlhans; Olden, Matthias; Chen, Ming-Huei; Tin, Adrienne; Taliun, Daniel; Li, Man; Gao, Xiaoyi; Gorski, Mathias; Yang, Qiong; Hundertmark, Claudia; Foster, Meredith C.; O'Seaghdha, Conall M.; Glazer, Nicole; Isaacs, Aaron; Liu, Ching-Ti; Smith, Albert V.; O'Connell, Jeffrey R.; Struchalin, Maksim; Tanaka, Toshiko; Li, Guo; Johnson, Andrew D.; Gierman, Hinco J.; Feitosa, Mary F.; Hwang, Shih-Jen; Atkinson, Elizabeth J.; Lohman, Kurt; Cornelis, Marilyn C.; Johansson, Åsa; Tönjes, Anke; Dehghan, Abbas; Lambert, Jean-Charles; Holliday, Elizabeth G.; Sorice, Rossella; Kutalik, Zoltan; Lehtimäki, Terho; Esko, Tõnu; Deshmukh, Harshal; Ulivi, Sheila; Chu, Audrey Y.; Murgia, Federico; Trompet, Stella; Imboden, Medea; Coassin, Stefan; Pistis, Giorgio; Harris, Tamara B.; Launer, Lenore J.; Aspelund, Thor; Eiriksdottir, Gudny; Mitchell, Braxton D.; Boerwinkle, Eric; Schmidt, Helena; Cavalieri, Margherita; Rao, Madhumathi; Hu, Frank; Demirkan, Ayse; Oostra, Ben A.; de Andrade, Mariza; Turner, Stephen T.; Ding, Jingzhong; Andrews, Jeanette S.; Freedman, Barry I.; Giulianini, Franco; Koenig, Wolfgang; Illig, Thomas; Meisinger, Christa; Gieger, Christian; Zgaga, Lina; Zemunik, Tatijana; Boban, Mladen; Minelli, Cosetta; Wheeler, Heather E.; Igl, Wilmar; Zaboli, Ghazal; Wild, Sarah H.; Wright, Alan F.; Campbell, Harry; Ellinghaus, David; Nöthlings, Ute; Jacobs, Gunnar; Biffar, Reiner; Ernst, Florian; Homuth, Georg; Kroemer, Heyo K.; Nauck, Matthias; Stracke, Sylvia; Völker, Uwe; Völzke, Henry; Kovacs, Peter; Stumvoll, Michael; Mägi, Reedik; Hofman, Albert; Uitterlinden, Andre G.; Rivadeneira, Fernando; Aulchenko, Yurii S.; Polasek, Ozren; Hastie, Nick; Vitart, Veronique; Helmer, Catherine; Wang, Jie Jin; Stengel, Bénédicte; Ruggiero, Daniela; Bergmann, Sven; Kähönen, Mika; Viikari, Jorma; Nikopensius, Tiit; Province, Michael; Ketkar, Shamika; Colhoun, Helen; Doney, Alex; Robino, Antonietta; Krämer, Bernhard K.; Portas, Laura; Ford, Ian; Buckley, Brendan M.; Adam, Martin; Thun, Gian-Andri; Paulweber, Bernhard; Haun, Margot; Sala, Cinzia; Mitchell, Paul; Ciullo, Marina; Kim, Stuart K.; Vollenweider, Peter; Raitakari, Olli; Metspalu, Andres; Palmer, Colin; Gasparini, Paolo; Pirastu, Mario; Jukema, J. Wouter; Probst-Hensch, Nicole M.; Kronenberg, Florian; Toniolo, Daniela; Gudnason, Vilmundur; Shuldiner, Alan R.; Coresh, Josef; Schmidt, Reinhold; Ferrucci, Luigi; Siscovick, David S.; van Duijn, Cornelia M.; Borecki, Ingrid B.; Kardia, Sharon L.R.; Liu, Yongmei; Curhan, Gary C.; Rudan, Igor; Gyllensten, Ulf; Wilson, James F.; Franke, Andre; Pramstaller, Peter P.; Rettig, Rainer; Prokopenko, Inga; Witteman, Jacqueline; Hayward, Caroline; Ridker, Paul M; Parsa, Afshin; Bochud, Murielle; Heid, Iris M.; Kao, W.H. Linda; Fox, Caroline S.; Köttgen, Anna

2012-01-01

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10−9) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10−4–2.2 × 10−7. Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general. PMID:22962313

Functional annotation of regulatory pathways.

PubMed

Pandey, Jayesh; Koyutürk, Mehmet; Kim, Yohan; Szpankowski, Wojciech; Subramaniam, Shankar; Grama, Ananth

2007-07-01

Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.

Prediction of enzymatic pathways by integrative pathway mapping

PubMed Central

Wichelecki, Daniel J; San Francisco, Brian; Zhao, Suwen; Rodionov, Dmitry A; Vetting, Matthew W; Al-Obaidi, Nawar F; Lin, Henry; O'Meara, Matthew J; Scott, David A; Morris, John H; Russel, Daniel; Almo, Steven C; Osterman, Andrei L

2018-01-01

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology. PMID:29377793

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research.

PubMed

Slenter, Denise N; Kutmon, Martina; Hanspers, Kristina; Riutta, Anders; Windsor, Jacob; Nunes, Nuno; Mélius, Jonathan; Cirillo, Elisa; Coort, Susan L; Digles, Daniela; Ehrhart, Friederike; Giesbertz, Pieter; Kalafati, Marianthi; Martens, Marvin; Miller, Ryan; Nishida, Kozo; Rieswijk, Linda; Waagmeester, Andra; Eijssen, Lars M T; Evelo, Chris T; Pico, Alexander R; Willighagen, Egon L

2018-01-04

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Pathway analysis from lists of microRNAs: common pitfalls and alternative strategy

PubMed Central

Godard, Patrice; van Eyll, Jonathan

2015-01-01

MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods. PMID:25800743

Genomic pathway analysis reveals that EZH2 and HDAC4 represent mutually exclusive epigenetic pathways across human cancers

PubMed Central

2013-01-01

Background Alterations in epigenetic marks, including methylation or acetylation, are common in human cancers. For many epigenetic pathways, however, direct measures of activity are unknown, making their role in various cancers difficult to assess. Gene expression signatures facilitate the examination of patterns of epigenetic pathway activation across and within human cancer types allowing better understanding of the relationships between these pathways. Methods We used Bayesian regression to generate gene expression signatures from normal epithelial cells before and after epigenetic pathway activation. Signatures were applied to datasets from TCGA, GEO, CaArray, ArrayExpress, and the cancer cell line encyclopedia. For TCGA data, signature results were correlated with copy number variation and DNA methylation changes. GSEA was used to identify biologic pathways related to the signatures. Results We developed and validated signatures reflecting downstream effects of enhancer of zeste homolog 2(EZH2), histone deacetylase(HDAC) 1, HDAC4, sirtuin 1(SIRT1), and DNA methyltransferase 2(DNMT2). By applying these signatures to data from cancer cell lines and tumors in large public repositories, we identify those cancers that have the highest and lowest activation of each of these pathways. Highest EZH2 activation is seen in neuroblastoma, hepatocellular carcinoma, small cell lung cancer, and melanoma, while highest HDAC activity is seen in pharyngeal cancer, kidney cancer, and pancreatic cancer. Across all datasets studied, activation of both EZH2 and HDAC4 is significantly underrepresented. Using breast cancer and glioblastoma as examples to examine intrinsic subtypes of particular cancers, EZH2 activation was highest in luminal breast cancers and proneural glioblastomas, while HDAC4 activation was highest in basal breast cancer and mesenchymal glioblastoma. EZH2 and HDAC4 activation are associated with particular chromosome abnormalities: EZH2 activation with

Differentiating Pathway-Specific From Nonspecific Effects in High-Throughput Toxicity Data: A Foundation for Prioritizing Adverse Outcome Pathway Development.

PubMed

Fay, Kellie A; Villeneuve, Daniel L; Swintek, Joe; Edwards, Stephen W; Nelms, Mark D; Blackwell, Brett R; Ankley, Gerald T

2018-06-01

The U.S. Environmental Protection Agency's ToxCast program has screened thousands of chemicals for biological activity, primarily using high-throughput in vitro bioassays. Adverse outcome pathways (AOPs) offer a means to link pathway-specific biological activities with potential apical effects relevant to risk assessors. Thus, efforts are underway to develop AOPs relevant to pathway-specific perturbations detected in ToxCast assays. Previous work identified a "cytotoxic burst" (CTB) phenomenon wherein large numbers of the ToxCast assays begin to respond at or near test chemical concentrations that elicit cytotoxicity, and a statistical approach to defining the bounds of the CTB was developed. To focus AOP development on the molecular targets corresponding to ToxCast assays indicating pathway-specific effects, we conducted a meta-analysis to identify which assays most frequently respond at concentrations below the CTB. A preliminary list of potentially important, target-specific assays was determined by ranking assays by the fraction of chemical hits below the CTB compared with the number of chemicals tested. Additional priority assays were identified using a diagnostic-odds-ratio approach which gives greater ranking to assays with high specificity but low responsivity. Combined, the two prioritization methods identified several novel targets (e.g., peripheral benzodiazepine and progesterone receptors) to prioritize for AOP development, and affirmed the importance of a number of existing AOPs aligned with ToxCast targets (e.g., thyroperoxidase, estrogen receptor, aromatase). The prioritization approaches did not appear to be influenced by inter-assay differences in chemical bioavailability. Furthermore, the outcomes were robust based on a variety of different parameters used to define the CTB.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network

PubMed Central

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C.; Mohanty, Bidyut K.; Gao, Nan; Tang, Jijun; Lawson, Andrew B.; Hannun, Yusuf A.; Zheng, W. Jim

2014-01-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes’ Ontology Fingerprints—a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms’ corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. PMID:25063300

Identifying relevant data for a biological database: handcrafted rules versus machine learning.

PubMed

Sehgal, Aditya Kumar; Das, Sanmay; Noto, Keith; Saier, Milton H; Elkan, Charles

2011-01-01

With well over 1,000 specialized biological databases in use today, the task of automatically identifying novel, relevant data for such databases is increasingly important. In this paper, we describe practical machine learning approaches for identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both the methods compared and the results will be of interest to curators of many specialized databases.

Alternative ground states enable pathway switching in biological electron transfer

DOE PAGES

Abriata, Luciano A.; Alvarez-Paggi, Damian; Ledesma, Gabirela N.; ...

2012-10-10

Electron transfer is the simplest chemical reaction and constitutes the basis of a large variety of biological processes, such as photosynthesis and cellular respiration. Nature has evolved specific proteins and cofactors for these functions. The mechanisms optimizing biological electron transfer have been matter of intense debate, such as the role of the protein milieu between donor and acceptor sites. Here we propose a mechanism regulating long-range electron transfer in proteins. Specifically, we report a spectroscopic, electrochemical, and theoretical study on WT and single-mutant CuA redox centers from Thermus thermophilus, which shows that thermal fluctuations may populate two alternative ground-state electronicmore » wave functions optimized for electron entry and exit, respectively, through two different and nearly perpendicular pathways. In conclusion, these findings suggest a unique role for alternative or “invisible” electronic ground states in directional electron transfer. Moreover, it is shown that this energy gap and, therefore, the equilibrium between ground states can be fine-tuned by minor perturbations, suggesting alternative ways through which protein–protein interactions and membrane potential may optimize and regulate electron–proton energy transduction.« less

MelanomaDB: A Web Tool for Integrative Analysis of Melanoma Genomic Information to Identify Disease-Associated Molecular Pathways

PubMed Central

Trevarton, Alexander J.; Mann, Michael B.; Knapp, Christoph; Araki, Hiromitsu; Wren, Jonathan D.; Stones-Havas, Steven; Black, Michael A.; Print, Cristin G.

2013-01-01

Despite on-going research, metastatic melanoma survival rates remain low and treatment options are limited. Researchers can now access a rapidly growing amount of molecular and clinical information about melanoma. This information is becoming difficult to assemble and interpret due to its dispersed nature, yet as it grows it becomes increasingly valuable for understanding melanoma. Integration of this information into a comprehensive resource to aid rational experimental design and patient stratification is needed. As an initial step in this direction, we have assembled a web-accessible melanoma database, MelanomaDB, which incorporates clinical and molecular data from publically available sources, which will be regularly updated as new information becomes available. This database allows complex links to be drawn between many different aspects of melanoma biology: genetic changes (e.g., mutations) in individual melanomas revealed by DNA sequencing, associations between gene expression and patient survival, data concerning drug targets, biomarkers, druggability, and clinical trials, as well as our own statistical analysis of relationships between molecular pathways and clinical parameters that have been produced using these data sets. The database is freely available at http://genesetdb.auckland.ac.nz/melanomadb/about.html. A subset of the information in the database can also be accessed through a freely available web application in the Illumina genomic cloud computing platform BaseSpace at http://www.biomatters.com/apps/melanoma-profiler-for-research. The MelanomaDB database illustrates dysregulation of specific signaling pathways across 310 exome-sequenced melanomas and in individual tumors and identifies the distribution of somatic variants in melanoma. We suggest that MelanomaDB can provide a context in which to interpret the tumor molecular profiles of individual melanoma patients relative to biological information and available drug therapies. PMID:23875173

Multivariate inference of pathway activity in host immunity and response to therapeutics

PubMed Central

Goel, Gautam; Conway, Kara L.; Jaeger, Martin; Netea, Mihai G.; Xavier, Ramnik J.

2014-01-01

Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene–gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene–environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method. PMID:25147207

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

PubMed Central

Forsberg, Erica M; Huan, Tao; Rinehart, Duane; Benton, H Paul; Warth, Benedikt; Hilmers, Brian; Siuzdak, Gary

2018-01-01

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ~30 min. PMID:29494574

Identifying the missing proteins in human proteome by biological language model.

PubMed

Dong, Qiwen; Wang, Kai; Liu, Xuan

2016-12-23

With the rapid development of high-throughput sequencing technology, the proteomics research becomes a trendy field in the post genomics era. It is necessary to identify all the native-encoding protein sequences for further function and pathway analysis. Toward that end, the Human Proteome Organization lunched the Human Protein Project in 2011. However many proteins are hard to be detected by experiment methods, which becomes one of the bottleneck in Human Proteome Project. In consideration of the complicatedness of detecting these missing proteins by using wet-experiment approach, here we use bioinformatics method to pre-filter the missing proteins. Since there are analogy between the biological sequences and natural language, the n-gram models from Natural Language Processing field has been used to filter the missing proteins. The dataset used in this study contains 616 missing proteins from the "uncertain" category of the neXtProt database. There are 102 proteins deduced by the n-gram model, which have high probability to be native human proteins. We perform a detail analysis on the predicted structure and function of these missing proteins and also compare the high probability proteins with other mass spectrum datasets. The evaluation shows that the results reported here are in good agreement with those obtained by other well-established databases. The analysis shows that 102 proteins may be native gene-coding proteins and some of the missing proteins are membrane or natively disordered proteins which are hard to be detected by experiment methods.

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

Serum Metabolomic Profiling in Acute Alcoholic Hepatitis Identifies Multiple Dysregulated Pathways

PubMed Central

Rachakonda, Vikrant; Gabbert, Charles; Raina, Amit; Bell, Lauren N.; Cooper, Sara; Malik, Shahid; Behari, Jaideep

2014-01-01

Background and Objectives While animal studies have implicated derangements of global energy homeostasis in the pathogenesis of acute alcoholic hepatitis (AAH), the relevance of these findings to the development of human AAH remains unclear. Using global, unbiased serum metabolomics analysis, we sought to characterize alterations in metabolic pathways associated with severe AAH and identify potential biomarkers for disease prognosis. Methods This prospective, case-control study design included 25 patients with severe AAH and 25 ambulatory patients with alcoholic cirrhosis. Serum samples were collected within 24 hours of the index clinical encounter. Global, unbiased metabolomics profiling was performed. Patients were followed for 180 days after enrollment to determine survival. Results Levels of 234 biochemicals were altered in subjects with severe AAH. Random-forest analysis, principal component analysis, and integrated hierarchical clustering methods demonstrated that metabolomics profiles separated the two cohorts with 100% accuracy. Severe AAH was associated with enhanced triglyceride lipolysis, impaired mitochondrial fatty acid beta oxidation, and upregulated omega oxidation. Low levels of multiple lysolipids and related metabolites suggested decreased plasma membrane remodeling in severe AAH. While most measured bile acids were increased in severe AAH, low deoxycholate and glycodeoxycholate levels indicated intestinal dysbiosis. Several changes in substrate utilization for energy homeostasis were identified in severe AAH, including increased glucose consumption by the pentose phosphate pathway, altered tricarboxylic acid (TCA) cycle activity, and enhanced peptide catabolism. Finally, altered levels of small molecules related to glutathione metabolism and antioxidant vitamin depletion were observed in patients with severe AAH. Univariable logistic regression revealed 15 metabolites associated with 180-day survival in severe AAH. Conclusion Severe AAH is

Tracking of Short Distance Transport Pathways in Biological Tissues by Ultra-Small Nanoparticles

NASA Astrophysics Data System (ADS)

Segmehl, Jana S.; Lauria, Alessandro; Keplinger, Tobias; Berg, John K.; Burgert, Ingo

2018-03-01

In this work, ultra-small europium-doped HfO2 nanoparticles were infiltrated into native wood and used as trackers for studying penetrability and diffusion pathways in the hierarchical wood structure. The high electron density, laser induced luminescence, and crystallinity of these particles allowed for a complementary detection of the particles in the cellular tissue. Confocal Raman microscopy and high-resolution synchrotron scanning wide-angle X-ray scattering (WAXS) measurements were used to detect the infiltrated particles in the native wood cell walls. This approach allows for simultaneously obtaining chemical information of the probed biological tissue and the spatial distribution of the integrated particles. The in-depth information about particle distribution in the complex wood structure can be used for revealing transport pathways in plant tissues, but also for gaining better understanding of modification treatments of plant scaffolds aiming at novel functionalized materials.

DAISY: a new software tool to test global identifiability of biological and physiological systems.

PubMed

Bellu, Giuseppina; Saccomani, Maria Pia; Audoly, Stefania; D'Angiò, Leontina

2007-10-01

A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.

DAISY: a new software tool to test global identifiability of biological and physiological systems

PubMed Central

Bellu, Giuseppina; Saccomani, Maria Pia; Audoly, Stefania; D’Angiò, Leontina

2009-01-01

A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/. PMID:17707944

A new screening pathway for identifying asymptomatic patients using dental panoramic radiographs

NASA Astrophysics Data System (ADS)

Hayashi, Tatsuro; Matsumoto, Takuya; Sawagashira, Tsuyoshi; Tagami, Motoki; Katsumata, Akitoshi; Hayashi, Yoshinori; Muramatsu, Chisako; Zhou, Xiangrong; Iida, Yukihiro; Matsuoka, Masato; Katagi, Kiyoji; Fujita, Hiroshi

2012-03-01

To identify asymptomatic patients is the challenging task and the essential first step in diagnosis. Findings of dental panoramic radiographs include not only dental conditions but also radiographic signs that are suggestive of possible systemic diseases such as osteoporosis, arteriosclerosis, and maxillary sinusitis. Detection of such signs on panoramic radiographs has a potential to provide supplemental benefits for patients. However, it is not easy for general dental practitioners to pay careful attention to such signs. We addressed the development of a computer-aided detection (CAD) system that detects radiographic signs of pathology on panoramic images, and the design of the framework of new screening pathway by cooperation of dentists and our CAD system. The performance evaluation of our CAD system showed the sensitivity and specificity in the identification of osteoporotic patients were 92.6 % and 100 %, respectively, and those of the maxillary sinus abnormality were 89.6 % and 73.6 %, respectively. The detection rate of carotid artery calcifications that suggests the need for further medical evaluation was approximately 93.6 % with 4.4 false-positives per image. To validate the utility of the new screening pathway, preliminary clinical trials by using our CAD system were conducted. To date, 223 panoramic images were processed and 4 asymptomatic patients with suspected osteoporosis, 7 asymptomatic patients with suspected calcifications, and 40 asymptomatic patients with suspected maxillary sinusitis were detected in our initial trial. It was suggested that our new screening pathway could be useful to identify asymptomatic patients with systemic diseases.

A Systems Biology Approach To Identify the Combination Effects of Human Herpesvirus 8 Genes on NF-κB Activation▿

PubMed Central

Konrad, Andreas; Wies, Effi; Thurau, Mathias; Marquardt, Gaby; Naschberger, Elisabeth; Hentschel, Sonja; Jochmann, Ramona; Schulz, Thomas F.; Erfle, Holger; Brors, Benedikt; Lausen, Berthold; Neipel, Frank; Stürzl, Michael

2009-01-01

Human herpesvirus 8 (HHV-8) is the etiologic agent of Kaposi's sarcoma and primary effusion lymphoma. Activation of the cellular transcription factor nuclear factor-kappa B (NF-κB) is essential for latent persistence of HHV-8, survival of HHV-8-infected cells, and disease progression. We used reverse-transfected cell microarrays (RTCM) as an unbiased systems biology approach to systematically analyze the effects of HHV-8 genes on the NF-κB signaling pathway. All HHV-8 genes individually (n = 86) and, additionally, all K and latent genes in pairwise combinations (n = 231) were investigated. Statistical analyses of more than 14,000 transfections identified ORF75 as a novel and confirmed K13 as a known HHV-8 activator of NF-κB. K13 and ORF75 showed cooperative NF-κB activation. Small interfering RNA-mediated knockdown of ORF75 expression demonstrated that this gene contributes significantly to NF-κB activation in HHV-8-infected cells. Furthermore, our approach confirmed K10.5 as an NF-κB inhibitor and newly identified K1 as an inhibitor of both K13- and ORF75-mediated NF-κB activation. All results obtained with RTCM were confirmed with classical transfection experiments. Our work describes the first successful application of RTCM for the systematic analysis of pathofunctions of genes of an infectious agent. With this approach, ORF75 and K1 were identified as novel HHV-8 regulatory molecules on the NF-κB signal transduction pathway. The genes identified may be involved in fine-tuning of the balance between latency and lytic replication, since this depends critically on the state of NF-κB activity. PMID:19129458

An Integrated Clinico-transcriptomic Approach Identifies a Central Role of the Heme Degradation Pathway for Septic Complications after Trauma.

PubMed

Rittirsch, Daniel; Schoenborn, Veit; Lindig, Sandro; Wanner, Elisabeth; Sprengel, Kai; Günkel, Sebastian; Blaess, Markus; Schaarschmidt, Barbara; Sailer, Patricia; Märsmann, Sonja; Simmen, Hans-Peter; Cinelli, Paolo; Bauer, Michael; Claus, Ralf A; Wanner, Guido A

2016-12-01

The present study was aimed to identify mechanisms linked to complicated courses and adverse events after severe trauma by a systems biology approach. In severe trauma, overwhelming systemic inflammation can result in additional damage and the development of complications, including sepsis. In a prospective, longitudinal single-center study, RNA samples from circulating leukocytes from patients with multiple injury (injury severity score ≥17 points; n = 81) were analyzed for dynamic changes in gene expression over a period of 21 days by whole-genome screening (discovery set; n = 10 patients; 90 samples) and quantitative RT-PCR (validation set; n = 71 patients, 517 samples). Multivariate correlational analysis of transcripts and clinical parameters was used to identify mechanisms related to sepsis. Transcriptome profiling of the discovery set revealed the strongest changes between patients with either systemic inflammation or sepsis in gene expression of the heme degradation pathway. Using quantitative RT-PCR analyses (validation set), the key components haptoglobin (HP), cluster of differentiation (CD) 163, heme oxygenase-1 (HMOX1), and biliverdin reductase A (BLVRA) showed robust changes following trauma. Upregulation of HP was associated with the severity of systemic inflammation and the development of sepsis. Patients who received allogeneic blood transfusions had a higher incidence of nosocomial infections and sepsis, and the amount of blood transfusion as source of free heme correlated with the expression pattern of HP. These findings indicate that the heme degradation pathway is associated with increased susceptibility to septic complications after trauma, which is indicated by HP expression in particular.

Cancer-related marketing centrality motifs acting as pivot units in the human signaling network and mediating cross-talk between biological pathways.

PubMed

Li, Wan; Chen, Lina; Li, Xia; Jia, Xu; Feng, Chenchen; Zhang, Liangcai; He, Weiming; Lv, Junjie; He, Yuehan; Li, Weiguo; Qu, Xiaoli; Zhou, Yanyan; Shi, Yuchen

2013-12-01

Network motifs in central positions are considered to not only have more in-coming and out-going connections but are also localized in an area where more paths reach the networks. These central motifs have been extensively investigated to determine their consistent functions or associations with specific function categories. However, their functional potentials in the maintenance of cross-talk between different functional communities are unclear. In this paper, we constructed an integrated human signaling network from the Pathway Interaction Database. We identified 39 essential cancer-related motifs in central roles, which we called cancer-related marketing centrality motifs, using combined centrality indices on the system level. Our results demonstrated that these cancer-related marketing centrality motifs were pivotal units in the signaling network, and could mediate cross-talk between 61 biological pathways (25 could be mediated by one motif on average), most of which were cancer-related pathways. Further analysis showed that molecules of most marketing centrality motifs were in the same or adjacent subcellular localizations, such as the motif containing PI3K, PDK1 and AKT1 in the plasma membrane, to mediate signal transduction between 32 cancer-related pathways. Finally, we analyzed the pivotal roles of cancer genes in these marketing centrality motifs in the pathogenesis of cancers, and found that non-cancer genes were potential cancer-related genes.

Measuring job stress among hospital nurses: an attempt to identify biological markers.

PubMed

Kawaguchi, Yoshichika; Toyomasu, Kouji; Yoshida, Noriko; Baba, Kaori; Uemoto, Masaharu; Minota, Shoichi

2007-02-01

The purpose of this study was to identify biological markers corresponding to job stress among hospital nurses. The subjects of this study were 128 nurses working at a university hospital. The NIOSH job stress questionnaire and the Miki Nurse Stressor 35-item Scale measured their job stress levels. The GHQ28 was also used to measure the subjects' general mental health status. Blood analyses for neuroendocrine function and immunity reaction were performed in order to identify biological markers of job stress. Stress is related to the plasma levels of catecholamine, cortisol, adrenocorticotrophic hormone, and natural killer cell activity, therefore these factors were measured accordingly. In consideration to circadian rhythms, blood was collected from the subjects prior to the start of the day shift. The nurses filled out the questionnaires on the day of the blood tests. In order to investigate the correlation between job stress reactions indicated by the questionnaires and the results of the blood tests, we utilized Pearson's correlation coefficient and partial correlation coefficient for which other affected items were controlled. In this study, significant correlations were found between job stress and biological factors; however, the correlations were not strong. Thus, it can be said that the biological markers associated with a specific kind of job stress remain unclear. In the future, rather than implementing a simple cross-sectional study, a longitudinal study including follow-up research will be more effective in establishing biological markers for job stress.

Genome-wide pleiotropy and shared biological pathways for resistance to bovine pathogens

PubMed Central

Zeng, Y.; Yin, T.; Brügemann, K.

2018-01-01

Host genetic architecture is a major factor in resistance to pathogens and parasites. The collection and analysis of sufficient data on both disease resistance and host genetics has, however, been a major obstacle to dissection the genetics of resistance to single or multiple pathogens. A severe challenge in the estimation of heritabilities and genetic correlations from pedigree-based studies has been the confounding effects of the common environment shared among relatives which are difficult to model in pedigree analyses, especially for health traits with low incidence rates. To circumvent this problem we used genome-wide single-nucleotide polymorphism data and implemented the Genomic-Restricted Maximum Likelihood (G-REML) method to estimate the heritabilities and genetic correlations for resistance to 23 different infectious pathogens in calves and cows in populations undergoing natural pathogen challenge. Furthermore, we conducted gene-based analysis and generalized gene-set analysis to understand the biological background of resistance to infectious diseases. The results showed relatively higher heritabilities of resistance in calves than in cows and significant pleiotropy (both positive and negative) among some calf and cow resistance traits. We also found significant pleiotropy between resistance and performance in both calves and cows. Finally, we confirmed the role of the B-lymphocyte pathway as one of the most important biological pathways associated with resistance to all pathogens. These results both illustrate the potential power of these approaches to illuminate the genetics of pathogen resistance in cattle and provide foundational information for future genomic selection aimed at improving the overall production fitness of cattle. PMID:29608619

Pathways to Aging: The Mitochondrion at the Intersection of Biological and Psychosocial Sciences

PubMed Central

Picard, Martin

2011-01-01

Compelling evidence suggests that both biological and psychosocial factors impact the process of aging. However, our understanding of the dynamic interplay among biological and psychosocial factors across the life course is still fragmentary. For example, it needs to be established how the interaction of individual factors (e.g., genetic and epigenetic endowment and personality), behavioral factors (e.g., physical activity, diet, and stress management), and psychosocial experiences (e.g., social support, well-being, socioeconomic status, and marriage) in perinatal, childhood, and adulthood influence health across the aging continuum. This paper aims to outline potential intersection points serving as an interface between biological and psychosocial factors, with an emphasis on the mitochondrion. Mitochondria are cellular organelles which play a critical role in cellular senescence. Both chronic exposure to psychosocial stress and genetic-based mitochondrial dysfunction have strikingly similar biological consequences; both predispose individuals to adverse age-related health disorders and early mortality. Exploring the interactive nature of the factors resulting in pathways to normal healthy aging, as well as those leading to morbidity and early mortality, will continue to enhance our ability to translate research into effective practices that can be implemented throughout the life course to optimise the aging process. PMID:21961065

Pathways to aging: the mitochondrion at the intersection of biological and psychosocial sciences.

PubMed

Picard, Martin

2011-01-01

Compelling evidence suggests that both biological and psychosocial factors impact the process of aging. However, our understanding of the dynamic interplay among biological and psychosocial factors across the life course is still fragmentary. For example, it needs to be established how the interaction of individual factors (e.g., genetic and epigenetic endowment and personality), behavioral factors (e.g., physical activity, diet, and stress management), and psychosocial experiences (e.g., social support, well-being, socioeconomic status, and marriage) in perinatal, childhood, and adulthood influence health across the aging continuum. This paper aims to outline potential intersection points serving as an interface between biological and psychosocial factors, with an emphasis on the mitochondrion. Mitochondria are cellular organelles which play a critical role in cellular senescence. Both chronic exposure to psychosocial stress and genetic-based mitochondrial dysfunction have strikingly similar biological consequences; both predispose individuals to adverse age-related health disorders and early mortality. Exploring the interactive nature of the factors resulting in pathways to normal healthy aging, as well as those leading to morbidity and early mortality, will continue to enhance our ability to translate research into effective practices that can be implemented throughout the life course to optimise the aging process.

A MicroRNA Screen Identifies the Wnt Signaling Pathway as a Regulator of the Interferon Response during Flavivirus Infection

PubMed Central

Smith, Jessica L.; Jeng, Sophia; McWeeney, Shannon K.

2017-01-01

ABSTRACT The impact of mosquito-borne flavivirus infections worldwide is significant, and many critical aspects of these viruses' biology, including virus-host interactions, host cell requirements for replication, and how virus-host interactions impact pathology, remain to be fully understood. The recent reemergence and spread of flaviviruses, including dengue virus (DENV), West Nile virus (WNV), and Zika virus (ZIKV), highlight the importance of performing basic research on this important group of pathogens. MicroRNAs (miRNAs) are small, noncoding RNAs that modulate gene expression posttranscriptionally and have been demonstrated to regulate a broad range of cellular processes. Our research is focused on identifying pro- and antiflaviviral miRNAs as a means of characterizing cellular pathways that support or limit viral replication. We have screened a library of known human miRNA mimics for their effect on the replication of three flaviviruses, DENV, WNV, and Japanese encephalitis virus (JEV), using a high-content immunofluorescence screen. Several families of miRNAs were identified as inhibiting multiple flaviviruses, including the miRNA miR-34, miR-15, and miR-517 families. Members of the miR-34 family, which have been extensively characterized for their ability to repress Wnt/β-catenin signaling, demonstrated strong antiflaviviral effects, and this inhibitory activity extended to other viruses, including ZIKV, alphaviruses, and herpesviruses. Previous research suggested a possible link between the Wnt and type I interferon (IFN) signaling pathways. Therefore, we investigated the role of type I IFN induction in the antiviral effects of the miR-34 family and confirmed that these miRNAs potentiate interferon regulatory factor 3 (IRF3) phosphorylation and translocation to the nucleus, the induction of IFN-responsive genes, and the release of type I IFN from transfected cells. We further demonstrate that the intersection between the Wnt and IFN signaling pathways

A MicroRNA Screen Identifies the Wnt Signaling Pathway as a Regulator of the Interferon Response during Flavivirus Infection.

PubMed

Smith, Jessica L; Jeng, Sophia; McWeeney, Shannon K; Hirsch, Alec J

2017-04-15

The impact of mosquito-borne flavivirus infections worldwide is significant, and many critical aspects of these viruses' biology, including virus-host interactions, host cell requirements for replication, and how virus-host interactions impact pathology, remain to be fully understood. The recent reemergence and spread of flaviviruses, including dengue virus (DENV), West Nile virus (WNV), and Zika virus (ZIKV), highlight the importance of performing basic research on this important group of pathogens. MicroRNAs (miRNAs) are small, noncoding RNAs that modulate gene expression posttranscriptionally and have been demonstrated to regulate a broad range of cellular processes. Our research is focused on identifying pro- and antiflaviviral miRNAs as a means of characterizing cellular pathways that support or limit viral replication. We have screened a library of known human miRNA mimics for their effect on the replication of three flaviviruses, DENV, WNV, and Japanese encephalitis virus (JEV), using a high-content immunofluorescence screen. Several families of miRNAs were identified as inhibiting multiple flaviviruses, including the miRNA miR-34, miR-15, and miR-517 families. Members of the miR-34 family, which have been extensively characterized for their ability to repress Wnt/β-catenin signaling, demonstrated strong antiflaviviral effects, and this inhibitory activity extended to other viruses, including ZIKV, alphaviruses, and herpesviruses. Previous research suggested a possible link between the Wnt and type I interferon (IFN) signaling pathways. Therefore, we investigated the role of type I IFN induction in the antiviral effects of the miR-34 family and confirmed that these miRNAs potentiate interferon regulatory factor 3 (IRF3) phosphorylation and translocation to the nucleus, the induction of IFN-responsive genes, and the release of type I IFN from transfected cells. We further demonstrate that the intersection between the Wnt and IFN signaling pathways occurs at

[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.

Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network.

PubMed

Qin, Tingting; Matmati, Nabil; Tsoi, Lam C; Mohanty, Bidyut K; Gao, Nan; Tang, Jijun; Lawson, Andrew B; Hannun, Yusuf A; Zheng, W Jim

2014-10-01

To enhance our knowledge regarding biological pathway regulation, we took an integrated approach, using the biomedical literature, ontologies, network analyses and experimental investigation to infer novel genes that could modulate biological pathways. We first constructed a novel gene network via a pairwise comparison of all yeast genes' Ontology Fingerprints--a set of Gene Ontology terms overrepresented in the PubMed abstracts linked to a gene along with those terms' corresponding enrichment P-values. The network was further refined using a Bayesian hierarchical model to identify novel genes that could potentially influence the pathway activities. We applied this method to the sphingolipid pathway in yeast and found that many top-ranked genes indeed displayed altered sphingolipid pathway functions, initially measured by their sensitivity to myriocin, an inhibitor of de novo sphingolipid biosynthesis. Further experiments confirmed the modulation of the sphingolipid pathway by one of these genes, PFA4, encoding a palmitoyl transferase. Comparative analysis showed that few of these novel genes could be discovered by other existing methods. Our novel gene network provides a unique and comprehensive resource to study pathway modulations and systems biology in general. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Biological aspects of chondrosarcoma: Leaps and hurdles.

PubMed

Mery, Benoîte; Espenel, Sophie; Guy, Jean-Baptiste; Rancoule, Chloé; Vallard, Alexis; Aloy, Marie-Thérèse; Rodriguez-Lafrasse, Claire; Magné, Nicolas

2018-06-01

Chondrosarcomas are characterized by their chemo- and radioresistance leading to a therapeutic surgical approach which remains the only available treatment with a 10-year survival between 30% and 80% depending on the grade. Non-surgical treatments are under investigation and rely on an accurate biological understanding of drug resistance mechanisms. Novel targeted therapy which represents a new relevant therapeutic approach will open new treatment options by targeting several pathways responsible for processes of proliferation and invasion. Survival pathways such as PI3K, AKT, mTOR and VEGF have been shown to be involved in proliferation of chondrosarcoma cells and antiapoptotic proteins may also play a relevant role. Other proteins such as p53 or COX2 have been identified as potential new targets. This review provides an insight into the biological substantial treatment challenges of CHS and focuses on improving our understanding of CH biology through an overview of major signaling pathways that could represent targets for new therapeutic approaches. Copyright © 2018 Elsevier B.V. All rights reserved.

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

Nitric oxide and nitrous oxide turnover in natural and engineered microbial communities: biological pathways, chemical reactions, and novel technologies

PubMed Central

Schreiber, Frank; Wunderlin, Pascal; Udert, Kai M.; Wells, George F.

2012-01-01

Nitrous oxide (N2O) is an environmentally important atmospheric trace gas because it is an effective greenhouse gas and it leads to ozone depletion through photo-chemical nitric oxide (NO) production in the stratosphere. Mitigating its steady increase in atmospheric concentration requires an understanding of the mechanisms that lead to its formation in natural and engineered microbial communities. N2O is formed biologically from the oxidation of hydroxylamine (NH2OH) or the reduction of nitrite (NO−2) to NO and further to N2O. Our review of the biological pathways for N2O production shows that apparently all organisms and pathways known to be involved in the catabolic branch of microbial N-cycle have the potential to catalyze the reduction of NO−2 to NO and the further reduction of NO to N2O, while N2O formation from NH2OH is only performed by ammonia oxidizing bacteria (AOB). In addition to biological pathways, we review important chemical reactions that can lead to NO and N2O formation due to the reactivity of NO−2, NH2OH, and nitroxyl (HNO). Moreover, biological N2O formation is highly dynamic in response to N-imbalance imposed on a system. Thus, understanding NO formation and capturing the dynamics of NO and N2O build-up are key to understand mechanisms of N2O release. Here, we discuss novel technologies that allow experiments on NO and N2O formation at high temporal resolution, namely NO and N2O microelectrodes and the dynamic analysis of the isotopic signature of N2O with quantum cascade laser absorption spectroscopy (QCLAS). In addition, we introduce other techniques that use the isotopic composition of N2O to distinguish production pathways and findings that were made with emerging molecular techniques in complex environments. Finally, we discuss how a combination of the presented tools might help to address important open questions on pathways and controls of nitrogen flow through complex microbial communities that eventually lead to N2O build

Metabolomics for undergraduates: Identification and pathway assignment of mitochondrial metabolites.

PubMed

Marques, Ana Patrícia; Serralheiro, Maria Luisa; Ferreira, António E N; Freire, Ana Ponces; Cordeiro, Carlos; Silva, Marta Sousa

2016-01-01

Metabolomics is a key discipline in systems biology, together with genomics, transcriptomics, and proteomics. In this omics cascade, the metabolome represents the biochemical products that arise from cellular processes and is often regarded as the final response of a biological system to environmental or genetic changes. The overall screening approach to identify all the metabolites in a given biological system is called metabolic fingerprinting. Using high-resolution and high-mass accuracy mass spectrometry, large metabolome coverage, sensitivity, and specificity can be attained. Although the theoretical concepts of this methodology are usually provided in life-science programs, hands-on laboratory experiments are not usually accessible to undergraduate students. Even if the instruments are available, there are not simple laboratory protocols created specifically for teaching metabolomics. We designed a straightforward hands-on laboratory experiment to introduce students to this methodology, relating it to biochemical knowledge through metabolic pathway mapping of the identified metabolites. This study focuses on mitochondrial metabolomics since mitochondria have a well-known, medium-sized cellular sub-metabolome. These features facilitate both data processing and pathway mapping. In this experiment, students isolate mitochondria from potatoes, extract the metabolites, and analyze them by high-resolution mass spectrometry (using an FT-ICR mass spectrometer). The resulting mass list is submitted to an online program for metabolite identification, and compounds associated with mitochondrial pathways can be highlighted in a metabolic network map. © 2015 The International Union of Biochemistry and Molecular Biology.

The Mucin MUC4 and Its Membrane Partner ErbB2 Regulate Biological Properties of Human CAPAN-2 Pancreatic Cancer Cells via Different Signalling Pathways

PubMed Central

Jonckheere, Nicolas; Skrypek, Nicolas; Merlin, Johann; Dessein, Anne Frédérique; Dumont, Patrick; Leteurtre, Emmanuelle; Harris, Ann; Desseyn, Jean-Luc; Susini, Christiane; Frénois, Frédéric; Van Seuningen, Isabelle

2012-01-01

The mucin MUC4 and its membrane partner the ErbB2 oncogenic receptor are potential interacting partners in human pancreatic tumour development. However, the way they function is still largely unknown. In this work, we aimed to identify the cellular mechanisms and the intracellular signalling pathways under the control of both ErbB2 and MUC4 in a human pancreatic adenocarcinomatous cell line. Using co-immunoprecipitation and GST pull-down, we show that MUC4 and ErbB2 interact in the human pancreatic adenocarcinomatous cell line CAPAN-2 via the EGF domains of MUC4. Stable cell clones were generated in which either MUC4 or ErbB2 were knocked down (KD) by a shRNA approach. Biological properties of these cells were then studied in vitro and in vivo. Our results show that ErbB2-KD cells are more apoptotic and less proliferative (decreased cyclin D1 and increased p27kip1 expression) while migration and invasive properties were not altered. MUC4-KD clones were less proliferative with decreased cyclin D1 expression, G1 cell cycle arrest and altered ErbB2/ErbB3 expression. Their migration properties were reduced whereas invasive properties were increased. Importantly, inhibition of ErbB2 and MUC4 expression did not impair the same signalling pathways (inhibition of MUC4 expression affected the JNK pathway whereas that of ErbB2 altered the MAPK pathway). Finally, ErbB2-KD and MUC4-KD cells showed impaired tumour growth in vivo. Our results show that ErbB2 and MUC4, which interact physically, activate different intracellular signalling pathways to regulate biological properties of CAPAN-2 pancreatic cancer cells. PMID:22393391

The mucin MUC4 and its membrane partner ErbB2 regulate biological properties of human CAPAN-2 pancreatic cancer cells via different signalling pathways.

PubMed

Jonckheere, Nicolas; Skrypek, Nicolas; Merlin, Johann; Dessein, Anne Frédérique; Dumont, Patrick; Leteurtre, Emmanuelle; Harris, Ann; Desseyn, Jean-Luc; Susini, Christiane; Frénois, Frédéric; Van Seuningen, Isabelle

2012-01-01

The mucin MUC4 and its membrane partner the ErbB2 oncogenic receptor are potential interacting partners in human pancreatic tumour development. However, the way they function is still largely unknown. In this work, we aimed to identify the cellular mechanisms and the intracellular signalling pathways under the control of both ErbB2 and MUC4 in a human pancreatic adenocarcinomatous cell line. Using co-immunoprecipitation and GST pull-down, we show that MUC4 and ErbB2 interact in the human pancreatic adenocarcinomatous cell line CAPAN-2 via the EGF domains of MUC4. Stable cell clones were generated in which either MUC4 or ErbB2 were knocked down (KD) by a shRNA approach. Biological properties of these cells were then studied in vitro and in vivo. Our results show that ErbB2-KD cells are more apoptotic and less proliferative (decreased cyclin D1 and increased p27kip1 expression) while migration and invasive properties were not altered. MUC4-KD clones were less proliferative with decreased cyclin D1 expression, G1 cell cycle arrest and altered ErbB2/ErbB3 expression. Their migration properties were reduced whereas invasive properties were increased. Importantly, inhibition of ErbB2 and MUC4 expression did not impair the same signalling pathways (inhibition of MUC4 expression affected the JNK pathway whereas that of ErbB2 altered the MAPK pathway). Finally, ErbB2-KD and MUC4-KD cells showed impaired tumour growth in vivo. Our results show that ErbB2 and MUC4, which interact physically, activate different intracellular signalling pathways to regulate biological properties of CAPAN-2 pancreatic cancer cells.

Integrative biology approach identifies cytokine targeting strategies for psoriasis.

PubMed

Perera, Gayathri K; Ainali, Chrysanthi; Semenova, Ekaterina; Hundhausen, Christian; Barinaga, Guillermo; Kassen, Deepika; Williams, Andrew E; Mirza, Muddassar M; Balazs, Mercedesz; Wang, Xiaoting; Rodriguez, Robert Sanchez; Alendar, Andrej; Barker, Jonathan; Tsoka, Sophia; Ouyang, Wenjun; Nestle, Frank O

2014-02-12

Cytokines are critical checkpoints of inflammation. The treatment of human autoimmune disease has been revolutionized by targeting inflammatory cytokines as key drivers of disease pathogenesis. Despite this, there exist numerous pitfalls when translating preclinical data into the clinic. We developed an integrative biology approach combining human disease transcriptome data sets with clinically relevant in vivo models in an attempt to bridge this translational gap. We chose interleukin-22 (IL-22) as a model cytokine because of its potentially important proinflammatory role in epithelial tissues. Injection of IL-22 into normal human skin grafts produced marked inflammatory skin changes resembling human psoriasis. Injection of anti-IL-22 monoclonal antibody in a human xenotransplant model of psoriasis, developed specifically to test potential therapeutic candidates, efficiently blocked skin inflammation. Bioinformatic analysis integrating both the IL-22 and anti-IL-22 cytokine transcriptomes and mapping them onto a psoriasis disease gene coexpression network identified key cytokine-dependent hub genes. Using knockout mice and small-molecule blockade, we show that one of these hub genes, the so far unexplored serine/threonine kinase PIM1, is a critical checkpoint for human skin inflammation and potential future therapeutic target in psoriasis. Using in silico integration of human data sets and biological models, we were able to identify a new target in the treatment of psoriasis.

Biological interpretation of genome-wide association studies using predicted gene functions.

PubMed

Pers, Tune H; Karjalainen, Juha M; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R; Yang, Jian; Lui, Julian C; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S N; Hirschhorn, Joel N; Franke, Lude

2015-01-19

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Consensus and conflict cards for metabolic pathway databases.

PubMed

Stobbe, Miranda D; Swertz, Morris A; Thiele, Ines; Rengaw, Trebor; van Kampen, Antoine H C; Moerland, Perry D

2013-06-26

The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial. We introduce the concept of Consensus and Conflict Cards (C₂Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C₂Cards(Human), as a web application http://www.molgenis.org/c2cards, covering five human pathway databases. C₂Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C₂Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.

Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis

PubMed Central

Newaz, Khalique; Sriram, K.; Bera, Debajyoti

2015-01-01

Prion diseases are transmissible neurodegenerative diseases that arise due to conformational change of normal, cellular prion protein (PrPC) to protease-resistant isofrom (rPrPSc). Deposition of misfolded PrpSc proteins leads to an alteration of many signaling pathways that includes immunological and apoptotic pathways. As a result, this culminates in the dysfunction and death of neuronal cells. Earlier works on transcriptomic studies have revealed some affected pathways, but it is not clear which is (are) the prime network pathway(s) that change during the disease progression and how these pathways are involved in crosstalks with each other from the time of incubation to clinical death. We perform network analysis on large-scale transcriptomic data of differentially expressed genes obtained from whole brain in six different mouse strain-prion strain combination models to determine the pathways involved in prion diseases, and to understand the role of crosstalks in disease propagation. We employ a notion of differential network centrality measures on protein interaction networks to identify the potential biological pathways involved. We also propose a crosstalk ranking method based on dynamic protein interaction networks to identify the core network elements involved in crosstalk with different pathways. We identify 148 DEGs (differentially expressed genes) potentially related to the prion disease progression. Functional association of the identified genes implicates a strong involvement of immunological pathways. We extract a bow-tie structure that is potentially dysregulated in prion disease. We also propose an ODE model for the bow-tie network. Predictions related to diseased condition suggests the downregulation of the core signaling elements (PI3Ks and AKTs) of the bow-tie network. In this work, we show using transcriptomic data that the neuronal dysfunction in prion disease is strongly related to the immunological pathways. We conclude that these

Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways.

PubMed

Chen, Lei; Zhang, Yu-Hang; Lu, Guohui; Huang, Tao; Cai, Yu-Dong

2017-02-01

Cancer is a disease that involves abnormal cell growth and can invade or metastasize to other tissues. It is known that several factors are related to its initiation, proliferation, and invasiveness. Recently, it has been reported that long non-coding RNAs (lncRNAs) can participate in specific functional pathways and further regulate the biological function of cancer cells. Studies on lncRNAs are therefore helpful for uncovering the underlying mechanisms of cancer biological processes. We investigated cancer-related lncRNAs using gene ontology (GO) terms and KEGG pathway enrichment scores of neighboring genes that are co-expressed with the lncRNAs by extracting important GO terms and KEGG pathways that can help us identify cancer-related lncRNAs. The enrichment theory of GO terms and KEGG pathways was adopted to encode each lncRNA. Then, feature selection methods were employed to analyze these features and obtain the key GO terms and KEGG pathways. The analysis indicated that the extracted GO terms and KEGG pathways are closely related to several cancer associated processes, such as hormone associated pathways, energy associated pathways, and ribosome associated pathways. And they can accurately predict cancer-related lncRNAs. This study provided novel insight of how lncRNAs may affect tumorigenesis and which pathways may play important roles during it. These results could help understanding the biological mechanisms of lncRNAs and treating cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio.

PubMed

Ryu, Do-Yeal; Rahman, Md Saidur; Pang, Myung-Geol

2017-09-06

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases.

Modeling biochemical pathways in the gene ontology

DOE PAGES

Hill, David P.; D’Eustachio, Peter; Berardini, Tanya Z.; ...

2016-09-01

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes inmore » the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.« less

A living foundry for Synthetic Biological Materials: A synthetic biology roadmap to new advanced materials.

PubMed

Le Feuvre, Rosalind A; Scrutton, Nigel S

2018-06-01

Society is on the cusp of harnessing recent advances in synthetic biology to discover new bio-based products and routes to their affordable and sustainable manufacture. This is no more evident than in the discovery and manufacture of Synthetic Biological Materials , where synthetic biology has the capacity to usher in a new Materials from Biology era that will revolutionise the discovery and manufacture of innovative synthetic biological materials. These will encompass novel, smart, functionalised and hybrid materials for diverse applications whose discovery and routes to bio-production will be stimulated by the fusion of new technologies positioned across physical, digital and biological spheres. This article, which developed from an international workshop held in Manchester, United Kingdom, in 2017 [1], sets out to identify opportunities in the new materials from biology era. It considers requirements, early understanding and foresight of the challenges faced in delivering a Discovery to Manufacturing Pipeline for synthetic biological materials using synthetic biology approaches. This challenge spans the complete production cycle from intelligent and predictive design, fabrication, evaluation and production of synthetic biological materials to new ways of bringing these products to market. Pathway opportunities are identified that will help foster expertise sharing and infrastructure development to accelerate the delivery of a new generation of synthetic biological materials and the leveraging of existing investments in synthetic biology and advanced materials research to achieve this goal.

A LATS biosensor screen identifies VEGFR as a regulator of the Hippo pathway in angiogenesis.

PubMed

Azad, T; Janse van Rensburg, H J; Lightbody, E D; Neveu, B; Champagne, A; Ghaffari, A; Kay, V R; Hao, Y; Shen, H; Yeung, B; Croy, B A; Guan, K L; Pouliot, F; Zhang, J; Nicol, C J B; Yang, X

2018-03-13

The Hippo pathway is a central regulator of tissue development and homeostasis, and has been reported to have a role during vascular development. Here we develop a bioluminescence-based biosensor that monitors the activity of the Hippo core component LATS kinase. Using this biosensor and a library of small molecule kinase inhibitors, we perform a screen for kinases modulating LATS activity and identify VEGFR as an upstream regulator of the Hippo pathway. We find that VEGFR activation by VEGF triggers PI3K/MAPK signaling, which subsequently inhibits LATS and activates the Hippo effectors YAP and TAZ. We further show that the Hippo pathway is a critical mediator of VEGF-induced angiogenesis and tumor vasculogenic mimicry. Thus, our work offers a biosensor tool for the study of the Hippo pathway and suggests a role for Hippo signaling in regulating blood vessel formation in physiological and pathological settings.

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

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.

Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity.

PubMed

Aggio, Raphael B M; Ruggiero, Katya; Villas-Bôas, Silas Granato

2010-12-01

Metabolomics is one of the most recent omics-technologies and uses robust analytical techniques to screen low molecular mass metabolites in biological samples. It has evolved very quickly during the last decade. However, metabolomics datasets are considered highly complex when used to relate metabolite levels to metabolic pathway activity. Despite recent developments in bioinformatics, which have improved the quality of metabolomics data, there is still no straightforward method capable of correlating metabolite level to the activity of different metabolic pathways operating within the cells. Thus, this kind of analysis still depends on extremely laborious and time-consuming processes. Here, we present a new algorithm Pathway Activity Profiling (PAPi) with which we are able to compare metabolic pathway activities from metabolite profiles. The applicability and potential of PAPi was demonstrated using a previously published data from the yeast Saccharomyces cerevisiae. PAPi was able to support the biological interpretations of the previously published observations and, in addition, generated new hypotheses in a straightforward manner. However, PAPi is time consuming to perform manually. Thus, we also present here a new R-software package (PAPi) which implements the PAPi algorithm and facilitates its usage to quickly compare metabolic pathways activities between different experimental conditions. Using the identified metabolites and their respective abundances as input, the PAPi package calculates pathways' Activity Scores, which represents the potential metabolic pathways activities and allows their comparison between conditions. PAPi also performs principal components analysis and analysis of variance or t-test to investigate differences in activity level between experimental conditions. In addition, PAPi generates comparative graphs highlighting up- and down-regulated pathway activity. These datasets are available in http://www.4shared

The exploration of contrasting pathways in Triple Negative Breast Cancer (TNBC).

PubMed

Narrandes, Shavira; Huang, Shujun; Murphy, Leigh; Xu, Wayne

2018-01-04

Triple Negative Breast Cancers (TNBCs) lack the appropriate targets for currently used breast cancer therapies, conferring an aggressive phenotype, more frequent relapse and poorer survival rates. The biological heterogeneity of TNBC complicates the clinical treatment further. We have explored and compared the biological pathways in TNBC and other subtypes of breast cancers, using an in silico approach and the hypothesis that two opposing effects (Yin and Yang) pathways in cancer cells determine the fate of cancer cells. Identifying breast subgroup specific components of these opposing pathways may aid in selecting potential therapeutic targets as well as further classifying the heterogeneous TNBC subtype. Gene expression and patient clinical data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used for this study. Gene Set Enrichment Analysis (GSEA) was used to identify the more active pathways in cancer (Yin) than in normal and the more active pathways in normal (Yang) than in cancer. The clustering analysis was performed to compare pathways of TNBC with other types of breast cancers. The association of pathway classified TNBC sub-groups to clinical outcomes was tested using Cox regression model. Among 4729 curated canonical pathways in GSEA database, 133 Yin pathways (FDR < 0.05) and 71 Yang pathways (p-value <0.05) were discovered in TNBC. The FOXM1 is the top Yin pathway while PPARα is the top Yang pathway in TNBC. The TNBC and other types of breast cancers showed different pathways enrichment significance profiles. Using top Yin and Yang pathways as classifier, the TNBC can be further subtyped into six sub-groups each having different clinical outcomes. We first reported that the FOMX1 pathway is the most upregulated and the PPARα pathway is the most downregulated pathway in TNBC. These two pathways could be simultaneously targeted in further studies. Also the pathway classifier we

Global expression analysis of gene regulatory pathways during endocrine pancreatic development.

PubMed

Gu, Guoqiang; Wells, James M; Dombkowski, David; Preffer, Fred; Aronow, Bruce; Melton, Douglas A

2004-01-01

To define genetic pathways that regulate development of the endocrine pancreas, we generated transcriptional profiles of enriched cells isolated from four biologically significant stages of endocrine pancreas development: endoderm before pancreas specification, early pancreatic progenitor cells, endocrine progenitor cells and adult islets of Langerhans. These analyses implicate new signaling pathways in endocrine pancreas development, and identified sets of known and novel genes that are temporally regulated, as well as genes that spatially define developing endocrine cells from their neighbors. The differential expression of several genes from each time point was verified by RT-PCR and in situ hybridization. Moreover, we present preliminary functional evidence suggesting that one transcription factor encoding gene (Myt1), which was identified in our screen, is expressed in endocrine progenitors and may regulate alpha, beta and delta cell development. In addition to identifying new genes that regulate endocrine cell fate, this global gene expression analysis has uncovered informative biological trends that occur during endocrine differentiation.

Plant synthetic biology for molecular engineering of signalling and development.

PubMed

Nemhauser, Jennifer L; Torii, Keiko U

2016-03-02

Molecular genetic studies of model plants in the past few decades have identified many key genes and pathways controlling development, metabolism and environmental responses. Recent technological and informatics advances have led to unprecedented volumes of data that may uncover underlying principles of plants as biological systems. The newly emerged discipline of synthetic biology and related molecular engineering approaches is built on this strong foundation. Today, plant regulatory pathways can be reconstituted in heterologous organisms to identify and manipulate parameters influencing signalling outputs. Moreover, regulatory circuits that include receptors, ligands, signal transduction components, epigenetic machinery and molecular motors can be engineered and introduced into plants to create novel traits in a predictive manner. Here, we provide a brief history of plant synthetic biology and significant recent examples of this approach, focusing on how knowledge generated by the reference plant Arabidopsis thaliana has contributed to the rapid rise of this new discipline, and discuss potential future directions.

Biological interpretation of genome-wide association studies using predicted gene functions

PubMed Central

Pers, Tune H.; Karjalainen, Juha M.; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R.; Yang, Jian; Lui, Julian C.; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K.; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S.N.; Hirschhorn, Joel N.; Franke, Lude

2015-01-01

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. PMID:25597830

Modeling biological pathway dynamics with timed automata.

PubMed

Schivo, Stefano; Scholma, Jetse; Wanders, Brend; Urquidi Camacho, Ricardo A; van der Vet, Paul E; Karperien, Marcel; Langerak, Rom; van de Pol, Jaco; Post, Janine N

2014-05-01

Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires capturing their dynamics through computational support and models. ANIMO (analysis of Networks with Interactive Modeling) is a tool that enables the construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analyzed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signaling networks. This enforces precision and uniformity in the definition of signaling pathways, contributing to the integration of isolated signaling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behavior of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behavior of the network components is displayed graphically, allowing for an intuitive and interactive modeling experience.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases

PubMed Central

Arakelyan, Arsen; Nersisyan, Lilit; Petrek, Martin; Löffler-Wirth, Henry; Binder, Hans

2016-01-01

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent. PMID:27200087

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.

Profiling conserved biological pathways in Autosomal Dominant Polycystic Kidney Disorder (ADPKD) to elucidate key transcriptomic alterations regulating cystogenesis: A cross-species meta-analysis approach.

PubMed

Chatterjee, Shatakshee; Verma, Srikant Prasad; Pandey, Priyanka

2017-09-05

Initiation and progression of fluid filled cysts mark Autosomal Dominant Polycystic Kidney Disease (ADPKD). Thus, improved therapeutics targeting cystogenesis remains a constant challenge. Microarray studies in single ADPKD animal models species with limited sample sizes tend to provide scattered views on underlying ADPKD pathogenesis. Thus we aim to perform a cross species meta-analysis to profile conserved biological pathways that might be key targets for therapy. Nine ADPKD microarray datasets on rat, mice and human fulfilled our study criteria and were chosen. Intra-species combined analysis was performed after considering removal of batch effect. Significantly enriched GO biological processes and KEGG pathways were computed and their overlap was observed. For the conserved pathways, biological modules and gene regulatory networks were observed. Additionally, Gene Set Enrichment Analysis (GSEA) using Molecular Signature Database (MSigDB) was performed for genes found in conserved pathways. We obtained 28 modules of significantly enriched GO processes and 5 major functional categories from significantly enriched KEGG pathways conserved in human, mice and rats that in turn suggest a global transcriptomic perturbation affecting cyst - formation, growth and progression. Significantly enriched pathways obtained from up-regulated genes such as Genomic instability, Protein localization in ER and Insulin Resistance were found to regulate cyst formation and growth whereas cyst progression due to increased cell adhesion and inflammation was suggested by perturbations in Angiogenesis, TGF-beta, CAMs, and Infection related pathways. Additionally, networks revealed shared genes among pathways e.g. SMAD2 and SMAD7 in Endocytosis and TGF-beta. Our study suggests cyst formation and progression to be an outcome of interplay between a set of several key deregulated pathways. Thus, further translational research is warranted focusing on developing a combinatorial therapeutic

Advancing Biological Understanding and Therapeutics Discovery with Small Molecule Probes

PubMed Central

Schreiber, Stuart L.; Kotz, Joanne D.; Li, Min; Aubé, Jeffrey; Austin, Christopher P.; Reed, John C.; Rosen, Hugh; White, E. Lucile; Sklar, Larry A.; Lindsley, Craig W.; Alexander, Benjamin R.; Bittker, Joshua A.; Clemons, Paul A.; de Souza, Andrea; Foley, Michael A.; Palmer, Michelle; Shamji, Alykhan F.; Wawer, Mathias J.; McManus, Owen; Wu, Meng; Zou, Beiyan; Yu, Haibo; Golden, Jennifer E.; Schoenen, Frank J.; Simeonov, Anton; Jadhav, Ajit; Jackson, Michael R.; Pinkerton, Anthony B.; Chung, Thomas D.Y.; Griffin, Patrick R.; Cravatt, Benjamin F.; Hodder, Peter S.; Roush, William R.; Roberts, Edward; Chung, Dong-Hoon; Jonsson, Colleen B.; Noah, James W.; Severson, William E.; Ananthan, Subramaniam; Edwards, Bruce; Oprea, Tudor I.; Conn, P. Jeffrey; Hopkins, Corey R.; Wood, Michael R.; Stauffer, Shaun R.; Emmitte, Kyle A.

2015-01-01

Small-molecule probes can illuminate biological processes and aid in the assessment of emerging therapeutic targets by perturbing biological systems in a manner distinct from other experimental approaches. Despite the tremendous promise of chemical tools for investigating biology and disease, small-molecule probes were unavailable for most targets and pathways as recently as a decade ago. In 2005, the U.S. National Institutes of Health launched the decade-long Molecular Libraries Program with the intent of innovating in and broadening access to small-molecule science. This Perspective describes how novel small-molecule probes identified through the program are enabling the exploration of biological pathways and therapeutic hypotheses not otherwise testable. These experiences illustrate how small-molecule probes can help bridge the chasm between biological research and the development of medicines, but also highlight the need to innovate the science of therapeutic discovery. PMID:26046436

Identifying Pathways of Teachers' PCK Development

ERIC Educational Resources Information Center

Wongsopawiro, Dirk S.; Zwart, Rosanne C.; van Driel, Jan H.

2017-01-01

This paper describes a method of analysing teacher growth in the context of science education. It focuses on the identification of pathways in the development of secondary school teachers' pedagogical content knowledge (PCK) by the use of the interconnected model of teachers' professional growth (IMTPG).The teachers (n = 12) participated in a…

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

Six Siderophore-Producing Microorganisms Identified in Biological Soil Crusts

NASA Astrophysics Data System (ADS)

Noonan, K.; Anbar, A. D.; Garcia-Pichel, F.; Poret-peterson, A. T.; Hartnett, H. E.

2011-12-01

Biological soil crusts (BSCs) are diverse microbial communities that colonize soils in arid and semi-arid environments. Cyanobacteria in BSCs are pioneer organisms that increase ecosystem habitability by providing fixed carbon (C) and nitrogen (N) as well as by reducing water run-off and increasing infiltration. Photosynthesis and N fixation, in particular, require a variety of metals in large quantities, and yet, metals are predominantly insoluble in the environments where BSCs thrive. Therefore, BSC organisms must have efficient strategies for extracting metals from soil minerals. We hypothesized that BSC microbes, particularly the cyanobacteria, produce siderophores to serve their metal-acquisition needs. Siderophores are small organic compounds that bind Fe with high affinity and are produced by a variety of microorganisms, including cyanobacteria. Most siderophores bind Fe, primarily; however, some can also bind Mo, V, and Cu. Soil siderophores are released by microbes to increase the solubility of metals from minerals and to facilitate microbial uptake. Thus, siderophores serve as chemical weathering agents and provide a direct link between soil microbes and minerals. Studying siderophore production in BSCs provides insight into how BSCs tackle the challenge of acquiring insoluble metals, and may help conservationists determine useful fertilizers for BSC growth by facilitating metal acquisition. Biological soil crusts were collected near Moab, UT. Soil slurries were prepared in deionized water and transferred to modified BG-11 agar plates. The O-CAS agar plate assay was used to screen organisms for siderophore production. Siderophore producing microbes were isolated and identified by16S rRNA gene sequencing. Cultures were then grown in 3 L batch cultures under metal limitation, and siderophore presence was monitored using the traditional liquid CAS assay. After siderophore detection, cells were removed by centrifugation, organic compounds were separated using

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

Systems biology for organotypic cell cultures.

PubMed

Grego, Sonia; Dougherty, Edward R; Alexander, Francis J; Auerbach, Scott S; Berridge, Brian R; Bittner, Michael L; Casey, Warren; Cooley, Philip C; Dash, Ajit; Ferguson, Stephen S; Fennell, Timothy R; Hawkins, Brian T; Hickey, Anthony J; Kleensang, Andre; Liebman, Michael N J; Martin, Florian; Maull, Elizabeth A; Paragas, Jason; Qiao, Guilin Gary; Ramaiahgari, Sreenivasa; Sumner, Susan J; Yoon, Miyoung

2017-01-01

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology

PubMed Central

Latendresse, Mario; Paley, Suzanne M.; Krummenacker, Markus; Ong, Quang D.; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M.; Caspi, Ron

2016-01-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. PMID:26454094

A systems biology approach identified different regulatory networks targeted by KSHV miR-K12-11 in B cells and endothelial cells.

PubMed

Yang, Yajie; Boss, Isaac W; McIntyre, Lauren M; Renne, Rolf

2014-08-08

Kaposi's sarcoma associated herpes virus (KSHV) is associated with tumors of endothelial and lymphoid origin. During latent infection, KSHV expresses miR-K12-11, an ortholog of the human tumor gene hsa-miR-155. Both gene products are microRNAs (miRNAs), which are important post-transcriptional regulators that contribute to tissue specific gene expression. Advances in target identification technologies and molecular interaction databases have allowed a systems biology approach to unravel the gene regulatory networks (GRNs) triggered by miR-K12-11 in endothelial and lymphoid cells. Understanding the tissue specific function of miR-K12-11 will help to elucidate underlying mechanisms of KSHV pathogenesis. Ectopic expression of miR-K12-11 differentially affected gene expression in BJAB cells of lymphoid origin and TIVE cells of endothelial origin. Direct miRNA targeting accounted for a small fraction of the observed transcriptome changes: only 29 genes were identified as putative direct targets of miR-K12-11 in both cell types. However, a number of commonly affected biological pathways, such as carbohydrate metabolism and interferon response related signaling, were revealed by gene ontology analysis. Integration of transcriptome profiling, bioinformatic algorithms, and databases of protein-protein interactome from the ENCODE project identified different nodes of GRNs utilized by miR-K12-11 in a tissue-specific fashion. These effector genes, including cancer associated transcription factors and signaling proteins, amplified the regulatory potential of a single miRNA, from a small set of putative direct targets to a larger set of genes. This is the first comparative analysis of miRNA-K12-11's effects in endothelial and B cells, from tissues infected with KSHV in vivo. MiR-K12-11 was able to broadly modulate gene expression in both cell types. Using a systems biology approach, we inferred that miR-K12-11 establishes its GRN by both repressing master TFs and influencing

Identification of key target genes and pathways in laryngeal carcinoma

PubMed Central

Liu, Feng; Du, Jintao; Liu, Jun; Wen, Bei

2016-01-01

The purpose of the present study was to screen the key genes associated with laryngeal carcinoma and to investigate the molecular mechanism of laryngeal carcinoma progression. The gene expression profile of GSE10935 [Gene Expression Omnibus (GEO) accession number], including 12 specimens from laryngeal papillomas and 12 specimens from normal laryngeal epithelia controls, was downloaded from the GEO database. Differentially expressed genes (DEGs) were screened in laryngeal papillomas compared with normal controls using Limma package in R language, followed by Gene Ontology (GO) enrichment analysis and pathway enrichment analysis. Furthermore, the protein-protein interaction (PPI) network of DEGs was constructed using Cytoscape software and modules were analyzed using MCODE plugin from the PPI network. Furthermore, significant biological pathway regions (sub-pathway) were identified by using iSubpathwayMiner analysis. A total of 67 DEGs were identified, including 27 up-regulated genes and 40 down-regulated genes and they were involved in different GO terms and pathways. PPI network analysis revealed that Ras association (RalGDS/AF-6) domain family member 1 (RASSF1) was a hub protein. The sub-pathway analysis identified 9 significantly enriched sub-pathways, including glycolysis/gluconeogenesis and nitrogen metabolism. Genes such as phosphoglycerate kinase 1 (PGK1), carbonic anhydrase II (CA2), and carbonic anhydrase XII (CA12) whose node degrees were >10 were identified in the disease risk sub-pathway. Genes in the sub-pathway, such as RASSF1, PGK1, CA2 and CA12 were presumed to serve critical roles in laryngeal carcinoma. The present study identified DEGs and their sub-pathways in the disease, which may serve as potential targets for treatment of laryngeal carcinoma. PMID:27446427

Systematic analysis of signaling pathways using an integrative environment.

PubMed

Visvanathan, Mahesh; Breit, Marc; Pfeifer, Bernhard; Baumgartner, Christian; Modre-Osprian, Robert; Tilg, Bernhard

2007-01-01

Understanding the biological processes of signaling pathways as a whole system requires an integrative software environment that has comprehensive capabilities. The environment should include tools for pathway design, visualization, simulation and a knowledge base concerning signaling pathways as one. In this paper we introduce a new integrative environment for the systematic analysis of signaling pathways. This system includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning signaling pathways that provides the basic understanding of the biological system, its structure and functioning. The system is designed with a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. The TNFa-mediated NF-kB signal trans-duction pathway model was designed and tested using our integrative framework. It was also useful to define the structure of the knowledge base. Sensitivity analysis of this specific pathway was performed providing simulation data. Then the model was extended showing promising initial results. The proposed system offers a holistic view of pathways containing biological and modeling data. It will help us to perform biological interpretation of the simulation results and thus contribute to a better understanding of the biological system for drug identification.

Consensus and conflict cards for metabolic pathway databases

PubMed Central

2013-01-01

Background The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial. Results We introduce the concept of Consensus and Conflict Cards (C2Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C2CardsHuman, as a web application http://www.molgenis.org/c2cards, covering five human pathway databases. Conclusions C2Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C2Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement. PMID:23803311

Pathway index models for construction of patient-specific risk profiles.

PubMed

Eng, Kevin H; Wang, Sijian; Bradley, William H; Rader, Janet S; Kendziorski, Christina

2013-04-30

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd.

New Statistics for Testing Differential Expression of Pathways from Microarray Data

NASA Astrophysics Data System (ADS)

Siu, Hoicheong; Dong, Hua; Jin, Li; Xiong, Momiao

Exploring biological meaning from microarray data is very important but remains a great challenge. Here, we developed three new statistics: linear combination test, quadratic test and de-correlation test to identify differentially expressed pathways from gene expression profile. We apply our statistics to two rheumatoid arthritis datasets. Notably, our results reveal three significant pathways and 275 genes in common in two datasets. The pathways we found are meaningful to uncover the disease mechanisms of rheumatoid arthritis, which implies that our statistics are a powerful tool in functional analysis of gene expression data.

Suppressors of systemin signaling identify genes in the tomato wound response pathway.

PubMed Central

Howe, G A; Ryan, C A

1999-01-01

In tomato plants, systemic induction of defense genes in response to herbivory or mechanical wounding is regulated by an 18-amino-acid peptide signal called systemin. Transgenic plants that overexpress prosystemin, the systemin precursor, from a 35S::prosystemin (35S::prosys) transgene exhibit constitutive expression of wound-inducible defense proteins including proteinase inhibitors and polyphenol oxidase. To study further the role of (pro)systemin in the wound response pathway, we isolated and characterized mutations that suppress 35S::prosys-mediated phenotypes. Ten recessive, extragenic suppressors were identified. Two of these define new alleles of def-1, a previously identified mutation that blocks both wound- and systemin-induced gene expression and renders plants susceptible to herbivory. The remaining mutants defined four loci designated Spr-1, Spr-2, Spr-3, and Spr-4 (for Suppressed in 35S::prosystemin-mediated responses). spr-3 and spr-4 mutants were not significantly affected in their response to either systemin or mechanical wounding. In contrast, spr-1 and spr-2 plants lacked systemic wound responses and were insensitive to systemin. These results confirm the function of (pro)systemin in the transduction of systemic wound signals and further establish that wounding, systemin, and 35S::prosys induce defensive gene expression through a common signaling pathway defined by at least three genes (Def-1, Spr-1, and Spr-2). PMID:10545469

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.

PubMed

Ozerov, Ivan V; Lezhnina, Ksenia V; Izumchenko, Evgeny; Artemov, Artem V; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N; Labat, Ivan; West, Michael D; Buzdin, Anton; Cantor, Charles R; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-11-16

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

PubMed Central

Ozerov, Ivan V.; Lezhnina, Ksenia V.; Izumchenko, Evgeny; Artemov, Artem V.; Medintsev, Sergey; Vanhaelen, Quentin; Aliper, Alexander; Vijg, Jan; Osipov, Andreyan N.; Labat, Ivan; West, Michael D.; Buzdin, Anton; Cantor, Charles R.; Nikolsky, Yuri; Borisov, Nikolay; Irincheeva, Irina; Khokhlovich, Edward; Sidransky, David; Camargo, Miguel Luiz; Zhavoronkov, Alex

2016-01-01

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy. PMID:27848968

Integrative analysis of RUNX1 downstream pathways and target genes

PubMed Central

Michaud, Joëlle; Simpson, Ken M; Escher, Robert; Buchet-Poyau, Karine; Beissbarth, Tim; Carmichael, Catherine; Ritchie, Matthew E; Schütz, Frédéric; Cannon, Ping; Liu, Marjorie; Shen, Xiaofeng; Ito, Yoshiaki; Raskind, Wendy H; Horwitz, Marshall S; Osato, Motomi; Turner, David R; Speed, Terence P; Kavallaris, Maria; Smyth, Gordon K; Scott, Hamish S

2008-01-01

Background The RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML). The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia. Results Here we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1) cell lines with RUNX1 mutations from FPD-AML patients, 2) over-expression of RUNX1 and CBFβ, and 3) Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes) significantly correlate with published microarray data from sporadic AML patients with mutations in either RUNX1 or its cofactor, CBFβ. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous RUNX1 point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes. Conclusion This work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease progression in both

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology.

PubMed

Karp, Peter D; Latendresse, Mario; Paley, Suzanne M; Krummenacker, Markus; Ong, Quang D; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M; Caspi, Ron

2016-09-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio

PubMed Central

Ryu, Do-Yeal

2017-01-01

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases. PMID:28878155

Identifying mutant pathways in the histiocytoses.

PubMed

Prince, H Miles

2014-11-06

In this issue of Blood, the findings of Chakraborty et al and Emile et al support a model in which the mitogen-activated protein kinase (MAPK) and PI3K/AKT pathways are critical in the pathogenesis of 2 of the most common histiocytoses—Langerhans cell histiocytosis (LCH) and Erdheim-Chester disease (ECD)—whereas their respective mutational profiles demonstrate important similarities and differences.

Framing Ethnic Variations in Alcohol Outcomes from Biological Pathways to Neighborhood Context

PubMed Central

Chartier, Karen G.; Scott, Denise M.; Wall, Tamara L.; Covault, Jonathan; Karriker-Jaffe, Katherine J.; Mills, Britain A.; Luczak, Susan E.; Caetano, Raul; Arroyo, Judith A.

2013-01-01

Health disparities research seeks to eliminate disproportionate negative health outcomes experienced in some racial/ethnic minority groups. This brief review presents findings on factors associated with drinking and alcohol-related problems in racial/ethnic groups. Those discussed are: 1) biological pathways to alcohol problems, 2) gene by stress interactions, 3) neighborhood disadvantage, stress, and access to alcohol, and 4) drinking cultures and contexts. These factors and their interrelationships are complex, requiring a multi-level perspective. The use of interdisciplinary teams and an epigenetic focus are suggested to move the research forward. The application of multi-level research to policy, prevention, and intervention programs may help prioritize combinations of the most promising intervention targets. PMID:24483624

InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer.

PubMed

Dimitrova, N; Nagaraj, A B; Razi, A; Singh, S; Kamalakaran, S; Banerjee, N; Joseph, P; Mankovich, A; Mittal, P; DiFeo, A; Varadan, V

2017-04-27

Characterizing the complex interplay of cellular processes in cancer would enable the discovery of key mechanisms underlying its development and progression. Published approaches to decipher driver mechanisms do not explicitly model tissue-specific changes in pathway networks and the regulatory disruptions related to genomic aberrations in cancers. We therefore developed InFlo, a novel systems biology approach for characterizing complex biological processes using a unique multidimensional framework integrating transcriptomic, genomic and/or epigenomic profiles for any given cancer sample. We show that InFlo robustly characterizes tissue-specific differences in activities of signalling networks on a genome scale using unique probabilistic models of molecular interactions on a per-sample basis. Using large-scale multi-omics cancer datasets, we show that InFlo exhibits higher sensitivity and specificity in detecting pathway networks associated with specific disease states when compared to published pathway network modelling approaches. Furthermore, InFlo's ability to infer the activity of unmeasured signalling network components was also validated using orthogonal gene expression signatures. We then evaluated multi-omics profiles of primary high-grade serous ovarian cancer tumours (N=357) to delineate mechanisms underlying resistance to frontline platinum-based chemotherapy. InFlo was the only algorithm to identify hyperactivation of the cAMP-CREB1 axis as a key mechanism associated with resistance to platinum-based therapy, a finding that we subsequently experimentally validated. We confirmed that inhibition of CREB1 phosphorylation potently sensitized resistant cells to platinum therapy and was effective in killing ovarian cancer stem cells that contribute to both platinum-resistance and tumour recurrence. Thus, we propose InFlo to be a scalable and widely applicable and robust integrative network modelling framework for the discovery of evidence-based biomarkers

Modeling of cell signaling pathways in macrophages by semantic networks

PubMed Central

Hsing, Michael; Bellenson, Joel L; Shankey, Conor; Cherkasov, Artem

2004-01-01

Background Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways. Results We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages. Conclusions We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed

A systems biology approach to detect key pathways and interaction networks in gastric cancer on the basis of microarray analysis.

PubMed

Guo, Leilei; Song, Chunhua; Wang, Peng; Dai, Liping; Zhang, Jianying; Wang, Kaijuan

2015-11-01

The aim of the present study was to explore key molecular pathways contributing to gastric cancer (GC) and to construct an interaction network between significant pathways and potential biomarkers. Publicly available gene expression profiles of GSE29272 for GC, and data for the corresponding normal tissue, were downloaded from Gene Expression Omnibus. Pre‑processing and differential analysis were performed with R statistical software packages, and a number of differentially expressed genes (DEGs) were obtained. A functional enrichment analysis was performed for all the DEGs with a BiNGO plug‑in in Cytoscape. Their correlation was analyzed in order to construct a network. The modularity analysis and pathway identification operations were used to identify graph clusters and associated pathways. The underlying molecular mechanisms involving these DEGs were also assessed by data mining. A total of 249 DEGs, which were markedly upregulated and downregulated, were identified. The extracellular region contained the most significantly over‑represented functional terms, with respect to upregulated and downregulated genes, and the closest topological matches were identified for taste transduction and regulation of autophagy. In addition, extracellular matrix‑receptor interactions were identified as the most relevant pathway associated with the progression of GC. The genes for fibronectin 1, secreted phosphoprotein 1, collagen type 4 variant α‑1/2 and thrombospondin 1, which are involved in the pathways, may be considered as potential therapeutic targets for GC. A series of associations between candidate genes and key pathways were also identified for GC, and their correlation may provide novel insights into the pathogenesis of GC.

Systems Biology in Animal Breeding: Identifying relationships among markers, genes, and phenotypes

USDA-ARS?s Scientific Manuscript database

The Breeding and Genetics Symposium titled “Systems Biology in Animal Breeding: Identifying relationships among markers, genes, and phenotypes” was held at the Joint Annual Meeting of the American Dairy Science Association and the American Society of Animal Science in Phoenix, AZ, July 15 to 19, 201...

Knowledge-Assisted Approach to Identify Pathways with Differential Dependencies | Office of Cancer Genomics

Cancer.gov

We have previously developed a statistical method to identify gene sets enriched with condition-specific genetic dependencies. The method constructs gene dependency networks from bootstrapped samples in one condition and computes the divergence between distributions of network likelihood scores from different conditions. It was shown to be capable of sensitive and specific identification of pathways with phenotype-specific dysregulation, i.e., rewiring of dependencies between genes in different conditions.

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

Regulation of Tissue Growth by the Mammalian Hippo Signaling Pathway

PubMed Central

Watt, Kevin I.; Harvey, Kieran F.; Gregorevic, Paul

2017-01-01

The integrative control of diverse biological processes such as proliferation, differentiation, apoptosis and metabolism is essential to maintain cellular and tissue homeostasis. Disruption of these underlie the development of many disease states including cancer and diabetes, as well as many of the complications that arise as a consequence of aging. These biological outputs are governed by many cellular signaling networks that function independently, and in concert, to convert changes in hormonal, mechanical and metabolic stimuli into alterations in gene expression. First identified in Drosophila melanogaster as a powerful mediator of cell division and apoptosis, the Hippo signaling pathway is a highly conserved regulator of mammalian organ size and functional capacity in both healthy and diseased tissues. Recent studies have implicated the pathway as an effector of diverse physiological cues demonstrating an essential role for the Hippo pathway as an integrative component of cellular homeostasis. In this review, we will: (a) outline the critical signaling elements that constitute the mammalian Hippo pathway, and how they function to regulate Hippo pathway-dependent gene expression and tissue growth, (b) discuss evidence that shows this pathway functions as an effector of diverse physiological stimuli and (c) highlight key questions in this developing field. PMID:29225579

Exploration of potential biomarkers and related biological pathways for PCB exposure in maternal and cord serum: A pilot birth cohort study in Chiba, Japan.

PubMed

Eguchi, Akifumi; Sakurai, Kenichi; Watanabe, Masahiro; Mori, Chisato

2017-05-01

Polychlorinated biphenyls (PCBs) have been associated with adverse human reproductive and fetal developmental measures or outcomes because of their endocrine-disrupting effects; however, the biological mechanisms of adverse effects of PCB exposure in humans are not currently well established. In this study, we aimed to identify the biological pathways and potential biomarkers of PCB exposure in maternal and umbilical cord serum using a hydrophilic interaction chromatography-tandem mass spectrometry (HILIC-MS/MS) metabolomics platform. The median concentration of total PCBs in maternal (n=93) and cord serum (n=93) were 350 and 70pgg -1 wet wt, respectively. PCB levels in maternal and fetal serum from the Chiba Study of Mother and Children's Health (C-MACH) cohort are comparable to those of earlier cohort studies conducted in Japan, the USA, and European countries. We used the random forest model with the metabolome profile to predict exposure levels of PCB (first quartile [Q1] and fourth quartile [Q4]) for pregnant women and fetuses. In the prediction model for classification of Q1 versus Q4 (area-under-curve [AUC]: pregnant women=0.812 and fetuses=0.919), citraconic acid level in maternal serum and ethanolamine, p-hydroxybenzoate, and purine levels in cord serum had >0.70 AUC values. These candidate biomarkers and metabolite included in composited models were related to glutathione and amino acid metabolism in maternal serum and the amino acid metabolism and ubiquinone and other terpenoid-quinone biosynthesis in cord serum (FDR <0.10), indicating disruption of metabolic pathways by PCB exposure in pregnant women and fetuses. These results showed that metabolome analysis might be useful to explore potential biomarkers and related biological pathways for PCB exposure. Thus, more detailed studies are needed to verify sensitivity of the biomarkers and clarify the biochemical changes resulting from PCB exposure. Copyright © 2017 The Authors. Published by Elsevier Ltd

Use of RNA-seq to identify cardiac genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy

PubMed Central

Friedenberg, Steven G.; Chdid, Lhoucine; Keene, Bruce; Sherry, Barbara; Motsinger-Reif, Alison; Meurs, Kathryn M.

2017-01-01

OBJECTIVE To identify cardiac tissue genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy (DCM). ANIMALS 8 dogs with and 5 dogs without DCM. PROCEDURES Following euthanasia, samples of left ventricular myocardium were collected from each dog. Total RNA was extracted from tissue samples, and RNA sequencing was performed on each sample. Samples from dogs with and without DCM were grouped to identify genes that were differentially regulated between the 2 populations. Overrepresentation analysis was performed on upregulated and downregulated gene sets to identify altered molecular pathways in dogs with DCM. RESULTS Genes involved in cellular energy metabolism, especially metabolism of carbohydrates and fats, were significantly downregulated in dogs with DCM. Expression of cardiac structural proteins was also altered in affected dogs. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that RNA sequencing may provide important insights into the pathogenesis of DCM in dogs and highlight pathways that should be explored to identify causative mutations and develop novel therapeutic interventions. PMID:27347821

Use of RNA-seq to identify cardiac genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy.

PubMed

Friedenberg, Steven G; Chdid, Lhoucine; Keene, Bruce; Sherry, Barbara; Motsinger-Reif, Alison; Meurs, Kathryn M

2016-07-01

OBJECTIVE To identify cardiac tissue genes and gene pathways differentially expressed between dogs with and without dilated cardiomyopathy (DCM). ANIMALS 8 dogs with and 5 dogs without DCM. PROCEDURES Following euthanasia, samples of left ventricular myocardium were collected from each dog. Total RNA was extracted from tissue samples, and RNA sequencing was performed on each sample. Samples from dogs with and without DCM were grouped to identify genes that were differentially regulated between the 2 populations. Overrepresentation analysis was performed on upregulated and downregulated gene sets to identify altered molecular pathways in dogs with DCM. RESULTS Genes involved in cellular energy metabolism, especially metabolism of carbohydrates and fats, were significantly downregulated in dogs with DCM. Expression of cardiac structural proteins was also altered in affected dogs. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that RNA sequencing may provide important insights into the pathogenesis of DCM in dogs and highlight pathways that should be explored to identify causative mutations and develop novel therapeutic interventions.

Disrupted Signaling through the Fanconi Anemia Pathway Leads to Dysfunctional Hematopoietic Stem Cell Biology: Underlying Mechanisms and Potential Therapeutic Strategies

PubMed Central

Geiselhart, Anja; Lier, Amelie; Walter, Dagmar; Milsom, Michael D.

2012-01-01

Fanconi anemia (FA) is the most common inherited bone marrow failure syndrome. FA patients suffer to varying degrees from a heterogeneous range of developmental defects and, in addition, have an increased likelihood of developing cancer. Almost all FA patients develop a severe, progressive bone marrow failure syndrome, which impacts upon the production of all hematopoietic lineages and, hence, is thought to be driven by a defect at the level of the hematopoietic stem cell (HSC). This hypothesis would also correlate with the very high incidence of MDS and AML that is observed in FA patients. In this paper, we discuss the evidence that supports the role of dysfunctional HSC biology in driving the etiology of the disease. Furthermore, we consider the different model systems currently available to study the biology of cells defective in the FA signaling pathway and how they are informative in terms of identifying the physiologic mediators of HSC depletion and dissecting their putative mechanism of action. Finally, we ask whether the insights gained using such disease models can be translated into potential novel therapeutic strategies for the treatment of the hematologic disorders in FA patients. PMID:22675615

No longer "if," but "when": the coming abbreviated approval pathway for follow-on biologics.

PubMed

Kelly, Jeremiah J; David, Michael

2009-01-01

Abbreviated approval of follow-on biologics involves answering complex scientific, legal, and policy questions. The Food and Drug Administration (FDA or the Agency) asserts that it lacks the statutory authority to approve follow-on versions of biologics licensed under Section 351 of the Public Health Service Act (PHSA). Despite persuasive arguments to the contrary the one hundred and tenth Congress entertained four legislative proposals to give FDA this authority, each markedly different. It is no longer a question of "if," but "when" FDA will receive authority to review and license abbreviated applications for follow-on biologics. Any legislation in the one hundred and eleventh Congress must determine: (1) if FDA should be granted authority to develop an abbreviated pathway through rulemaking or guidance; (2) if human clinical trials should be mandatory or discretionary; (3) the feasibility of interchangeability determinations in light of patient safety concerns; (4) the duration of marketing exclusivity for associated products; (5) which products are eligible for follow-on approval; and (6) the degree to which uniformity is achievable between the FD&C Act and the PHSA. This paper recommends the one hundred and eleventh Congress strike a balance between patient safety, incentives for product innovation, price competition, and the need for a flexible, transparent process that capitalizes on FDA's growing expertise with follow-on biologics approvals under Section 505(b)(2) of the FD&C Act.

Incorporating Information of microRNAs into Pathway Analysis in a Genome-Wide Association Study of Bipolar Disorder

PubMed Central

Shih, Wei-Liang; Kao, Chung-Feng; Chuang, Li-Chung; Kuo, Po-Hsiu

2012-01-01

MicroRNAs (miRNAs) are known to be important post-transcriptional regulators that are involved in the etiology of complex psychiatric traits. The present study aimed to incorporate miRNAs information into pathway analysis using a genome-wide association dataset to identify relevant biological pathways for bipolar disorder (BPD). We selected psychiatric- and neurological-associated miRNAs (N = 157) from PhenomiR database. The miRNA target genes (miTG) predictions were obtained from microRNA.org. Canonical pathways (N = 4,051) were downloaded from the Molecule Signature Database. We employed a novel weighting scheme for miTGs in pathway analysis using methods of gene set enrichment analysis and sum-statistic. Under four statistical scenarios, 38 significantly enriched pathways (P-value < 0.01 after multiple testing correction) were identified for the risk of developing BPD, including pathways of ion channels associated (e.g., gated channel activity, ion transmembrane transporter activity, and ion channel activity) and nervous related biological processes (e.g., nervous system development, cytoskeleton, and neuroactive ligand receptor interaction). Among them, 19 were identified only when the weighting scheme was applied. Many miRNA-targeted genes were functionally related to ion channels, collagen, and axonal growth and guidance that have been suggested to be associated with BPD previously. Some of these genes are linked to the regulation of miRNA machinery in the literature. Our findings provide support for the potential involvement of miRNAs in the psychopathology of BPD. Further investigations to elucidate the functions and mechanisms of identified candidate pathways are needed. PMID:23264780

Extending double modulation: combinatorial rules for identifying the modulations necessary for determining elasticities in metabolic pathways.

PubMed

Giersch, C; Cornish-Bowden, A

1996-10-07

The double modulation method for determining the elasticities of pathway enzymes, originally devised by Kacser & Burns (Biochem. Soc. Trans. 7, 1149-1160, 1979), is extended to pathways of complex topological structure, including branching and feedback loops. An explicit system of linear equations for the unknown elasticities is derived. The constraints imposed on this linear system imply that modulations of more than one enzyme are not necessarily independent. Simple combinatorial rules are described for identifying without using any algebra the set of independent modulations that allow the determination of the elasticities of any enzyme. By repeated application, the minimum numbers of modulations required to determine the elasticities of all enzymes of a given pathway can be determined. The procedure is illustrated with numerous examples.

Computational Approaches for Identifying Adverse Outcome Pathways

EPA Science Inventory

Adverse Outcome Pathways (AOPs) provide a framework for organizing toxicity information to improve predictions of the potential adverse impact of environment stressors on humans or wildlife populations, but these benefits are currently limited by the small number of AOPs currentl...

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

Systems Biology for Organotypic Cell Cultures

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

Grego, Sonia; Dougherty, Edward R.; Alexander, Francis J.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the

A model for genetic and epigenetic regulatory networks identifies rare pathways for transcription factor induced pluripotency

NASA Astrophysics Data System (ADS)

Artyomov, Maxim; Meissner, Alex; Chakraborty, Arup

2010-03-01

Most cells in an organism have the same DNA. Yet, different cell types express different proteins and carry out different functions. This is because of epigenetic differences; i.e., DNA in different cell types is packaged distinctly, making it hard to express certain genes while facilitating the expression of others. During development, upon receipt of appropriate cues, pluripotent embryonic stem cells differentiate into diverse cell types that make up the organism (e.g., a human). There has long been an effort to make this process go backward -- i.e., reprogram a differentiated cell (e.g., a skin cell) to pluripotent status. Recently, this has been achieved by transfecting certain transcription factors into differentiated cells. This method does not use embryonic material and promises the development of patient-specific regenerative medicine, but it is inefficient. The mechanisms that make reprogramming rare, or even possible, are poorly understood. We have developed the first computational model of transcription factor-induced reprogramming. Results obtained from the model are consistent with diverse observations, and identify the rare pathways that allow reprogramming to occur. If validated, our model could be further developed to design optimal strategies for reprogramming and shed light on basic questions in biology.

Proteome Profiling Reveals Potential Toxicity and Detoxification Pathways Following Exposure of BEAS-2B Cells to Engineered Nanoparticle Titanium Dioxide

EPA Science Inventory

Identification of toxicity pathways linked to chemical -exposure is critical for a better understanding of biological effects of the exposure, toxic mechanisms, and for enhancement of the prediction of chemical toxicity and adverse health outcomes. To identify toxicity pathways a...

Clinical and biological relevance of genomic heterogeneity in chronic lymphocytic leukemia.

PubMed

Friedman, Daphne R; Lucas, Joseph E; Weinberg, J Brice

2013-01-01

Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients' leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL. To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups. Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes. Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways.

Framing ethnic variations in alcohol outcomes from biological pathways to neighborhood context.

PubMed

Chartier, Karen G; Scott, Denise M; Wall, Tamara L; Covault, Jonathan; Karriker-Jaffe, Katherine J; Mills, Britain A; Luczak, Susan E; Caetano, Raul; Arroyo, Judith A

2014-03-01

Health disparities research seeks to eliminate disproportionate negative health outcomes experienced in some racial/ethnic minority groups. This brief review presents findings on factors associated with drinking and alcohol-related problems in racial/ethnic groups. Those discussed are as follows: (i) biological pathways to alcohol problems, (ii) gene × stress interactions, (iii) neighborhood disadvantage, stress, and access to alcohol, and (iv) drinking cultures and contexts. These factors and their interrelationships are complex, requiring a multilevel perspective. The use of interdisciplinary teams and an epigenetic focus are suggested to move the research forward. The application of multilevel research to policy, prevention, and intervention programs may help prioritize combinations of the most promising intervention targets. Copyright © 2014 by the Research Society on Alcoholism.

Hepatic Proteomic Analysis Revealed Altered Metabolic Pathways in Insulin Resistant Akt1+/-/Akt2-/-Mice

PubMed Central

Pedersen, Brian A; Wang, Weiwen; Taylor, Jared F; Khattab, Omar S; Chen, Yu-Han; Edwards, Robert A; Yazdi, Puya G; Wang, Ping H

2015-01-01

Objective The aim of this study was to identify liver proteome changes in a mouse model of severe insulin resistance and markedly decreased leptin levels. Methods Two-dimensional differential gel electrophoresis was utilized to identify liver proteome changes in AKT1+/-/AKT2-/- mice. Proteins with altered levels were identified with tandem mass spectrometry. Ingenuity Pathway analysis was performed for the interpretation of the biological significance of the observed proteomic changes. Results 11 proteins were identified from 2 biological replicates to be differentially expressed by a ratio of at least 1.3 between age-matched insulin resistant (Akt1+/-/Akt2-/-) and wild type mice. Albumin and mitochondrial ornithine aminotransferase were detected from multiple spots, which suggest post-translational modifications. Enzymes of the urea cycle were common members of top regulated pathways. Conclusion Our results help to unveil the regulation of the liver proteome underlying altered metabolism in an animal model of severe insulin resistance. PMID:26455965

A shortcut to wide-ranging biological actions of dietary polyphenols: modulation of the nitrate-nitrite-nitric oxide pathway in the gut.

PubMed

Rocha, Bárbara S; Nunes, Carla; Pereira, Cassilda; Barbosa, Rui M; Laranjinha, João

2014-08-01

Dietary polyphenols are complex, natural compounds with recognized health benefits. Initially attractive to the biomedical area due to their in vitro antioxidant properties, the biological implications of polyphenols are now known to be far from their acute ability to scavenge free radicals but rather to modulate redox signaling pathways. Actually, it is now recognized that dietary polyphenols are extensively metabolized in vivo and that the chemical, biophysical and biological properties of their metabolites are, in most cases, quite different from the ones of the parent molecules. Hence, the study of the metabolic, absorptive and signaling pathways of both phenolics and derivatives has become a major issue. In this paper we propose a short-cut for the systemic effects of polyphenols in connection with nitric oxide (˙NO) biology. This free radical is a ubiquitous signaling molecule with pivotal functions in vivo. It is produced through an enzymatic pathway and also through the reduction of dietary nitrate and nitrite in the human stomach. At acidic gastric pH, dietary polyphenols, in the form they are conveyed in foods and at high concentration, not only promote nitrite reduction to ˙NO but also embark in a complex network of chemical reactions to produce higher nitrogen oxides with signaling functions, namely by inducing post-translational modifications. Modified endogenous molecules, such as nitrated proteins and lipids, acquire important physiological functions. Thus, local and systemic effects of ˙NO such as modulation of vascular tone, mucus production in the gut and protection against ischemia-reperfusion injury are, in this sense, triggered by dietary polyphenols. Evidence to support the signaling and biological effects of polyphenols by modulation of the nitrate-nitrite-NO pathway will be herein provided and discussed. General actions of polyphenols encompassing absorption and metabolism in the intestine/liver are short-cut via the production of

Target Deconvolution Efforts on Wnt Pathway Screen Reveal Dual Modulation of Oxidative Phosphorylation and SERCA2.

PubMed

Casás-Selves, Matias; Zhang, Andrew X; Dowling, James E; Hallén, Stefan; Kawatkar, Aarti; Pace, Nicholas J; Denz, Christopher R; Pontz, Timothy; Garahdaghi, Farzin; Cao, Qing; Sabirsh, Alan; Thakur, Kumar; O'Connell, Nichole; Hu, Jun; Cornella-Taracido, Iván; Weerapana, Eranthie; Zinda, Michael; Goodnow, Robert A; Castaldi, M Paola

2017-06-21

Wnt signaling is critical for development, cell proliferation and differentiation, and mutations in this pathway resulting in constitutive signaling have been implicated in various cancers. A pathway screen using a Wnt-dependent reporter identified a chemical series based on a 1,2,3-thiadiazole-5-carboxamide (TDZ) core with sub-micromolar potency. Herein we report a comprehensive mechanism-of-action deconvolution study toward identifying the efficacy target(s) and biological implication of this chemical series involving bottom-up quantitative chemoproteomics, cell biology, and biochemical methods. Through observing the effects of our probes on metabolism and performing confirmatory cellular and biochemical assays, we found that this chemical series inhibits ATP synthesis by uncoupling the mitochondrial potential. Affinity chemoproteomics experiments identified sarco(endo)plasmic reticulum Ca 2+ -dependent ATPase (SERCA2) as a binding partner of the TDZ series, and subsequent validation studies suggest that the TDZ series can act as ionophores through SERCA2 toward Wnt pathway inhibition. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identifying key genes, pathways and screening therapeutic agents for manganese-induced Alzheimer disease using bioinformatics analysis.

PubMed

Ling, JunJun; Yang, Shengyou; Huang, Yi; Wei, Dongfeng; Cheng, Weidong

2018-06-01

Alzheimer disease (AD) is a progressive neurodegenerative disease, the etiology of which remains largely unknown. Accumulating evidence indicates that elevated manganese (Mn) in brain exerts toxic effects on neurons and contributes to AD development. Thus, we aimed to explore the gene and pathway variations through analysis of high through-put data in this process.To screen the differentially expressed genes (DEGs) that may play critical roles in Mn-induced AD, public microarray data regarding Mn-treated neurocytes versus controls (GSE70845), and AD versus controls (GSE48350), were downloaded and the DEGs were screened out, respectively. The intersection of the DEGs of each datasets was obtained by using Venn analysis. Then, gene ontology (GO) function analysis and KEGG pathway analysis were carried out. For screening hub genes, protein-protein interaction network was constructed. At last, DEGs were analyzed in Connectivity Map (CMAP) for identification of small molecules that overcome Mn-induced neurotoxicity or AD development.The intersection of the DEGs obtained 140 upregulated and 267 downregulated genes. The top 5 items of biological processes of GO analysis were taxis, chemotaxis, cell-cell signaling, regulation of cellular physiological process, and response to wounding. The top 5 items of KEGG pathway analysis were cytokine-cytokine receptor interaction, apoptosis, oxidative phosphorylation, Toll-like receptor signaling pathway, and insulin signaling pathway. Afterwards, several hub genes such as INSR, VEGFA, PRKACB, DLG4, and BCL2 that might play key roles in Mn-induced AD were further screened out. Interestingly, tyrphostin AG-825, an inhibitor of tyrosine phosphorylation, was predicted to be a potential agent for overcoming Mn-induced neurotoxicity or AD development.The present study provided a novel insight into the molecular mechanisms of Mn-induced neurotoxicity or AD development and screened out several small molecular candidates that might be

Crystallization Pathways in Biomineralization

NASA Astrophysics Data System (ADS)

Weiner, Steve; Addadi, Lia

2011-08-01

A crystallization pathway describes the movement of ions from their source to the final product. Cells are intimately involved in biological crystallization pathways. In many pathways the cells utilize a unique strategy: They temporarily concentrate ions in intracellular membrane-bound vesicles in the form of a highly disordered solid phase. This phase is then transported to the final mineralization site, where it is destabilized and crystallizes. We present four case studies, each of which demonstrates specific aspects of biological crystallization pathways: seawater uptake by foraminifera, calcite spicule formation by sea urchin larvae, goethite formation in the teeth of limpets, and guanine crystal formation in fish skin and spider cuticles. Three representative crystallization pathways are described, and aspects of the different stages of crystallization are discussed. An in-depth understanding of these complex processes can lead to new ideas for synthetic crystallization processes of interest to materials science.

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

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.

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.

BioPAX – A community standard for pathway data sharing

PubMed Central

Demir, Emek; Cary, Michael P.; Paley, Suzanne; Fukuda, Ken; Lemer, Christian; Vastrik, Imre; Wu, Guanming; D’Eustachio, Peter; Schaefer, Carl; Luciano, Joanne; Schacherer, Frank; Martinez-Flores, Irma; Hu, Zhenjun; Jimenez-Jacinto, Veronica; Joshi-Tope, Geeta; Kandasamy, Kumaran; Lopez-Fuentes, Alejandra C.; Mi, Huaiyu; Pichler, Elgar; Rodchenkov, Igor; Splendiani, Andrea; Tkachev, Sasha; Zucker, Jeremy; Gopinath, Gopal; Rajasimha, Harsha; Ramakrishnan, Ranjani; Shah, Imran; Syed, Mustafa; Anwar, Nadia; Babur, Ozgun; Blinov, Michael; Brauner, Erik; Corwin, Dan; Donaldson, Sylva; Gibbons, Frank; Goldberg, Robert; Hornbeck, Peter; Luna, Augustin; Murray-Rust, Peter; Neumann, Eric; Reubenacker, Oliver; Samwald, Matthias; van Iersel, Martijn; Wimalaratne, Sarala; Allen, Keith; Braun, Burk; Whirl-Carrillo, Michelle; Dahlquist, Kam; Finney, Andrew; Gillespie, Marc; Glass, Elizabeth; Gong, Li; Haw, Robin; Honig, Michael; Hubaut, Olivier; Kane, David; Krupa, Shiva; Kutmon, Martina; Leonard, Julie; Marks, Debbie; Merberg, David; Petri, Victoria; Pico, Alex; Ravenscroft, Dean; Ren, Liya; Shah, Nigam; Sunshine, Margot; Tang, Rebecca; Whaley, Ryan; Letovksy, Stan; Buetow, Kenneth H.; Rzhetsky, Andrey; Schachter, Vincent; Sobral, Bruno S.; Dogrusoz, Ugur; McWeeney, Shannon; Aladjem, Mirit; Birney, Ewan; Collado-Vides, Julio; Goto, Susumu; Hucka, Michael; Le Novère, Nicolas; Maltsev, Natalia; Pandey, Akhilesh; Thomas, Paul; Wingender, Edgar; Karp, Peter D.; Sander, Chris; Bader, Gary D.

2010-01-01

BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery. PMID:20829833

CCR researchers identify pathway critical for preventing premature aging | Center for Cancer Research

Cancer.gov

Hutchinson-Gilford progeria syndrome (HGPS) is a rare, fatal disease in which patients age prematurely. To identify primary HGPS driver mechanisms, Nard Kubben, Ph.D., a Research Fellow in the laboratory of Tom Misteli, Ph.D., in CCR’s Laboratory of Receptor Biology and Gene Expression, and colleagues in the NCI High-throughput Imaging Facility developed an imaging-based

Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation.

PubMed

Godec, Jernej; Tan, Yan; Liberzon, Arthur; Tamayo, Pablo; Bhattacharya, Sanchita; Butte, Atul J; Mesirov, Jill P; Haining, W Nicholas

2016-01-19

Gene-expression profiling has become a mainstay in immunology, but subtle changes in gene networks related to biological processes are hard to discern when comparing various datasets. For instance, conservation of the transcriptional response to sepsis in mouse models and human disease remains controversial. To improve transcriptional analysis in immunology, we created ImmuneSigDB: a manually annotated compendium of ∼5,000 gene-sets from diverse cell states, experimental manipulations, and genetic perturbations in immunology. Analysis using ImmuneSigDB identified signatures induced in activated myeloid cells and differentiating lymphocytes that were highly conserved between humans and mice. Sepsis triggered conserved patterns of gene expression in humans and mouse models. However, we also identified species-specific biological processes in the sepsis transcriptional response: although both species upregulated phagocytosis-related genes, a mitosis signature was specific to humans. ImmuneSigDB enables granular analysis of transcriptomic data to improve biological understanding of immune processes of the human and mouse immune systems. Copyright © 2016 Elsevier Inc. All rights reserved.

Pathway Analysis of Metabolic Syndrome Using a Genome-Wide Association Study of Korea Associated Resource (KARE) Cohorts.

PubMed

Shim, Unjin; Kim, Han-Na; Sung, Yeon-Ah; Kim, Hyung-Lae

2014-12-01

Metabolic syndrome (MetS) is a complex disorder related to insulin resistance, obesity, and inflammation. Genetic and environmental factors also contribute to the development of MetS, and through genome-wide association studies (GWASs), important susceptibility loci have been identified. However, GWASs focus more on individual single-nucleotide polymorphisms (SNPs), explaining only a small portion of genetic heritability. To overcome this limitation, pathway analyses are being applied to GWAS datasets. The aim of this study is to elucidate the biological pathways involved in the pathogenesis of MetS through pathway analysis. Cohort data from the Korea Associated Resource (KARE) was used for analysis, which include 8,842 individuals (age, 52.2 ± 8.9 years; body mass index, 24.6 ± 3.2 kg/m(2)). A total of 312,121 autosomal SNPs were obtained after quality control. Pathway analysis was conducted using Meta-analysis Gene-Set Enrichment of Variant Associations (MAGENTA) to discover the biological pathways associated with MetS. In the discovery phase, SNPs from chromosome 12, including rs11066280, rs2074356, and rs12229654, were associated with MetS (p < 5 × 10(-6)), and rs11066280 satisfied the Bonferroni-corrected cutoff (unadjusted p < 1.38 × 10(-7), Bonferroni-adjusted p < 0.05). Through pathway analysis, biological pathways, including electron carrier activity, signaling by platelet-derived growth factor (PDGF), the mitogen-activated protein kinase kinase kinase cascade, PDGF binding, peroxisome proliferator-activated receptor (PPAR) signaling, and DNA repair, were associated with MetS. Through pathway analysis of MetS, pathways related with PDGF, mitogen-activated protein kinase, and PPAR signaling, as well as nucleic acid binding, protein secretion, and DNA repair, were identified. Further studies will be needed to clarify the genetic pathogenesis leading to MetS.

A Western Blot-based Investigation of the Yeast Secretory Pathway Designed for an Intermediate-Level Undergraduate Cell Biology Laboratory

ERIC Educational Resources Information Center

Hood-DeGrenier, Jennifer K.

2008-01-01

The movement of newly synthesized proteins through the endomembrane system of eukaryotic cells, often referred to generally as the secretory pathway, is a topic covered in most intermediate-level undergraduate cell biology courses. An article previously published in this journal described a laboratory exercise in which yeast mutants defective in…

Differentiating pathway-specific from nonspecific effects in high-throughput toxicity data: A foundation for prioritizing adverse outcome pathway development

EPA Science Inventory

The U.S. Environmental Protection Agency’s ToxCast program has screened thousands of chemicals for biological activity, primarily using high-throughput in vitro bioassays. Adverse outcome pathways (AOPs) offer a means to link pathway-specific biological activities with potential ...

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

Hierarchical modularization of biochemical pathways using fuzzy-c means clustering.

PubMed

de Luis Balaguer, Maria A; Williams, Cranos M

2014-08-01

Biological systems that are representative of regulatory, metabolic, or signaling pathways can be highly complex. Mathematical models that describe such systems inherit this complexity. As a result, these models can often fail to provide a path toward the intuitive comprehension of these systems. More coarse information that allows a perceptive insight of the system is sometimes needed in combination with the model to understand control hierarchies or lower level functional relationships. In this paper, we present a method to identify relationships between components of dynamic models of biochemical pathways that reside in different functional groups. We find primary relationships and secondary relationships. The secondary relationships reveal connections that are present in the system, which current techniques that only identify primary relationships are unable to show. We also identify how relationships between components dynamically change over time. This results in a method that provides the hierarchy of the relationships among components, which can help us to understand the low level functional structure of the system and to elucidate potential hierarchical control. As a proof of concept, we apply the algorithm to the epidermal growth factor signal transduction pathway, and to the C3 photosynthesis pathway. We identify primary relationships among components that are in agreement with previous computational decomposition studies, and identify secondary relationships that uncover connections among components that current computational approaches were unable to reveal.

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

PubMed

Cordell, Heather J; Han, Younghun; Mells, George F; Li, Yafang; Hirschfield, Gideon M; Greene, Casey S; Xie, Gang; Juran, Brian D; Zhu, Dakai; Qian, David C; Floyd, James A B; Morley, Katherine I; Prati, Daniele; Lleo, Ana; Cusi, Daniele; Gershwin, M Eric; Anderson, Carl A; Lazaridis, Konstantinos N; Invernizzi, Pietro; Seldin, Michael F; Sandford, Richard N; Amos, Christopher I; Siminovitch, Katherine A

2015-09-22

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist.

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

Morphological covariance in anatomical MRI scans can identify discrete neural pathways in the brain and their disturbances in persons with neuropsychiatric disorders.

PubMed

Bansal, Ravi; Hao, Xuejun; Peterson, Bradley S

2015-05-01

We hypothesize that coordinated functional activity within discrete neural circuits induces morphological organization and plasticity within those circuits. Identifying regions of morphological covariation that are independent of morphological covariation in other regions therefore may therefore allow us to identify discrete neural systems within the brain. Comparing the magnitude of these variations in individuals who have psychiatric disorders with the magnitude of variations in healthy controls may allow us to identify aberrant neural pathways in psychiatric illnesses. We measured surface morphological features by applying nonlinear, high-dimensional warping algorithms to manually defined brain regions. We transferred those measures onto the surface of a unit sphere via conformal mapping and then used spherical wavelets and their scaling coefficients to simplify the data structure representing these surface morphological features of each brain region. We used principal component analysis (PCA) to calculate covariation in these morphological measures, as represented by their scaling coefficients, across several brain regions. We then assessed whether brain subregions that covaried in morphology, as identified by large eigenvalues in the PCA, identified specific neural pathways of the brain. To do so, we spatially registered the subnuclei for each eigenvector into the coordinate space of a Diffusion Tensor Imaging dataset; we used these subnuclei as seed regions to track and compare fiber pathways with known fiber pathways identified in neuroanatomical atlases. We applied these procedures to anatomical MRI data in a cohort of 82 healthy participants (42 children, 18 males, age 10.5 ± 2.43 years; 40 adults, 22 males, age 32.42 ± 10.7 years) and 107 participants with Tourette's Syndrome (TS) (71 children, 59 males, age 11.19 ± 2.2 years; 36 adults, 21 males, age 37.34 ± 10.9 years). We evaluated the construct validity of the identified covariation in morphology

Quantitative Biology of Exercise-Induced Signal Transduction Pathways.

PubMed

Liu, Timon Cheng-Yi; Liu, Gang; Hu, Shao-Juan; Zhu, Ling; Yang, Xiang-Bo; Zhang, Quan-Guang

2017-01-01

Exercise is essential in regulating energy metabolism. Exercise activates cellular, molecular, and biochemical pathways with regulatory roles in training response adaptation. Among them, endurance/strength training of an individual has been shown to activate its respective signal transduction pathways in skeletal muscle. This was further studied from the viewpoint of quantitative difference (QD). For the mean values, [Formula: see text], of two sets of data, their QD is defined as [Formula: see text] ([Formula: see text]). The function-specific homeostasis (FSH) of a function of a biosystem is a negative-feedback response of the biosystem to maintain the function-specific conditions inside the biosystem so that the function is perfectly performed. A function in/far from its FSH is called a normal/dysfunctional function. A cellular normal function can resist the activation of other signal transduction pathways so that there are normal function-specific signal transduction pathways which full activation maintains the normal function. An acute endurance/strength training may be dysfunctional, but its regular training may be normal. The normal endurance/strength training of an individual may resist the activation of other signal transduction pathways in skeletal muscle so that there may be normal endurance/strength training-specific signal transduction pathways (NEPs/NSPs) in skeletal muscle. The endurance/strength training may activate NSPs/NEPs, but the QD from the control is smaller than 0.80. The simultaneous activation of both NSPs and NEPs may enhance their respective activation, and the QD from the control is larger than 0.80. The low level laser irradiation pretreatment of rats may promote the activation of NSPs in endurance training skeletal muscle. There may be NEPs/NSPs in skeletal muscle trained by normal endurance/strength training.

Aligning Metabolic Pathways Exploiting Binary Relation of Reactions.

PubMed

Huang, Yiran; Zhong, Cheng; Lin, Hai Xiang; Huang, Jing

2016-01-01

Metabolic pathway alignment has been widely used to find one-to-one and/or one-to-many reaction mappings to identify the alternative pathways that have similar functions through different sets of reactions, which has important applications in reconstructing phylogeny and understanding metabolic functions. The existing alignment methods exhaustively search reaction sets, which may become infeasible for large pathways. To address this problem, we present an effective alignment method for accurately extracting reaction mappings between two metabolic pathways. We show that connected relation between reactions can be formalized as binary relation of reactions in metabolic pathways, and the multiplications of zero-one matrices for binary relations of reactions can be accomplished in finite steps. By utilizing the multiplications of zero-one matrices for binary relation of reactions, we efficiently obtain reaction sets in a small number of steps without exhaustive search, and accurately uncover biologically relevant reaction mappings. Furthermore, we introduce a measure of topological similarity of nodes (reactions) by comparing the structural similarity of the k-neighborhood subgraphs of the nodes in aligning metabolic pathways. We employ this similarity metric to improve the accuracy of the alignments. The experimental results on the KEGG database show that when compared with other state-of-the-art methods, in most cases, our method obtains better performance in the node correctness and edge correctness, and the number of the edges of the largest common connected subgraph for one-to-one reaction mappings, and the number of correct one-to-many reaction mappings. Our method is scalable in finding more reaction mappings with better biological relevance in large metabolic pathways.

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology

PubMed Central

Yue, Zongliang; Zheng, Qi; Neylon, Michael T; Yoo, Minjae; Shin, Jimin; Zhao, Zhiying; Tan, Aik Choon

2018-01-01

Abstract Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug–gene, miRNA–gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/. PMID:29126216

A whole organism screen identifies novel regulators of fat storage

PubMed Central

Lemieux, George A.; Liu, Jason; Mayer, Nasima; Bainton, Roland J.; Ashrafi, Kaveh; Werb, Zena

2011-01-01

The regulation of energy homeostasis integrates diverse biological processes ranging from behavior to metabolism and is linked fundamentally to numerous disease states. To identify new molecules that can bypass homeostatic compensatory mechanisms of energy balance in intact animals, we screened for small molecule modulators of C. elegans fat content. We report on several molecules that modulate fat storage without obvious deleterious effects on feeding, growth, and reproduction. A subset of these compounds also altered fat storage in mammalian and insect cell culture. We found that one of the newly identified compounds exerts its effects in C. elegans through a pathway that requires novel functions of an AMP-activated kinase catalytic subunit and a transcription factor previously unassociated with fat regulation. Thus, our strategy identifies small molecules that are effective within the context of intact animals and reveals relationships between new pathways that operate across phyla to influence energy homeostasis. PMID:21390037

Clinical and Biological Relevance of Genomic Heterogeneity in Chronic Lymphocytic Leukemia

PubMed Central

Friedman, Daphne R.; Lucas, Joseph E.; Weinberg, J. Brice

2013-01-01

Background Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients’ leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL. Methods To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups. Results Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes. Conclusions Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways. PMID:23468975

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.

A theoretical framework for biological control of soil-borne plant pathogens: Identifying effective strategies.

PubMed

Cunniffe, Nik J; Gilligan, Christopher A

2011-06-07

We develop and analyse a flexible compartmental model of the interaction between a plant host, a soil-borne pathogen and a microbial antagonist, for use in optimising biological control. By extracting invasion and persistence thresholds of host, pathogen and biological control agent, performing an equilibrium analysis, and numerical investigation of sensitivity to parameters and initial conditions, we determine criteria for successful biological control. We identify conditions for biological control (i) to prevent a pathogen entering a system, (ii) to eradicate a pathogen that is already present and, if that is not possible, (iii) to reduce the density of the pathogen. Control depends upon the epidemiology of the pathogen and how efficiently the antagonist can colonise particular habitats (i.e. healthy tissue, infected tissue and/or soil-borne inoculum). A sharp transition between totally effective control (i.e. eradication of the pathogen) and totally ineffective control can follow slight changes in biologically interpretable parameters or to the initial amounts of pathogen and biological control agent present. Effective biological control requires careful matching of antagonists to pathosystems. For preventative/eradicative control, antagonists must colonise susceptible hosts. However, for reduction in disease prevalence, the range of habitat is less important than the antagonist's bulking-up efficiency. Copyright © 2011 Elsevier Ltd. All rights reserved.

Proteomic analysis of Medulloblastoma reveals functional biology with translational potential.

PubMed

Rivero-Hinojosa, Samuel; Lau, Ling San; Stampar, Mojca; Staal, Jerome; Zhang, Huizhen; Gordish-Dressman, Heather; Northcott, Paul A; Pfister, Stefan M; Taylor, Michael D; Brown, Kristy J; Rood, Brian R

2018-06-07

Genomic characterization has begun to redefine diagnostic classifications of cancers. However, it remains a challenge to infer disease phenotypes from genomic alterations alone. To help realize the promise of genomics, we have performed a quantitative proteomics investigation using Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and 41 tissue samples spanning the 4 genomically based subgroups of medulloblastoma and control cerebellum. We have identified and quantitated thousands of proteins across these groups and find that we are able to recapitulate the genomic subgroups based upon subgroup restricted and differentially abundant proteins while also identifying subgroup specific protein isoforms. Integrating our proteomic measurements with genomic data, we calculate a poor correlation between mRNA and protein abundance. Using EPIC 850 k methylation array data on the same tissues, we also investigate the influence of copy number alterations and DNA methylation on the proteome in an attempt to characterize the impact of these genetic features on the proteome. Reciprocally, we are able to use the proteome to identify which genomic alterations result in altered protein abundance and thus are most likely to impact biology. Finally, we are able to assemble protein-based pathways yielding potential avenues for clinical intervention. From these, we validate the EIF4F cap-dependent translation pathway as a novel druggable pathway in medulloblastoma. Thus, quantitative proteomics complements genomic platforms to yield a more complete understanding of functional tumor biology and identify novel therapeutic targets for medulloblastoma.

Mining and integration of pathway diagrams from imaging data.

PubMed

Kozhenkov, Sergey; Baitaluk, Michael

2012-03-01

Pathway diagrams from PubMed and World Wide Web (WWW) contain valuable highly curated information difficult to reach without tools specifically designed and customized for the biological semantics and high-content density of the images. There is currently no search engine or tool that can analyze pathway images, extract their pathway components (molecules, genes, proteins, organelles, cells, organs, etc.) and indicate their relationships. Here, we describe a resource of pathway diagrams retrieved from article and web-page images through optical character recognition, in conjunction with data mining and data integration methods. The recognized pathways are integrated into the BiologicalNetworks research environment linking them to a wealth of data available in the BiologicalNetworks' knowledgebase, which integrates data from >100 public data sources and the biomedical literature. Multiple search and analytical tools are available that allow the recognized cellular pathways, molecular networks and cell/tissue/organ diagrams to be studied in the context of integrated knowledge, experimental data and the literature. BiologicalNetworks software and the pathway repository are freely available at www.biologicalnetworks.org. Supplementary data are available at Bioinformatics online.

Multiple biological pathways link cognitive lifestyle to protection from dementia.

PubMed

Valenzuela, Michael J; Matthews, Fiona E; Brayne, Carol; Ince, Paul; Halliday, Glenda; Kril, Jillian J; Dalton, Marshall A; Richardson, Kathryn; Forster, Gill; Sachdev, Perminder S

2012-05-01

An active cognitive lifestyle is linked to diminished dementia risk, but the underlying mechanisms are poorly understood. Potential mechanisms include disease modification, neuroprotection, and compensation. Prospective, population-based brain series provide the rare opportunity to test the plausibility of these mechanisms in humans. Participants came from the United Kingdom Medical Research Council Cognitive Function and Ageing Study, comprising 13,004 individuals aged over 65 years and followed for 14 years. In study 1, a Cognitive Lifestyle Score (CLS) was computed on all Cognitive Function and Ageing Study subjects to define low, middle, and high groups. By August 2004, 329 individuals with CLS data had come to autopsy and underwent Consortium to Establish a Registry of Alzheimer's Disease assessment. Study 2 involved more detailed quantitative histology in the hippocampus and Brodmann area 9 in 72 clinically matched individuals with high and low CLS. CLS groups did not differ on several Alzheimer disease neuropathologic measures; however, high CLS men had less cerebrovascular disease after accounting for vascular risk factors, and women had greater brain weight. No group differences were evident in hippocampal neuronal density. In Brodmann area 9, cognitively active individuals had significantly greater neuronal density, as well as correlated increases in cortical thickness. An active cognitive lifestyle was associated with protection from cerebrovascular disease in men, but there was no evidence for Alzheimer disease modification or hippocampal neuroprotection. Men and women both exhibited neurotrophic changes in the prefrontal lobe linked to cognitive lifestyle, consistent with a compensatory process. Lifespan complex cognitive activity may therefore protect against dementia through multiple biological pathways. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Genome-scale biological models for industrial microbial systems.

PubMed

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Pathways Impacted by Genomic Alterations in Pulmonary Carcinoid Tumors.

PubMed

Asiedu, Michael K; Thomas, Charles F; Dong, Jie; Schulte, Sandra C; Khadka, Prasidda; Sun, Zhifu; Kosari, Farhad; Jen, Jin; Molina, Julian; Vasmatzis, George; Kuang, Ray; Aubry, Marie Christine; Yang, Ping; Wigle, Dennis A

2018-04-01

Purpose: Pulmonary carcinoid tumors account for up to 5% of all lung malignancies in adults, comprise 30% of all carcinoid malignancies, and are defined histologically as typical carcinoid (TC) and atypical carcinoid (AC) tumors. The role of specific genomic alterations in the pathogenesis of pulmonary carcinoid tumors remains poorly understood. We sought to identify genomic alterations and pathways that are deregulated in these tumors to find novel therapeutic targets for pulmonary carcinoid tumors. Experimental Design: We performed integrated genomic analysis of carcinoid tumors comprising whole genome and exome sequencing, mRNA expression profiling and SNP genotyping of specimens from normal lung, TC and AC, and small cell lung carcinoma (SCLC) to fully represent the lung neuroendocrine tumor spectrum. Results: Analysis of sequencing data found recurrent mutations in cancer genes including ATP1A2, CNNM1, MACF1, RAB38, NF1, RAD51C, TAF1L, EPHB2, POLR3B , and AGFG1 The mutated genes are involved in biological processes including cellular metabolism, cell division cycle, cell death, apoptosis, and immune regulation. The top most significantly mutated genes were TMEM41B, DEFB127, WDYHV1, and TBPL1 Pathway analysis of significantly mutated and cancer driver genes implicated MAPK/ERK and amyloid beta precursor protein (APP) pathways whereas analysis of CNV and gene expression data suggested deregulation of the NF-κB and MAPK/ERK pathways. The mutation signature was predominantly C>T and T>C transitions with a minor contribution of T>G transversions. Conclusions: This study identified mutated genes affecting cancer relevant pathways and biological processes that could provide opportunities for developing targeted therapies for pulmonary carcinoid tumors. Clin Cancer Res; 24(7); 1691-704. ©2018 AACR . ©2018 American Association for Cancer Research.

Differentiating pathway-specific from non-specific effects in high-throughput toxicity data: A foundation for prioritizing adverse outcome pathway development

EPA Science Inventory

The U.S. Environmental Protection Agency’s ToxCast program has screened thousands of chemicals for biological activity, primarily using high-throughput in vitro bioassays. Adverse outcome pathways (AOPs) offer a means to link pathway-specific biological activities with pote...

Developmental Pathways Are Blueprints for Designing Successful Crops

PubMed Central

Trevaskis, Ben

2018-01-01

Genes controlling plant development have been studied in multiple plant systems. This has provided deep insights into conserved genetic pathways controlling core developmental processes including meristem identity, phase transitions, determinacy, stem elongation, and branching. These pathways control plant growth patterns and are fundamentally important to crop biology and agriculture. This review describes the conserved pathways that control plant development, using Arabidopsis as a model. Historical examples of how plant development has been altered through selection to improve crop performance are then presented. These examples, drawn from diverse crops, show how the genetic pathways controlling development have been modified to increase yield or tailor growth patterns to suit local growing environments or specialized crop management practices. Strategies to apply current progress in genomics and developmental biology to future crop improvement are then discussed within the broader context of emerging trends in plant breeding. The ways that knowledge of developmental processes and understanding of gene function can contribute to crop improvement, beyond what can be achieved by selection alone, are emphasized. These include using genome re-sequencing, mutagenesis, and gene editing to identify or generate novel variation in developmental genes. The expanding scope for comparative genomics, the possibility to engineer new developmental traits and new approaches to resolve gene–gene or gene–environment interactions are also discussed. Finally, opportunities to integrate fundamental research and crop breeding are highlighted. PMID:29922318

Developmental Pathways Are Blueprints for Designing Successful Crops.

PubMed

Trevaskis, Ben

2018-01-01

Genes controlling plant development have been studied in multiple plant systems. This has provided deep insights into conserved genetic pathways controlling core developmental processes including meristem identity, phase transitions, determinacy, stem elongation, and branching. These pathways control plant growth patterns and are fundamentally important to crop biology and agriculture. This review describes the conserved pathways that control plant development, using Arabidopsis as a model. Historical examples of how plant development has been altered through selection to improve crop performance are then presented. These examples, drawn from diverse crops, show how the genetic pathways controlling development have been modified to increase yield or tailor growth patterns to suit local growing environments or specialized crop management practices. Strategies to apply current progress in genomics and developmental biology to future crop improvement are then discussed within the broader context of emerging trends in plant breeding. The ways that knowledge of developmental processes and understanding of gene function can contribute to crop improvement, beyond what can be achieved by selection alone, are emphasized. These include using genome re-sequencing, mutagenesis, and gene editing to identify or generate novel variation in developmental genes. The expanding scope for comparative genomics, the possibility to engineer new developmental traits and new approaches to resolve gene-gene or gene-environment interactions are also discussed. Finally, opportunities to integrate fundamental research and crop breeding are highlighted.

Psychological intervention and health outcomes among women treated for breast cancer: a review of stress pathways and biological mediators

PubMed Central

McGregor, Bonnie A.; Antoni, Michael H.

2009-01-01

Breast cancer is a common cancer among American women. The diagnosis, treatment, and the challenges of survivorship all have potential to increase women’s levels of distress to levels that might influence their adaptation and possibly the course of disease. Psychological distress can influence tumor progression via many different pathways (e.g., genetic changes, immune surveillance, pro-angiogenic processes). Psychological intervention has been shown to facilitate psychological adaptation to breast cancer. But can psychological intervention influence cancer relevant biological outcomes among breast cancer survivors? We review the literature on how psychological intervention can influence cancer-relevant biological outcomes among breast cancer patients. We limited the present review to randomized controlled trials reported in the past 6 years that tested the effects of psychological intervention on biological dependent variables among patients with non-metatstic breast cancer. There are data to suggest that psychological intervention can influence neuroendocrine (e.g. cortisol) and immune function indicators, especially lymphocyte proliferation and TH1 cytokine production. Future psychological intervention studies should also focus on more newly discovered stress-tumor pathways (e.g., neuroendocrine processes promoting tumor growth and metastasis) and follow larger cohorts of the most vulnerable patients over longer periods to evaluate the lasting effects of these interventions on health and quality of life and their underlying biobehavioral mechanisms. PMID:18778768

Synthetic biology approaches to fluorinated polyketides

PubMed Central

Thuronyi, Benjamin W.; Chang, Michelle C. Y.

2016-01-01

Conspectus The catalytic diversity of living systems offers a broad range of opportunities for developing new methods to produce small molecule targets such as fuels, materials, and pharmaceuticals. In addition to providing cost-effective and renewable methods for large-scale commercial processes, the exploration of the unusual chemical phenotypes found in living organisms can also enable the expansion of chemical space for discovery of novel function by combining orthogonal attributes from both synthetic and biological chemistry. In this context, we have focused on the development of new fluorine chemistry using synthetic biology approaches. While fluorine has become an important feature in compounds of synthetic origin, the scope of biological fluorine chemistry in living systems is limited, with fewer than 20 organofluorine natural products identified to date. In order to expand the diversity of biosynthetically accessible organofluorines, we have begun to develop methods for the site-selective introduction of fluorine into complex natural products by engineering biosynthetic machinery to incorporate fluorinated building blocks. To gain insight into how both enzyme active sites and metabolic pathways can be evolved to manage and select for fluorinated compounds, we have studied one of the only characterized natural hosts for organofluorine biosynthesis, the soil microbe Streptomyces cattleya. This information provides a template for designing engineered organofluorine enzymes, pathways, and hosts and has allowed us to initiate construction of enzymatic and cellular pathways for the production of fluorinated polyketides. PMID:25719427

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database.

PubMed

Ikeda, Shun; Abe, Takashi; Nakamura, Yukiko; Kibinge, Nelson; Hirai Morita, Aki; Nakatani, Atsushi; Ono, Naoaki; Ikemura, Toshimichi; Nakamura, Kensuke; Altaf-Ul-Amin, Md; Kanaya, Shigehiko

2013-05-01

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Chemical and Biological Defense: Designated Entity Needed to Identify, Align, and Manage DOD’s Infrastructure

DTIC Science & Technology

2015-06-01

Designated Leader, GAO-10-645 (Washington, D.C.: June 30, 2010). 35See GAO, Biological Defense: DOD Has Strengthened Coordination on Medical... on track to be designated a Leadership in Energy and Environmental Design facility. metabolic poisons, and pulmonary toxicants; nerve agent...CHEMICAL AND BIOLOGICAL DEFENSE Designated Entity Needed to Identify, Align, and Manage DOD’s Infrastructure

Modeling Protein Expression and Protein Signaling Pathways

PubMed Central

Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

2015-01-01

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

Putative adverse outcome pathways relevant to neurotoxicity

PubMed Central

Bal-Price, Anna; Crofton, Kevin M.; Sachana, Magdalini; Shafer, Timothy J.; Behl, Mamta; Forsby, Anna; Hargreaves, Alan; Landesmann, Brigitte; Lein, Pamela J.; Louisse, Jochem; Monnet-Tschudi, Florianne; Paini, Alicia; Rolaki, Alexandra; Schrattenholz, André; Suñol, Cristina; van Thriel, Christoph; Whelan, Maurice; Fritsche, Ellen

2016-01-01

The Adverse Outcome Pathway (AOP) framework provides a template that facilitates understanding of complex biological systems and the pathways of toxicity that result in adverse outcomes (AOs). The AOP starts with an molecular initiating event (MIE) in which a chemical interacts with a biological target(s), followed by a sequential series of KEs, which are cellular, anatomical, and/or functional changes in biological processes, that ultimately result in an AO manifest in individual organisms and populations. It has been developed as a tool for a knowledge-based safety assessment that relies on understanding mechanisms of toxicity, rather than simply observing its adverse outcome. A large number of cellular and molecular processes are known to be crucial to proper development and function of the central (CNS) and peripheral nervous systems (PNS). However, there are relatively few examples of well-documented pathways that include causally linked MIEs and KEs that result in adverse outcomes in the CNS or PNS. As a first step in applying the AOP framework to adverse health outcomes associated with exposure to exogenous neurotoxic substances, the EU Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) organized a workshop (March 2013, Ispra, Italy) to identify potential AOPs relevant to neurotoxic and developmental neurotoxic outcomes. Although the AOPs outlined during the workshop are not fully described, they could serve as a basis for further, more detailed AOP development and evaluation that could be useful to support human health risk assessment in a variety of ways. PMID:25605028

Application of Adverse Outcome Pathways to U.S. EPA’s Endocrine Disruptor Screening Program

PubMed Central

Noyes, Pamela D.; Casey, Warren M.; Dix, David J.

2017-01-01

Background: The U.S. EPA’s Endocrine Disruptor Screening Program (EDSP) screens and tests environmental chemicals for potential effects in estrogen, androgen, and thyroid hormone pathways, and it is one of the only regulatory programs designed around chemical mode of action. Objectives: This review describes the EDSP’s use of adverse outcome pathway (AOP) and toxicity pathway frameworks to organize and integrate diverse biological data for evaluating the endocrine activity of chemicals. Using these frameworks helps to establish biologically plausible links between endocrine mechanisms and apical responses when those end points are not measured in the same assay. Results: Pathway frameworks can facilitate a weight of evidence determination of a chemical’s potential endocrine activity, identify data gaps, aid study design, direct assay development, and guide testing strategies. Pathway frameworks also can be used to evaluate the performance of computational approaches as alternatives for low-throughput and animal-based assays and predict downstream key events. In cases where computational methods can be validated based on performance, they may be considered as alternatives to specific assays or end points. Conclusions: A variety of biological systems affect apical end points used in regulatory risk assessments, and without mechanistic data, an endocrine mode of action cannot be determined. Because the EDSP was designed to consider mode of action, toxicity pathway and AOP concepts are a natural fit. Pathway frameworks have diverse applications to endocrine screening and testing. An estrogen pathway example is presented, and similar approaches are being used to evaluate alternative methods and develop predictive models for androgen and thyroid pathways. https://doi.org/10.1289/EHP1304 PMID:28934726

A new synthetic biology approach allows transfer of an entire metabolic pathway from a medicinal plant to a biomass crop.

PubMed

Fuentes, Paulina; Zhou, Fei; Erban, Alexander; Karcher, Daniel; Kopka, Joachim; Bock, Ralph

2016-06-14

Artemisinin-based therapies are the only effective treatment for malaria, the most devastating disease in human history. To meet the growing demand for artemisinin and make it accessible to the poorest, an inexpensive and rapidly scalable production platform is urgently needed. Here we have developed a new synthetic biology approach, combinatorial supertransformation of transplastomic recipient lines (COSTREL), and applied it to introduce the complete pathway for artemisinic acid, the precursor of artemisinin, into the high-biomass crop tobacco. We first introduced the core pathway of artemisinic acid biosynthesis into the chloroplast genome. The transplastomic plants were then combinatorially supertransformed with cassettes for all additional enzymes known to affect flux through the artemisinin pathway. By screening large populations of COSTREL lines, we isolated plants that produce more than 120 milligram artemisinic acid per kilogram biomass. Our work provides an efficient strategy for engineering complex biochemical pathways into plants and optimizing the metabolic output.

A new synthetic biology approach allows transfer of an entire metabolic pathway from a medicinal plant to a biomass crop

PubMed Central

Fuentes, Paulina; Zhou, Fei; Erban, Alexander; Karcher, Daniel; Kopka, Joachim; Bock, Ralph

2016-01-01

Artemisinin-based therapies are the only effective treatment for malaria, the most devastating disease in human history. To meet the growing demand for artemisinin and make it accessible to the poorest, an inexpensive and rapidly scalable production platform is urgently needed. Here we have developed a new synthetic biology approach, combinatorial supertransformation of transplastomic recipient lines (COSTREL), and applied it to introduce the complete pathway for artemisinic acid, the precursor of artemisinin, into the high-biomass crop tobacco. We first introduced the core pathway of artemisinic acid biosynthesis into the chloroplast genome. The transplastomic plants were then combinatorially supertransformed with cassettes for all additional enzymes known to affect flux through the artemisinin pathway. By screening large populations of COSTREL lines, we isolated plants that produce more than 120 milligram artemisinic acid per kilogram biomass. Our work provides an efficient strategy for engineering complex biochemical pathways into plants and optimizing the metabolic output. DOI: http://dx.doi.org/10.7554/eLife.13664.001 PMID:27296645