Science.gov

Sample records for coexpressed gene networks

  1. Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network.

    PubMed

    Jiang, Xue; Zhang, Han; Quan, Xiongwen

    2016-01-01

    Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

  2. Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network

    PubMed Central

    Quan, Xiongwen

    2016-01-01

    Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets. PMID:28042568

  3. COXPRESdb: a database of coexpressed gene networks in mammals.

    PubMed

    Obayashi, Takeshi; Hayashi, Shinpei; Shibaoka, Masayuki; Saeki, Motoshi; Ohta, Hiroyuki; Kinoshita, Kengo

    2008-01-01

    A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein-protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species.

  4. Gene Coexpression Network Topology of Cardiac Development, Hypertrophy, and Failure

    PubMed Central

    Dewey, Frederick E.; Perez, Marco V.; Wheeler, Matthew T.; Watt, Clifton; Spin, Joshua; Langfelder, Peter; Horvath, Stephen; Hannenhalli, Sridhar; Cappola, Thomas P.; Ashley, Euan A.

    2011-01-01

    Background Network analysis techniques allow a more accurate reflection of underlying systems biology to be realized than traditional unidimensional molecular biology approaches. Here, using gene coexpression network analysis, we define the gene expression network topology of cardiac hypertrophy and failure and the extent of recapitulation of fetal gene expression programs in failing and hypertrophied adult myocardium. Methods and Results We assembled all myocardial transcript data in the Gene Expression Omnibus (n = 1617). Since hierarchical analysis revealed species had primacy over disease clustering, we focused this analysis on the most complete (murine) dataset (n = 478). Using gene coexpression network analysis, we derived functional modules, regulatory mediators and higher order topological relationships between genes and identified 50 gene co-expression modules in developing myocardium that were not present in normal adult tissue. We found that known gene expression markers of myocardial adaptation were members of upregulated modules but not hub genes. We identified ZIC2 as a novel transcription factor associated with coexpression modules common to developing and failing myocardium. Of 50 fetal gene co-expression modules, three (6%) were reproduced in hypertrophied myocardium and seven (14%) were reproduced in failing myocardium. One fetal module was common to both failing and hypertrophied myocardium. Conclusions Network modeling allows systems analysis of cardiovascular development and disease. While we did not find evidence for a global coordinated program of fetal gene expression in adult myocardial adaptation, our analysis revealed specific gene expression modules active during both development and disease and specific candidates for their regulation. PMID:21127201

  5. Multiscale Embedded Gene Co-expression Network Analysis

    PubMed Central

    Song, Won-Min; Zhang, Bin

    2015-01-01

    Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma. PMID:26618778

  6. Functional Module Analysis for Gene Coexpression Networks with Network Integration

    PubMed Central

    Zhang, Shuqin; Zhao, Hongyu

    2015-01-01

    Network has been a general tool for studying the complex interactions between different genes, proteins and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with 3 complete subgraphs, and 11 modules with 2 complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally. PMID:26451826

  7. Beyond Genomics: Studying Evolution with Gene Coexpression Networks.

    PubMed

    Ruprecht, Colin; Vaid, Neha; Proost, Sebastian; Persson, Staffan; Mutwil, Marek

    2017-04-01

    Understanding how genomes change as organisms become more complex is a central question in evolution. Molecular evolutionary studies typically correlate the appearance of genes and gene families with the emergence of biological pathways and morphological features. While such approaches are of great importance to understand how organisms evolve, they are also limited, as functionally related genes work together in contexts of dynamic gene networks. Since functionally related genes are often transcriptionally coregulated, gene coexpression networks present a resource to study the evolution of biological pathways. In this opinion article, we discuss recent developments in this field and how coexpression analyses can be merged with existing genomic approaches to transfer functional knowledge between species to study the appearance or extension of pathways.

  8. Random matrix analysis of localization properties of gene coexpression network.

    PubMed

    Jalan, Sarika; Solymosi, Norbert; Vattay, Gábor; Li, Baowen

    2010-04-01

    We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation ratio suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets: (a) The nondegenerate part that follows RMT. (b) The nondegenerate part, at both ends and at intermediate eigenvalues, which deviates from RMT and expected to contain information about important nodes in the network. (c) The degenerate part with zero eigenvalue, which fluctuates around RMT-predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties.

  9. Analysis of bHLH coding genes using gene co-expression network approach.

    PubMed

    Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok

    2016-07-01

    Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species.

  10. Investigating the Combinatory Effects of Biological Networks on Gene Co-expression

    PubMed Central

    Zhang, Cheng; Lee, Sunjae; Mardinoglu, Adil; Hua, Qiang

    2016-01-01

    Co-expressed genes often share similar functions, and gene co-expression networks have been widely used in studying the functionality of gene modules. Previous analysis indicated that genes are more likely to be co-expressed if they are either regulated by the same transcription factors, forming protein complexes or sharing similar topological properties in protein-protein interaction networks. Here, we reconstructed transcriptional regulatory and protein-protein networks for Saccharomyces cerevisiae using well-established databases, and we evaluated their co-expression activities using publically available gene expression data. Based on our network-dependent analysis, we found that genes that were co-regulated in the transcription regulatory networks and shared similar neighbors in the protein-protein networks were more likely to be co-expressed. Moreover, their biological functions were closely related. PMID:27445830

  11. Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer.

    PubMed

    Ivliev, Alexander E; 't Hoen, Peter A C; Borisevich, Dmitrii; Nikolsky, Yuri; Sergeeva, Marina G

    2016-01-01

    Gene coexpression network analysis is a powerful "data-driven" approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise "meta-analysis" framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.

  12. Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

    PubMed Central

    Ivliev, Alexander E.; ‘t Hoen, Peter A. C.; Borisevich, Dmitrii; Nikolsky, Yuri; Sergeeva, Marina G.

    2016-01-01

    Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research. PMID:27824868

  13. Differentially correlated genes in co-expression networks control phenotype transitions

    PubMed Central

    Thomas, Lina D.; Vyshenska, Dariia; Shulzhenko, Natalia; Yambartsev, Anatoly; Morgun, Andrey

    2016-01-01

    Background: Co-expression networks are a tool widely used for analysis of “Big Data” in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer). Methods: Co-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for two mus musculus datasets: B10.A strain (12 normal, 12 BcKO) and BALB/c strain (10 normal, 10 BcKO). Co-expression networks of cervical cancer (normal and cancer) were reconstructed using local partial correlation method for five datasets (total of 64 normal, 148 cancer). Differentially correlated pairs were identified along with the location of their genes in BcKO and in cancer networks. Minimum Shortest Path and Bi-partite Betweenness Centrality where statistically evaluated for differentially co-expressed genes in corresponding networks.    Results: We show that in B cell deficiency the differentially co-expressed genes are highly enriched with immunoglobulin genes (causal genes). In cancer we found that differentially co-expressed genes act as “bottlenecks” rather than causal drivers with most flows that come from the key driver genes to the peripheral genes passing through differentially co-expressed genes. Using in vitro knockdown experiments for two out of 14 differentially co-expressed genes found in cervical cancer (FGFR2 and CACYBP), we

  14. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

    PubMed Central

    Luo, Feng; Yang, Yunfeng; Zhong, Jianxin; Gao, Haichun; Khan, Latifur; Thompson, Dorothea K; Zhou, Jizhong

    2007-01-01

    Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the

  15. Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data

    PubMed Central

    2012-01-01

    Background Using gene co-expression analysis, researchers were able to predict clusters of genes with consistent functions that are relevant to cancer development and prognosis. We applied a weighted gene co-expression network (WGCN) analysis algorithm on glioblastoma multiforme (GBM) data obtained from the TCGA project and predicted a set of gene co-expression networks which are related to GBM prognosis. Methods We modified the Quasi-Clique Merger algorithm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-network in WGCN. Each sub-network is considered a set of features to separate patients into two groups using K-means algorithm. Survival times of the two groups are compared using log-rank test and Kaplan-Meier curves. Simulations using random sets of genes are carried out to determine the thresholds for log-rank test p-values for network selection. Sub-networks with p-values less than their corresponding thresholds were further merged into clusters based on overlap ratios (>50%). The functions for each cluster are analyzed using gene ontology enrichment analysis. Results Using the eQCM algorithm, we identified 8,124 sub-networks in the WGCN, out of which 170 sub-networks show p-values less than their corresponding thresholds. They were then merged into 16 clusters. Conclusions We identified 16 gene clusters associated with GBM prognosis using the eQCM algorithm. Our results not only confirmed previous findings including the importance of cell cycle and immune response in GBM, but also suggested important epigenetic events in GBM development and prognosis. PMID:22536863

  16. RiceFREND: a platform for retrieving coexpressed gene networks in rice.

    PubMed

    Sato, Yutaka; Namiki, Nobukazu; Takehisa, Hinako; Kamatsuki, Kaori; Minami, Hiroshi; Ikawa, Hiroshi; Ohyanagi, Hajime; Sugimoto, Kazuhiko; Itoh, Jun-Ichi; Antonio, Baltazar A; Nagamura, Yoshiaki

    2013-01-01

    Similarity of gene expression across a wide range of biological conditions can be efficiently used in characterization of gene function. We have constructed a rice gene coexpression database, RiceFREND (http://ricefrend.dna.affrc.go.jp/), to identify gene modules with similar expression profiles and provide a platform for more accurate prediction of gene functions. Coexpression analysis of 27 201 genes was performed against 815 microarray data derived from expression profiling of various organs and tissues at different developmental stages, mature organs throughout the growth from transplanting until harvesting in the field and plant hormone treatment conditions, using a single microarray platform. The database is provided with two search options, namely, 'single guide gene search' and 'multiple guide gene search' to efficiently retrieve information on coexpressed genes. A user-friendly web interface facilitates visualization and interpretation of gene coexpression networks in HyperTree, Cytoscape Web and Graphviz formats. In addition, analysis tools for identification of enriched Gene Ontology terms and cis-elements provide clue for better prediction of biological functions associated with the coexpressed genes. These features allow users to clarify gene functions and gene regulatory networks that could lead to a more thorough understanding of many complex agronomic traits.

  17. Identification of hub genes and pathways associated with retinoblastoma based on co-expression network analysis.

    PubMed

    Wang, Q L; Chen, X; Zhang, M H; Shen, Q H; Qin, Z M

    2015-12-08

    The objective of this paper was to identify hub genes and pathways associated with retinoblastoma using centrality analysis of the co-expression network and pathway-enrichment analysis. The co-expression network of retinoblastoma was constructed by weighted gene co-expression network analysis (WGCNA) based on differentially expressed (DE) genes, and clusters were obtained through the molecular complex detection (MCODE) algorithm. Degree centrality analysis of the co-expression network was performed to explore hub genes present in retinoblastoma. Pathway-enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Validation of hub gene expression in retinoblastoma was performed by reverse transcription-polymerase chain reaction (RT-PCR) analysis. The co-expression network based on 221 DE genes between retinoblastoma and normal controls consisted of 210 nodes and 3965 edges, and 5 clusters of the network were evaluated. By assessing the centrality analysis of the co-expression network, 21 hub genes were identified, such as SNORD115-41, RASSF2, and SNORD115-44. According to RT-PCR analysis, 16 of the 21 hub genes were differently expressed, including RASSF2 and CDCA7, and 5 were not differently expressed in retinoblastoma compared to normal controls. Pathway analysis showed that genes in 2 clusters were enriched in 3 pathways: purine metabolism, p53 signaling pathway, and melanogenesis. In this study, we successfully identified 16 hub genes and 3 pathways associated with retinoblastoma, which may be potential biomarkers for early detection and therapy for retinoblastoma.

  18. Construction of citrus gene coexpression networks from microarray data using random matrix theory.

    PubMed

    Du, Dongliang; Rawat, Nidhi; Deng, Zhanao; Gmitter, Fred G

    2015-01-01

    After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus.

  19. Construction of citrus gene coexpression networks from microarray data using random matrix theory

    PubMed Central

    Du, Dongliang; Rawat, Nidhi; Deng, Zhanao; Gmitter, Fred G.

    2015-01-01

    After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus. PMID:26504573

  20. Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery

    PubMed Central

    Kumari, Sapna; Nie, Jeff; Chen, Huann-Sheng; Ma, Hao; Stewart, Ron; Li, Xiang; Lu, Meng-Zhu; Taylor, William M.; Wei, Hairong

    2012-01-01

    Background Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. Methods and Results In this study, we compared eight gene association methods – Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson – and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. Conclusions We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction. PMID:23226279

  1. Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering

    PubMed Central

    McDowell, Ian C.; Zhao, Shiwen; Brown, Christopher D.; Engelhardt, Barbara E.

    2016-01-01

    Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues. PMID:27467526

  2. Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

    PubMed

    Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

    2013-01-01

    The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

  3. Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory

    PubMed Central

    Isaacson, Sven; Luo, Feng; Feltus, Frank A.; Smith, Melissa C.

    2013-01-01

    The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust. PMID:23409071

  4. Pan- and core- network analysis of co-expression genes in a model plant

    PubMed Central

    He, Fei; Maslov, Sergei

    2016-01-01

    Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of ‘pan-network’ and ‘core-network’ representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis. PMID:27982071

  5. Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

    PubMed Central

    2012-01-01

    Background Current experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks. Results We developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses. Conclusions The bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean

  6. Discovery of Core Biotic Stress Responsive Genes in Arabidopsis by Weighted Gene Co-Expression Network Analysis

    PubMed Central

    Amrine, Katherine C. H.; Blanco-Ulate, Barbara; Cantu, Dario

    2015-01-01

    Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant Arabidopsis thaliana with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and cis-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different

  7. Characterization of Genes for Beef Marbling Based on Applying Gene Coexpression Network

    PubMed Central

    Lim, Dajeong; Kim, Nam-Kuk; Lee, Seung-Hwan; Park, Hye-Sun; Cho, Yong-Min; Chai, Han-Ha; Kim, Heebal

    2014-01-01

    Marbling is an important trait in characterization beef quality and a major factor for determining the price of beef in the Korean beef market. In particular, marbling is a complex trait and needs a system-level approach for identifying candidate genes related to the trait. To find the candidate gene associated with marbling, we used a weighted gene coexpression network analysis from the expression value of bovine genes. Hub genes were identified; they were topologically centered with large degree and BC values in the global network. We performed gene expression analysis to detect candidate genes in M. longissimus with divergent marbling phenotype (marbling scores 2 to 7) using qRT-PCR. The results demonstrate that transmembrane protein 60 (TMEM60) and dihydropyrimidine dehydrogenase (DPYD) are associated with increasing marbling fat. We suggest that the network-based approach in livestock may be an important method for analyzing the complex effects of candidate genes associated with complex traits like marbling or tenderness. PMID:24624372

  8. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

    PubMed

    Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

    2015-01-01

    Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  9. Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese Individuals

    PubMed Central

    Kogelman, Lisette J. A.; Fu, Jingyuan; Franke, Lude; Greve, Jan Willem; Hofker, Marten; Rensen, Sander S.; Kadarmideen, Haja N.

    2016-01-01

    Background Obesity is associated with severe co-morbidities such as type 2 diabetes and nonalcoholic steatohepatitis. However, studies have shown that 10–25 percent of the severely obese individuals are metabolically healthy. To date, the identification of genetic factors underlying the metabolically healthy obese (MHO) state is limited. Systems genetics approaches have led to the identification of genes and pathways in complex diseases. Here, we have used such approaches across tissues to detect genes and pathways involved in obesity-induced disease development. Methods Expression data of 60 severely obese individuals was accessible, of which 28 individuals were MHO and 32 were metabolically unhealthy obese (MUO). A whole genome expression profile of four tissues was available: liver, muscle, subcutaneous adipose tissue and visceral adipose tissue. Using insulin-related genes, we used the weighted gene co-expression network analysis (WGCNA) method to build within- and inter-tissue gene networks. We identified genes that were differentially connected between MHO and MUO individuals, which were further investigated by homing in on the modules they were active in. To identify potentially causal genes, we integrated genomic and transcriptomic data using an eQTL mapping approach. Results Both IL-6 and IL1B were identified as highly differentially co-expressed genes across tissues between MHO and MUO individuals, showing their potential role in obesity-induced disease development. WGCNA showed that those genes were clustering together within tissues, and further analysis showed different co-expression patterns between MHO and MUO subnetworks. A potential causal role for metabolic differences under similar obesity state was detected for PTPRE, IL-6R and SLC6A5. Conclusions We used a novel integrative approach by integration of co-expression networks across tissues to elucidate genetic factors related to obesity-induced metabolic disease development. The identified

  10. Coexpression landscape in ATTED-II: usage of gene list and gene network for various types of pathways.

    PubMed

    Obayashi, Takeshi; Kinoshita, Kengo

    2010-05-01

    Gene coexpression analyses are a powerful method to predict the function of genes and/or to identify genes that are functionally related to query genes. The basic idea of gene coexpression analyses is that genes with similar functions should have similar expression patterns under many different conditions. This approach is now widely used by many experimental researchers, especially in the field of plant biology. In this review, we will summarize recent successful examples obtained by using our gene coexpression database, ATTED-II. Specifically, the examples will describe the identification of new genes, such as the subunits of a complex protein, the enzymes in a metabolic pathway and transporters. In addition, we will discuss the discovery of a new intercellular signaling factor and new regulatory relationships between transcription factors and their target genes. In ATTED-II, we provide two basic views of gene coexpression, a gene list view and a gene network view, which can be used as guide gene approach and narrow-down approach, respectively. In addition, we will discuss the coexpression effectiveness for various types of gene sets.

  11. Discovering missing reactions of metabolic networks by using gene co-expression data

    PubMed Central

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-01-01

    Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts. PMID:28150713

  12. Discovering missing reactions of metabolic networks by using gene co-expression data

    NASA Astrophysics Data System (ADS)

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-02-01

    Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

  13. Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

    PubMed Central

    Creanza, Teresa Maria; Liguori, Maria; Liuni, Sabino; Nuzziello, Nicoletta; Ancona, Nicola

    2016-01-01

    Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment. PMID:27314336

  14. Gene Coexpression Analyses Differentiate Networks Associated with Diverse Cancers Harboring TP53 Missense or Null Mutations

    PubMed Central

    Oros Klein, Kathleen; Oualkacha, Karim; Lafond, Marie-Hélène; Bhatnagar, Sahir; Tonin, Patricia N.; Greenwood, Celia M. T.

    2016-01-01

    In a variety of solid cancers, missense mutations in the well-established TP53 tumor suppressor gene may lead to the presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumor biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of coexpression of genes in tumors grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2) consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene's strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumor biology. PMID:27536319

  15. Chronic ethanol exposure produces time- and brain region-dependent changes in gene coexpression networks.

    PubMed

    Osterndorff-Kahanek, Elizabeth A; Becker, Howard C; Lopez, Marcelo F; Farris, Sean P; Tiwari, Gayatri R; Nunez, Yury O; Harris, R Adron; Mayfield, R Dayne

    2015-01-01

    Repeated ethanol exposure and withdrawal in mice increases voluntary drinking and represents an animal model of physical dependence. We examined time- and brain region-dependent changes in gene coexpression networks in amygdala (AMY), nucleus accumbens (NAC), prefrontal cortex (PFC), and liver after four weekly cycles of chronic intermittent ethanol (CIE) vapor exposure in C57BL/6J mice. Microarrays were used to compare gene expression profiles at 0-, 8-, and 120-hours following the last ethanol exposure. Each brain region exhibited a large number of differentially expressed genes (2,000-3,000) at the 0- and 8-hour time points, but fewer changes were detected at the 120-hour time point (400-600). Within each region, there was little gene overlap across time (~20%). All brain regions were significantly enriched with differentially expressed immune-related genes at the 8-hour time point. Weighted gene correlation network analysis identified modules that were highly enriched with differentially expressed genes at the 0- and 8-hour time points with virtually no enrichment at 120 hours. Modules enriched for both ethanol-responsive and cell-specific genes were identified in each brain region. These results indicate that chronic alcohol exposure causes global 'rewiring' of coexpression systems involving glial and immune signaling as well as neuronal genes.

  16. Gene co-expression networks shed light into diseases of brain iron accumulation

    PubMed Central

    Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M.; Botía, Juan A.; Collingwood, Joanna F.; Hardy, John; Milward, Elizabeth A.; Ryten, Mina; Houlden, Henry

    2016-01-01

    Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. PMID:26707700

  17. The Structure of a Gene Co-Expression Network Reveals Biological Functions Underlying eQTLs

    PubMed Central

    Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

    2013-01-01

    What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology. PMID:23577081

  18. The structure of a gene co-expression network reveals biological functions underlying eQTLs.

    PubMed

    Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

    2013-01-01

    What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

  19. Gene coexpression network analysis of oil biosynthesis in an interspecific backcross of oil palm.

    PubMed

    Guerin, Chloé; Joët, Thierry; Serret, Julien; Lashermes, Philippe; Vaissayre, Virginie; Agbessi, Mawussé D T; Beulé, Thierry; Severac, Dany; Amblard, Philippe; Tregear, James; Durand-Gasselin, Tristan; Morcillo, Fabienne; Dussert, Stéphane

    2016-09-01

    Global demand for vegetable oils is increasing at a dramatic rate, while our understanding of the regulation of oil biosynthesis in plants remains limited. To gain insights into the mechanisms that govern oil synthesis and fatty acid (FA) composition in the oil palm fruit, we used a multilevel approach combining gene coexpression analysis, quantification of allele-specific expression and joint multivariate analysis of transcriptomic and lipid data, in an interspecific backcross population between the African oil palm, Elaeis guineensis, and the American oil palm, Elaeis oleifera, which display contrasting oil contents and FA compositions. The gene coexpression network produced revealed tight transcriptional coordination of fatty acid synthesis (FAS) in the plastid with sugar sensing, plastidial glycolysis, transient starch storage and carbon recapture pathways. It also revealed a concerted regulation, along with FAS, of both the transfer of nascent FA to the endoplasmic reticulum, where triacylglycerol assembly occurs, and of the production of glycerol-3-phosphate, which provides the backbone of triacylglycerols. Plastid biogenesis and auxin transport were the two other biological processes most tightly connected to FAS in the network. In addition to WRINKLED1, a transcription factor (TF) known to activate FAS genes, two novel TFs, termed NF-YB-1 and ZFP-1, were found at the core of the FAS module. The saturated FA content of palm oil appeared to vary above all in relation to the level of transcripts of the gene coding for β-ketoacyl-acyl carrier protein synthase II. Our findings should facilitate the development of breeding and engineering strategies in this and other oil crops.

  20. Broad integration of expression maps and co-expression networks compassing novel gene functions in the brain.

    PubMed

    Okamura-Oho, Yuko; Shimokawa, Kazuro; Nishimura, Masaomi; Takemoto, Satoko; Sato, Akira; Furuichi, Teiichi; Yokota, Hideo

    2014-11-10

    Using a recently invented technique for gene expression mapping in the whole-anatomy context, termed transcriptome tomography, we have generated a dataset of 36,000 maps of overall gene expression in the adult-mouse brain. Here, using an informatics approach, we identified a broad co-expression network that follows an inverse power law and is rich in functional interaction and gene-ontology terms. Our framework for the integrated analysis of expression maps and graphs of co-expression networks revealed that groups of combinatorially expressed genes, which regulate cell differentiation during development, were present in the adult brain and each of these groups was associated with a discrete cell types. These groups included non-coding genes of unknown function. We found that these genes specifically linked developmentally conserved groups in the network. A previously unrecognized robust expression pattern covering the whole brain was related to the molecular anatomy of key biological processes occurring in particular areas.

  1. ALCOdb: Gene Coexpression Database for Microalgae.

    PubMed

    Aoki, Yuichi; Okamura, Yasunobu; Ohta, Hiroyuki; Kinoshita, Kengo; Obayashi, Takeshi

    2016-01-01

    In the era of energy and food shortage, microalgae have gained much attention as promising sources of biofuels and food ingredients. However, only a small fraction of microalgal genes have been functionally characterized. Here, we have developed the Algae Gene Coexpression database (ALCOdb; http://alcodb.jp), which provides gene coexpression information to survey gene modules for a function of interest. ALCOdb currently supports two model algae: the green alga Chlamydomonas reinhardtii and the red alga Cyanidioschyzon merolae. Users can retrieve coexpression information for genes of interest through three unique data pages: (i) Coexpressed Gene List; (ii) Gene Information; and (iii) Coexpressed Gene Network. In addition to the basal coexpression information, ALCOdb also provides several advanced functionalities such as an expression profile viewer and a differentially expressed gene search tool. Using these user interfaces, we demonstrated that our gene coexpression data have the potential to detect functionally related genes and are useful in extrapolating the biological roles of uncharacterized genes. ALCOdb will facilitate molecular and biochemical studies of microalgal biological phenomena, such as lipid metabolism and organelle development, and promote the evolutionary understanding of plant cellular systems.

  2. ALCOdb: Gene Coexpression Database for Microalgae

    PubMed Central

    Aoki, Yuichi; Okamura, Yasunobu; Ohta, Hiroyuki; Kinoshita, Kengo; Obayashi, Takeshi

    2016-01-01

    In the era of energy and food shortage, microalgae have gained much attention as promising sources of biofuels and food ingredients. However, only a small fraction of microalgal genes have been functionally characterized. Here, we have developed the Algae Gene Coexpression database (ALCOdb; http://alcodb.jp), which provides gene coexpression information to survey gene modules for a function of interest. ALCOdb currently supports two model algae: the green alga Chlamydomonas reinhardtii and the red alga Cyanidioschyzon merolae. Users can retrieve coexpression information for genes of interest through three unique data pages: (i) Coexpressed Gene List; (ii) Gene Information; and (iii) Coexpressed Gene Network. In addition to the basal coexpression information, ALCOdb also provides several advanced functionalities such as an expression profile viewer and a differentially expressed gene search tool. Using these user interfaces, we demonstrated that our gene coexpression data have the potential to detect functionally related genes and are useful in extrapolating the biological roles of uncharacterized genes. ALCOdb will facilitate molecular and biochemical studies of microalgal biological phenomena, such as lipid metabolism and organelle development, and promote the evolutionary understanding of plant cellular systems. PMID:26644461

  3. Incorporating Motif Analysis into Gene Co-expression Networks Reveals Novel Modular Expression Pattern and New Signaling Pathways

    PubMed Central

    Ma, Shisong; Shah, Smit; Bohnert, Hans J.; Snyder, Michael; Dinesh-Kumar, Savithramma P.

    2013-01-01

    Understanding of gene regulatory networks requires discovery of expression modules within gene co-expression networks and identification of promoter motifs and corresponding transcription factors that regulate their expression. A commonly used method for this purpose is a top-down approach based on clustering the network into a range of densely connected segments, treating these segments as expression modules, and extracting promoter motifs from these modules. Here, we describe a novel bottom-up approach to identify gene expression modules driven by known cis-regulatory motifs in the gene promoters. For a specific motif, genes in the co-expression network are ranked according to their probability of belonging to an expression module regulated by that motif. The ranking is conducted via motif enrichment or motif position bias analysis. Our results indicate that motif position bias analysis is an effective tool for genome-wide motif analysis. Sub-networks containing the top ranked genes are extracted and analyzed for inherent gene expression modules. This approach identified novel expression modules for the G-box, W-box, site II, and MYB motifs from an Arabidopsis thaliana gene co-expression network based on the graphical Gaussian model. The novel expression modules include those involved in house-keeping functions, primary and secondary metabolism, and abiotic and biotic stress responses. In addition to confirmation of previously described modules, we identified modules that include new signaling pathways. To associate transcription factors that regulate genes in these co-expression modules, we developed a novel reporter system. Using this approach, we evaluated MYB transcription factor-promoter interactions within MYB motif modules. PMID:24098147

  4. An expression atlas of human primary cells: inference of gene function from coexpression networks

    PubMed Central

    2013-01-01

    Background The specialisation of mammalian cells in time and space requires genes associated with specific pathways and functions to be co-ordinately expressed. Here we have combined a large number of publically available microarray datasets derived from human primary cells and analysed large correlation graphs of these data. Results Using the network analysis tool BioLayout Express3D we identify robust co-associations of genes expressed in a wide variety of cell lineages. We discuss the biological significance of a number of these associations, in particular the coexpression of key transcription factors with the genes that they are likely to control. Conclusions We consider the regulation of genes in human primary cells and specifically in the human mononuclear phagocyte system. Of particular note is the fact that these data do not support the identity of putative markers of antigen-presenting dendritic cells, nor classification of M1 and M2 activation states, a current subject of debate within immunological field. We have provided this data resource on the BioGPS web site (http://biogps.org/dataset/2429/primary-cell-atlas/) and on macrophages.com (http://www.macrophages.com/hu-cell-atlas). PMID:24053356

  5. Comparison of low and high dose ionising radiation using topological analysis of gene coexpression networks

    PubMed Central

    2012-01-01

    Background The growing use of imaging procedures in medicine has raised concerns about exposure to low-dose ionising radiation (LDIR). While the disastrous effects of high dose ionising radiation (HDIR) is well documented, the detrimental effects of LDIR is not well understood and has been a topic of much debate. Since little is known about the effects of LDIR, various kinds of wet-lab and computational analyses are required to advance knowledge in this domain. In this paper we carry out an “upside-down pyramid” form of systems biology analysis of microarray data. We characterised the global genomic response following 10 cGy (low dose) and 100 cGy (high dose) doses of X-ray ionising radiation at four time points by analysing the topology of gene coexpression networks. This study includes a rich experimental design and state-of-the-art computational systems biology methods of analysis to study the differences in the transcriptional response of skin cells exposed to low and high doses of radiation. Results Using this method we found important genes that have been linked to immune response, cell survival and apoptosis. Furthermore, we also were able to identify genes such as BRCA1, ABCA1, TNFRSF1B, MLLT11 that have been associated with various types of cancers. We were also able to detect many genes known to be associated with various medical conditions. Conclusions Our method of applying network topological differences can aid in identifying the differences among similar (eg: radiation effect) yet very different biological conditions (eg: different dose and time) to generate testable hypotheses. This is the first study where a network level analysis was performed across two different radiation doses at various time points, thereby illustrating changes in the cellular response over time. PMID:22594378

  6. Gene networks in skeletal muscle following endurance exercise are coexpressed in blood neutrophils and linked with blood inflammation markers.

    PubMed

    Broadbent, James; Sampson, Dayle; Sabapathy, Surendran; Haseler, Luke J; Wagner, Karl-Heinz; Bulmer, Andrew C; Peake, Jonathan M; Neubauer, Oliver

    2017-04-01

    It remains incompletely understood whether there is an association between the transcriptome profiles of skeletal muscle and blood leukocytes in response to exercise or other physiological stressors. We have previously analyzed the changes in the muscle and blood neutrophil transcriptome in eight trained men before and 3, 48, and 96 h after 2 h cycling and running. Because we collected muscle and blood in the same individuals and under the same conditions, we were able to directly compare gene expression between the muscle and blood neutrophils. Applying weighted gene coexpression network analysis (WGCNA) as an advanced network-driven method to these original data sets enabled us to compare the muscle and neutrophil transcriptomes in a rigorous and systematic manner. Two gene networks were identified that were preserved between skeletal muscle and blood neutrophils, functionally related to mitochondria and posttranslational processes. Strong preservation measures (Zsummary > 10) for both muscle-neutrophil gene networks were evident within the postexercise recovery period. Muscle and neutrophil gene coexpression was strongly correlated in the mitochondria-related network (r = 0.97; P = 3.17E-2). We also identified multiple correlations between muscular gene subnetworks and exercise-induced changes in blood leukocyte counts, inflammation, and muscle damage markers. These data reveal previously unidentified gene coexpression between skeletal muscle and blood neutrophils following exercise, showing the value of WGCNA to understand exercise physiology. Furthermore, these findings provide preliminary evidence in support of the notion that blood neutrophil gene networks may potentially help us to track physiological and pathophysiological changes in the muscle.NEW & NOTEWORTHY By using weighted gene coexpression network analysis, an advanced bioinformatics method, we have identified previously unknown, functional gene networks that are preserved between skeletal muscle

  7. Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)

    PubMed Central

    Das, Samarendra; Meher, Prabina Kumar; Bhar, Lal Mohan; Mandal, Baidya Nath

    2017-01-01

    Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean. PMID:28056073

  8. A co-expression gene network associated with developmental regulation of apple fruit acidity.

    PubMed

    Bai, Yang; Dougherty, Laura; Cheng, Lailiang; Xu, Kenong

    2015-08-01

    Apple fruit acidity, which affects the fruit's overall taste and flavor to a large extent, is primarily determined by the concentration of malic acid. Previous studies demonstrated that the major QTL malic acid (Ma) on chromosome 16 is largely responsible for fruit acidity variations in apple. Recent advances suggested that a natural mutation that gives rise to a premature stop codon in one of the two aluminum-activated malate transporter (ALMT)-like genes (called Ma1) is the genetic causal element underlying Ma. However, the natural mutation does not explain the developmental changes of fruit malate levels in a given genotype. Using RNA-seq data from the fruit of 'Golden Delicious' taken at 14 developmental stages from 1 week after full-bloom (WAF01) to harvest (WAF20), we characterized their transcriptomes in groups of high (12.2 ± 1.6 mg/g fw, WAF03-WAF08), mid (7.4 ± 0.5 mg/g fw, WAF01-WAF02 and WAF10-WAF14) and low (5.4 ± 0.4 mg/g fw, WAF16-WAF20) malate concentrations. Detailed analyses showed that a set of 3,066 genes (including Ma1) were expressed not only differentially (P FDR < 0.05) between the high and low malate groups (or between the early and late developmental stages) but also in significant (P < 0.05) correlation with malate concentrations. The 3,066 genes fell in 648 MapMan (sub-) bins or functional classes, and 19 of them were significantly (P FDR < 0.05) co-enriched or co-suppressed in a malate dependent manner. Network inferring using the 363 genes encompassed in the 19 (sub-) bins, identified a major co-expression network of 239 genes. Since the 239 genes were also differentially expressed between the early (WAF03-WAF08) and late (WAF16-WAF20) developmental stages, the major network was considered to be associated with developmental regulation of apple fruit acidity in 'Golden Delicious'.

  9. Gene co-expression network analysis in Rhodobacter capsulatus and application to comparative expression analysis of Rhodobacter sphaeroides

    SciTech Connect

    Pena-Castillo, Lourdes; Mercer, Ryan; Gurinovich, Anastasia; Callister, Stephen J.; Wright, Aaron T.; Westbye, Alexander; Beatty, J. T.; Lang, Andrew S.

    2014-08-28

    The genus Rhodobacter contains purple nonsulfur bacteria found mostly in freshwater environments. Representative strains of two Rhodobacter species, R. capsulatus and R. sphaeroides, have had their genomes fully sequenced and both have been the subject of transcriptional profiling studies. Gene co-expression networks can be used to identify modules of genes with similar expression profiles. Functional analysis of gene modules can then associate co-expressed genes with biological pathways, and network statistics can determine the degree of module preservation in related networks. In this paper, we constructed an R. capsulatus gene co-expression network, performed functional analysis of identified gene modules, and investigated preservation of these modules in R. capsulatus proteomics data and in R. sphaeroides transcriptomics data. Results: The analysis identified 40 gene co-expression modules in R. capsulatus. Investigation of the module gene contents and expression profiles revealed patterns that were validated based on previous studies supporting the biological relevance of these modules. We identified two R. capsulatus gene modules preserved in the protein abundance data. We also identified several gene modules preserved between both Rhodobacter species, which indicate that these cellular processes are conserved between the species and are candidates for functional information transfer between species. Many gene modules were non-preserved, providing insight into processes that differentiate the two species. In addition, using Local Network Similarity (LNS), a recently proposed metric for expression divergence, we assessed the expression conservation of between-species pairs of orthologs, and within-species gene-protein expression profiles. Conclusions: Our analyses provide new sources of information for functional annotation in R. capsulatus because uncharacterized genes in modules are now connected with groups of genes that constitute a joint functional

  10. Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes

    PubMed Central

    Riquelme Medina, Ignacio; Lubovac-Pilav, Zelmina

    2016-01-01

    Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body’s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D. PMID:27257970

  11. Identification of hub genes of pneumocyte senescence induced by thoracic irradiation using weighted gene co-expression network analysis

    PubMed Central

    XING, YONGHUA; ZHANG, JUNLING; LU, LU; LI, DEGUAN; WANG, YUEYING; HUANG, SONG; LI, CHENGCHENG; ZHANG, ZHUBO; LI, JIANGUO; MENG, AIMIN

    2016-01-01

    Irradiation commonly causes pneumocyte senescence, which may lead to severe fatal lung injury characterized by pulmonary dysfunction and respiratory failure. However, the molecular mechanism underlying the induction of pneumocyte senescence by irradiation remains to be elucidated. In the present study, weighted gene co-expression network analysis (WGCNA) was used to screen for differentially expressed genes, and to identify the hub genes and gene modules, which may be critical for senescence. A total of 2,916 differentially expressed genes were identified between the senescence and non-senescence groups following thoracic irradiation. In total, 10 gene modules associated with cell senescence were detected, and six hub genes were identified, including B-cell scaffold protein with ankyrin repeats 1, translocase of outer mitochondrial membrane 70 homolog A, actin filament-associated protein 1, Cd84, Nuf2 and nuclear factor erythroid 2. These genes were markedly associated with cell proliferation, cell division and cell cycle arrest. The results of the present study demonstrated that WGCNA of microarray data may provide further insight into the molecular mechanism underlying pneumocyte senescence. PMID:26572216

  12. A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes.

    PubMed

    Ransbotyn, Vanessa; Yeger-Lotem, Esti; Basha, Omer; Acuna, Tania; Verduyn, Christoph; Gordon, Michal; Chalifa-Caspi, Vered; Hannah, Matthew A; Barak, Simon

    2015-05-01

    As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at http://netbio.bgu.ac.il/arnet. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.

  13. Gene Coexpression Networks in Human Brain Developmental Transcriptomes Implicate the Association of Long Noncoding RNAs with Intellectual Disability

    PubMed Central

    Gudenas, Brian L.; Wang, Liangjiang

    2015-01-01

    The advent of next-generation sequencing for genetic diagnoses of complex developmental disorders, such as intellectual disability (ID), has facilitated the identification of hundreds of predisposing genetic variants. However, there still exists a vast gap in our knowledge of causal genetic factors for ID as evidenced by low diagnostic yield of genetic screening, in which identifiable genetic causes are not found for the majority of ID cases. Most methods of genetic screening focus on protein-coding genes; however, noncoding RNAs may outnumber protein-coding genes and play important roles in brain development. Long noncoding RNAs (lncRNAs) specifically have been shown to be enriched in the brain and have diverse roles in gene regulation at the transcriptional and posttranscriptional levels. LncRNAs are a vastly uncharacterized group of noncoding genes, which could function in brain development and harbor ID-predisposing genetic variants. We analyzed lncRNAs for coexpression with known ID genes and affected biological pathways within a weighted gene coexpression network derived from RNA-sequencing data spanning human brain development. Several ID-associated gene modules were found to be enriched for lncRNAs, known ID genes, and affected biological pathways. Utilizing a list of de novo and pathogenic copy number variants detected in ID probands, we identified lncRNAs overlapping these genetic structural variants. By integrating our results, we have made a prioritized list of potential ID-associated lncRNAs based on the developing brain gene coexpression network and genetic structural variants found in ID probands. PMID:26523118

  14. Identification of rice genes associated with cosmic-ray response via co-expression gene network analysis.

    PubMed

    Hwang, Sun-Goo; Kim, Dong Sub; Hwang, Jung Eun; Han, A-Reum; Jang, Cheol Seong

    2014-05-15

    In order to better understand the biological systems that are affected in response to cosmic ray (CR), we conducted weighted gene co-expression network analysis using the module detection method. By using the Pearson's correlation coefficient (PCC) value, we evaluated complex gene-gene functional interactions between 680 CR-responsive probes from integrated microarray data sets, which included large-scale transcriptional profiling of 1000 microarray samples. These probes were divided into 6 distinct modules that contained 20 enriched gene ontology (GO) functions, such as oxidoreductase activity, hydrolase activity, and response to stimulus and stress. In particular, modules 1 and 2 commonly showed enriched annotation categories such as oxidoreductase activity, including enriched cis-regulatory elements known as ROS-specific regulators. These results suggest that the ROS-mediated irradiation response pathway is affected by CR in modules 1 and 2. We found 243 ionizing radiation (IR)-responsive probes that exhibited similarities in expression patterns in various irradiation microarray data sets. The expression patterns of 6 randomly selected IR-responsive genes were evaluated by quantitative reverse transcription polymerase chain reaction following treatment with CR, gamma rays (GR), and ion beam (IB); similar patterns were observed among these genes under these 3 treatments. Moreover, we constructed subnetworks of IR-responsive genes and evaluated the expression levels of their neighboring genes following GR treatment; similar patterns were observed among them. These results of network-based analyses might provide a clue to understanding the complex biological system related to the CR response in plants.

  15. Studying the complex expression dependences between sets of coexpressed genes.

    PubMed

    Huerta, Mario; Casanova, Oriol; Barchino, Roberto; Flores, Jose; Querol, Enrique; Cedano, Juan

    2014-01-01

    Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. The use of clustering methods to obtain sets of coexpressed genes from expression arrays is very common; nevertheless there are no appropriate tools to study the expression networks among these sets of coexpressed genes. The aim of the developed tools is to allow studying the complex expression dependences that exist between sets of coexpressed genes. For this purpose, we start detecting the nonlinear expression relationships between pairs of genes, plus the coexpressed genes. Next, we form networks among sets of coexpressed genes that maintain nonlinear expression dependences between all of them. The expression relationship between the sets of coexpressed genes is defined by the expression relationship between the skeletons of these sets, where this skeleton represents the coexpressed genes with a well-defined nonlinear expression relationship with the skeleton of the other sets. As a result, we can study the nonlinear expression relationships between a target gene and other sets of coexpressed genes, or start the study from the skeleton of the sets, to study the complex relationships of activation and deactivation between the sets of coexpressed genes that carry out the different cellular processes present in the expression experiments.

  16. A network approach of gene co-expression in the zea mays/Aspergillus flavus pathosystem to map host/pathogen interaction pathways

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A gene co-expression network was generated using a dual RNA-seq study with the fungal pathogen A. flavus and its plant host Z. mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network reveal...

  17. Identifying gene coexpression networks underlying the dynamic regulation of wood-forming tissues in Populus under diverse environmental conditions.

    PubMed

    Zinkgraf, Matthew; Liu, Lijun; Groover, Andrew; Filkov, Vladimir

    2017-03-01

    Trees modify wood formation through integration of environmental and developmental signals in complex but poorly defined transcriptional networks, allowing trees to produce woody tissues appropriate to diverse environmental conditions. In order to identify relationships among genes expressed during wood formation, we integrated data from new and publically available datasets in Populus. These datasets were generated from woody tissue and include transcriptome profiling, transcription factor binding, DNA accessibility and genome-wide association mapping experiments. Coexpression modules were calculated, each of which contains genes showing similar expression patterns across experimental conditions, genotypes and treatments. Conserved gene coexpression modules (four modules totaling 8398 genes) were identified that were highly preserved across diverse environmental conditions and genetic backgrounds. Functional annotations as well as correlations with specific experimental treatments associated individual conserved modules with distinct biological processes underlying wood formation, such as cell-wall biosynthesis, meristem development and epigenetic pathways. Module genes were also enriched for DNase I hypersensitivity footprints and binding from four transcription factors associated with wood formation. The conserved modules are excellent candidates for modeling core developmental pathways common to wood formation in diverse environments and genotypes, and serve as testbeds for hypothesis generation and testing for future studies.

  18. Differential Coexpression Analysis Reveals Extensive Rewiring of Arabidopsis Gene Coexpression in Response to Pseudomonas syringae Infection

    PubMed Central

    Jiang, Zhenhong; Dong, Xiaobao; Li, Zhi-Gang; He, Fei; Zhang, Ziding

    2016-01-01

    Plant defense responses to pathogens involve massive transcriptional reprogramming. Recently, differential coexpression analysis has been developed to study the rewiring of gene networks through microarray data, which is becoming an important complement to traditional differential expression analysis. Using time-series microarray data of Arabidopsis thaliana infected with Pseudomonas syringae, we analyzed Arabidopsis defense responses to P. syringae through differential coexpression analysis. Overall, we found that differential coexpression was a common phenomenon of plant immunity. Genes that were frequently involved in differential coexpression tend to be related to plant immune responses. Importantly, many of those genes have similar average expression levels between normal plant growth and pathogen infection but have different coexpression partners. By integrating the Arabidopsis regulatory network into our analysis, we identified several transcription factors that may be regulators of differential coexpression during plant immune responses. We also observed extensive differential coexpression between genes within the same metabolic pathways. Several metabolic pathways, such as photosynthesis light reactions, exhibited significant changes in expression correlation between normal growth and pathogen infection. Taken together, differential coexpression analysis provides a new strategy for analyzing transcriptional data related to plant defense responses and new insights into the understanding of plant-pathogen interactions. PMID:27721457

  19. Gene Co-Expression Network Analysis Provides Novel Insights into Myostatin Regulation at Three Different Mouse Developmental Timepoints

    PubMed Central

    Yang, Xuerong; Koltes, James E.; Park, Carissa A.; Chen, Daiwen; Reecy, James M.

    2015-01-01

    Myostatin (Mstn) knockout mice exhibit large increases in skeletal muscle mass. However, relatively few of the genes that mediate or modify MSTN effects are known. In this study, we performed co-expression network analysis using whole transcriptome microarray data from MSTN-null and wild-type mice to identify genes involved in important biological processes and pathways related to skeletal muscle and adipose development. Genes differentially expressed between wild-type and MSTN-null mice were further analyzed for shared DNA motifs using DREME. Differentially expressed genes were identified at 13.5 d.p.c. during primary myogenesis and at d35 during postnatal muscle development, but not at 17.5 d.p.c. during secondary myogenesis. In total, 283 and 2034 genes were differentially expressed at 13.5 d.p.c. and d35, respectively. Over-represented transcription factor binding sites in differentially expressed genes included SMAD3, SP1, ZFP187, and PLAGL1. The use of regulatory (RIF) and phenotypic (PIF) impact factor and differential hubbing co-expression analyses identified both known and potentially novel regulators of skeletal muscle growth, including Apobec2, Atp2a2, and Mmp13 at d35 and Sox2, Tmsb4x, and Vdac1 at 13.5 d.p.c. Among the genes with the highest PIF scores were many fiber type specifying genes. The use of RIF, PIF, and differential hubbing analyses identified both known and potentially novel regulators of muscle development. These results provide new details of how MSTN may mediate transcriptional regulation as well as insight into novel regulators of MSTN signal transduction that merit further study regarding their physiological roles in muscle and adipose development. PMID:25695797

  20. Coexpression network analysis of the genes regulated by two types of resistance responses to powdery mildew in wheat

    PubMed Central

    Zhang, Juncheng; Zheng, Hongyuan; Li, Yiwen; Li, Hongjie; Liu, Xin; Qin, Huanju; Dong, Lingli; Wang, Daowen

    2016-01-01

    Powdery mildew disease caused by Blumeria graminis f. sp. tritici (Bgt) inflicts severe economic losses in wheat crops. A systematic understanding of the molecular mechanisms involved in wheat resistance to Bgt is essential for effectively controlling the disease. Here, using the diploid wheat Triticum urartu as a host, the genes regulated by immune (IM) and hypersensitive reaction (HR) resistance responses to Bgt were investigated through transcriptome sequencing. Four gene coexpression networks (GCNs) were developed using transcriptomic data generated for 20 T. urartu accessions showing IM, HR or susceptible responses. The powdery mildew resistance regulated (PMRR) genes whose expression was significantly correlated with Bgt resistance were identified, and they tended to be hubs and enriched in six major modules. A wide occurrence of negative regulation of PMRR genes was observed. Three new candidate immune receptor genes (TRIUR3_13045, TRIUR3_01037 and TRIUR3_06195) positively associated with Bgt resistance were discovered. Finally, the involvement of TRIUR3_01037 in Bgt resistance was tentatively verified through cosegregation analysis in a F2 population and functional expression assay in Bgt susceptible leaf cells. This research provides insights into the global network properties of PMRR genes. Potential molecular differences between IM and HR resistance responses to Bgt are discussed. PMID:27033636

  1. Weighted gene co-expression network analysis in identification of metastasis-related genes of lung squamous cell carcinoma based on the Cancer Genome Atlas database

    PubMed Central

    Tian, Feng; Zhao, Jinlong; Kang, Zhenxing

    2017-01-01

    Background Lung squamous cell carcinoma (lung SCC) is a common type of malignancy. Its pathogenesis mechanism of tumor development is unclear. The aim of this study was to identify key genes for diagnosis biomarkers in lung SCC metastasis. Methods We searched and downloaded mRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA expression of primary tumor tissues from lung SCC with and without metastasis. Gene co-expression network analysis, protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and quantitative real-time polymerase chain reactions (qRT-PCR) were used to explore the biological functions of the identified dysregulated genes. Results Four hundred and eighty-two differentially expressed genes (DEGs) were identified between lung SCC with and without metastasis. Nineteen modules were identified in lung SCC through weighted gene co-expression network analysis (WGCNA). Twenty-three DEGs and 26 DEGs were significantly enriched in the respective pink and black module. KEGG pathway analysis displayed that 26 DEGs in the black module were significantly enriched in bile secretion pathway. Forty-nine DEGs in the two gene co-expression module were used to construct PPI network. CFTR in the black module was the hub protein, had the connectivity with 182 genes. The results of qRT-PCR displayed that FIGF, SFTPD, DYNLRB2 were significantly down-regulated in the tumor samples of lung SCC with metastasis and CFTR, SCGB3A2, SSTR1, SCTR, ROPN1L had the down-regulation tendency in lung SCC with metastasis compared to lung SCC without metastasis. Conclusions The dysregulated genes including CFTR, SCTR and FIGF might be involved in the pathology of lung SCC metastasis and could be used as potential diagnosis biomarkers or therapeutic targets for lung SCC. PMID:28203405

  2. Learning from Co-expression Networks: Possibilities and Challenges

    PubMed Central

    Serin, Elise A. R.; Nijveen, Harm; Hilhorst, Henk W. M.; Ligterink, Wilco

    2016-01-01

    Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of

  3. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

    PubMed Central

    2014-01-01

    Background Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. Methods We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P < 0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and

  4. Physiological Responses and Gene Co-Expression Network of Mycorrhizal Roots under K(+) Deprivation.

    PubMed

    Garcia, Kevin; Chasman, Deborah; Roy, Sushmita; Ané, Jean-Michel

    2017-03-01

    Arbuscular mycorrhizal (AM) associations enhance the phosphorous and nitrogen nutrition of host plants, but little is known about their role in potassium (K(+)) nutrition. Medicago truncatula plants were cocultured with the AM fungus Rhizophagus irregularis under high and low K(+) regimes for 6 weeks. We determined how K(+) deprivation affects plant development and mineral acquisition and how these negative effects are tempered by the AM colonization. The transcriptional response of AM roots under K(+) deficiency was analyzed by whole-genome RNA sequencing. K(+) deprivation decreased root biomass and external K(+) uptake and modulated oxidative stress gene expression in M. truncatula roots. AM colonization induced specific transcriptional responses to K(+) deprivation that seem to temper these negative effects. A gene network analysis revealed putative key regulators of these responses. This study confirmed that AM associations provide some tolerance to K(+) deprivation to host plants, revealed that AM symbiosis modulates the expression of specific root genes to cope with this nutrient stress, and identified putative regulators participating in these tolerance mechanisms.

  5. Physiological Responses and Gene Co-Expression Network of Mycorrhizal Roots under K+ Deprivation1[OPEN

    PubMed Central

    Roy, Sushmita

    2017-01-01

    Arbuscular mycorrhizal (AM) associations enhance the phosphorous and nitrogen nutrition of host plants, but little is known about their role in potassium (K+) nutrition. Medicago truncatula plants were cocultured with the AM fungus Rhizophagus irregularis under high and low K+ regimes for 6 weeks. We determined how K+ deprivation affects plant development and mineral acquisition and how these negative effects are tempered by the AM colonization. The transcriptional response of AM roots under K+ deficiency was analyzed by whole-genome RNA sequencing. K+ deprivation decreased root biomass and external K+ uptake and modulated oxidative stress gene expression in M. truncatula roots. AM colonization induced specific transcriptional responses to K+ deprivation that seem to temper these negative effects. A gene network analysis revealed putative key regulators of these responses. This study confirmed that AM associations provide some tolerance to K+ deprivation to host plants, revealed that AM symbiosis modulates the expression of specific root genes to cope with this nutrient stress, and identified putative regulators participating in these tolerance mechanisms. PMID:28159827

  6. Gene Co-Expression Network Analysis Unraveling Transcriptional Regulation of High-Altitude Adaptation of Tibetan Pig

    PubMed Central

    Koltes, James E.; Gou, Xiao; Yang, Shuli; Yan, Dawei; Lu, Shaoxiong

    2016-01-01

    Tibetan pigs have survived at high altitude for millennia and they have a suite of adaptive features to tolerate the hypoxic environment. However, the molecular mechanisms underlying the regulation of hypoxia-adaptive phenotypes have not been completely elucidated. In this study, we analyzed differentially expressed genes (DEGs), biological pathways and constructed co-expression regulation networks using whole-transcriptome microarrays from lung tissues of Tibetan and Duroc pigs both at high and low altitude. A total of 3,066 DEGs were identified and this list was over-represented for the ontology terms including metabolic process, catalytic activity, and KEGG pathway including metabolic pathway and PI3K-Akt signaling pathway. The regulatory (RIF) and phenotypic (PIF) impact factor analysis identified several known and several potentially novel regulators of hypoxia adaption, including: IKBKG, KLF6 and RBPJ (RIF1), SF3B1, EFEMP1, HOXB6 and ATF6 (RIF2). These findings provide new details of the regulatory architecture of hypoxia-adaptive genes and also insight into which genes may undergo epigenetic modification for further study in the high-altitude adaptation. PMID:27936142

  7. A Null Model for Pearson Coexpression Networks

    PubMed Central

    Gobbi, Andrea; Jurman, Giuseppe

    2015-01-01

    Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges. PMID:26030917

  8. A null model for Pearson coexpression networks.

    PubMed

    Gobbi, Andrea; Jurman, Giuseppe

    2015-01-01

    Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.

  9. Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system

    PubMed Central

    Vandenbon, Alexis; Dinh, Viet H.; Mikami, Norihisa; Kitagawa, Yohko; Teraguchi, Shunsuke; Ohkura, Naganari; Sakaguchi, Shimon

    2016-01-01

    High-throughput gene expression data are one of the primary resources for exploring complex intracellular dynamics in modern biology. The integration of large amounts of public data may allow us to examine general dynamical relationships between regulators and target genes. However, obstacles for such analyses are study-specific biases or batch effects in the original data. Here we present Immuno-Navigator, a batch-corrected gene expression and coexpression database for 24 cell types of the mouse immune system. We systematically removed batch effects from the underlying gene expression data and showed that this removal considerably improved the consistency between inferred correlations and prior knowledge. The data revealed widespread cell type-specific correlation of expression. Integrated analysis tools allow users to use this correlation of expression for the generation of hypotheses about biological networks and candidate regulators in specific cell types. We show several applications of Immuno-Navigator as examples. In one application we successfully predicted known regulators of importance in naturally occurring Treg cells from their expression correlation with a set of Treg-specific genes. For one high-scoring gene, integrin β8 (Itgb8), we confirmed an association between Itgb8 expression in forkhead box P3 (Foxp3)-positive T cells and Treg-specific epigenetic remodeling. Our results also suggest that the regulation of Treg-specific genes within Treg cells is relatively independent of Foxp3 expression, supporting recent results pointing to a Foxp3-independent component in the development of Treg cells. PMID:27078110

  10. ALS disrupts spinal motor neuron maturation and aging pathways within gene co-expression networks

    PubMed Central

    Ho, Ritchie; Sances, Samuel; Gowing, Genevieve; Amoroso, Mackenzie Weygandt; O'Rourke, Jacqueline G.; Sahabian, Anais; Wichterle, Hynek; Baloh, Robert H.; Sareen, Dhruv

    2016-01-01

    Modeling Amyotrophic Lateral Sclerosis (ALS) with human induced pluripotent stem cells (iPSCs) aims to reenact embryogenesis, maturation, and aging of spinal motor neurons (spMNs) in vitro. As the maturity of spMNs grown in vitro compared to spMNs in vivo remains largely unaddressed, it is unclear to what extent this in vitro system captures critical aspects of spMN development and molecular signatures associated with ALS. Here, we compared transcriptomes among iPSC-derived spMNs, fetal, and adult spinal tissues. This approach produced a maturation scale revealing that iPSC-derived spMNs were more similar to fetal spinal tissue than to adult spMNs. Additionally, we resolved gene networks and pathways associated with spMN maturation and aging. These networks enriched for pathogenic familial ALS genetic variants and were disrupted in sporadic ALS spMNs. Altogether, our findings suggest that developing strategies to further mature and age iPSC-derived spMNs will provide more effective iPSC models of ALS pathology. PMID:27428653

  11. CluGene: A Bioinformatics Framework for the Identification of Co-Localized, Co-Expressed and Co-Regulated Genes Aimed at the Investigation of Transcriptional Regulatory Networks from High-Throughput Expression Data

    PubMed Central

    Dottorini, Tania; Palladino, Pietro; Senin, Nicola; Persampieri, Tania; Spaccapelo, Roberta; Crisanti, Andrea

    2013-01-01

    The full understanding of the mechanisms underlying transcriptional regulatory networks requires unravelling of complex causal relationships. Genome high-throughput technologies produce a huge amount of information pertaining gene expression and regulation; however, the complexity of the available data is often overwhelming and tools are needed to extract and organize the relevant information. This work starts from the assumption that the observation of co-occurrent events (in particular co-localization, co-expression and co-regulation) may provide a powerful starting point to begin unravelling transcriptional regulatory networks. Co-expressed genes often imply shared functional pathways; co-expressed and functionally related genes are often co-localized, too; moreover, co-expressed and co-localized genes are also potential targets for co-regulation; finally, co-regulation seems more frequent for genes mapped to proximal chromosome regions. Despite the recognized importance of analysing co-occurrent events, no bioinformatics solution allowing the simultaneous analysis of co-expression, co-localization and co-regulation is currently available. Our work resulted in developing and valuating CluGene, a software providing tools to analyze multiple types of co-occurrences within a single interactive environment allowing the interactive investigation of combined co-expression, co-localization and co-regulation of genes. The use of CluGene will enhance the power of testing hypothesis and experimental approaches aimed at unravelling transcriptional regulatory networks. The software is freely available at http://bioinfolab.unipg.it/. PMID:23823315

  12. Identification of candidate genes in Arabidopsis and Populus cell wall biosynthesis using text-mining, co-expression network analysis and comparative genomics.

    PubMed

    Yang, Xiaohan; Ye, Chu-Yu; Bisaria, Anjali; Tuskan, Gerald A; Kalluri, Udaya C

    2011-12-01

    Populus is an important bioenergy crop for bioethanol production. A greater understanding of cell wall biosynthesis processes is critical in reducing biomass recalcitrance, a major hindrance in efficient generation of biofuels from lignocellulosic biomass. Here, we report the identification of candidate cell wall biosynthesis genes through the development and application of a novel bioinformatics pipeline. As a first step, via text-mining of PubMed publications, we obtained 121 Arabidopsis genes that had the experimental evidence supporting their involvement in cell wall biosynthesis or remodeling. The 121 genes were then used as bait genes to query an Arabidopsis co-expression database, and additional genes were identified as neighbors of the bait genes in the network, increasing the number of genes to 548. The 548 Arabidopsis genes were then used to re-query the Arabidopsis co-expression database and re-construct a network that captured additional network neighbors, expanding to a total of 694 genes. The 694 Arabidopsis genes were computationally divided into 22 clusters. Queries of the Populus genome using the Arabidopsis genes revealed 817 Populus orthologs. Functional analysis of gene ontology and tissue-specific gene expression indicated that these Arabidopsis and Populus genes are high likelihood candidates for functional characterization in relation to cell wall biosynthesis.

  13. Large-scale prediction of long non-coding RNA functions in a coding–non-coding gene co-expression network

    PubMed Central

    Liao, Qi; Liu, Changning; Yuan, Xiongying; Kang, Shuli; Miao, Ruoyu; Xiao, Hui; Zhao, Guoguang; Luo, Haitao; Bu, Dechao; Zhao, Haitao; Skogerbø, Geir; Wu, Zhongdao; Zhao, Yi

    2011-01-01

    Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine). PMID:21247874

  14. A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes.

    PubMed

    de Jong, Simone; Boks, Marco P M; Fuller, Tova F; Strengman, Eric; Janson, Esther; de Kovel, Carolien G F; Ori, Anil P S; Vi, Nancy; Mulder, Flip; Blom, Jan Dirk; Glenthøj, Birte; Schubart, Chris D; Cahn, Wiepke; Kahn, René S; Horvath, Steve; Ophoff, Roel A

    2012-01-01

    Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.

  15. Identifying genetic loci and spleen gene coexpression networks underlying immunophenotypes in BXD recombinant inbred mice

    PubMed Central

    Lynch, Rachel M.; Naswa, Sudhir; Rogers, Gary L.; Kania, Stephen A.; Das, Suchita; Chesler, Elissa J.; Saxton, Arnold M.; Langston, Michael A.

    2010-01-01

    The immune system plays a pivotal role in the susceptibility to and progression of a variety of diseases. Due to a strong genetic basis, heritable differences in immune function may contribute to differential disease susceptibility between individuals. Genetic reference populations, such as the BXD (C57BL/6J × DBA/2J) panel of recombinant inbred (RI) mouse strains, provide unique models through which to integrate baseline phenotypes in healthy individuals with heritable risk for disease because of the ability to combine data collected from these populations across both multiple studies and time. We performed basic immunophenotyping (e.g., percentage of circulating B and T lymphocytes and CD4+ and CD8+ T cell subpopulations) in peripheral blood of healthy mice from 41 BXD RI strains to define the immunophenotypic variation in this strain panel and to characterize the genetic architecture that underlies these traits. Significant QTL models that explained the majority (50–77%) of phenotypic variance were derived for each trait and for the T:B cell and CD4+:CD8+ ratios. Combining QTL mapping with spleen gene expression data uncovered two quantitative trait transcripts, Ptprk and Acp1, as candidates for heritable differences in the relative abundance of helper and cytotoxic T cells. These data will be valuable in extracting genetic correlates of the immune system in the BXD panel. In addition, they will be a useful resource for prospective, phenotype-driven model selection to test hypotheses about differential disease or environmental susceptibility between individuals with baseline differences in the composition of the immune system. PMID:20179155

  16. Identification of therapeutic targets for Alzheimer's disease via differentially expressed gene and weighted gene co-expression network analyses.

    PubMed

    Jia, Yujie; Nie, Kun; Li, Jing; Liang, Xinyue; Zhang, Xuezhu

    2016-11-01

    In order to investigate the pathogenic targets and associated biological process of Alzheimer's disease in the present study, mRNA expression profiles (GSE28146) and microRNA (miRNA) expression profiles (GSE16759) were downloaded from the Gene Expression Omnibus database. In GSE28146, eight control samples, and Alzheimer's disease samples comprising seven incipient, eight moderate, seven severe Alzheimer's disease samples, were included. The Affy package in R was used for background correction and normalization of the raw microarray data. The differentially expressed genes (DEGs) and differentially expressed miRNAs were identified using the Limma package. In addition, mRNAs were clustered using weighted gene correlation network analysis, and modules found to be significantly associated with the stages of Alzheimer's disease were screened out. The Database for Annotation, Visualization, and Integrated Discovery was used to perform Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The target genes of the differentially expressed miRNAs were identified using the miRWalk database. Compared with the control samples, 175,59 genes and 90 DEGs were identified in the incipient, moderate and severe Alzheimer's disease samples, respectively. A module, which contained 1,592 genes was found to be closely associated with the stage of Alzheimer's disease and biological processes. In addition, pathways associated with Alzheimer's disease and other neurological diseases were found to be enriched in those genes. A total of 139 overlapped genes were identified between those genes and the DEGs in the three groups. From the miRNA expression profiles, 189 miRNAs were found differentially expressed in the samples from patients with Alzheimer's disease and 1,647 target genes were obtained. In addition, five overlapped genes were identified between those 1,647 target genes and the 139 genes, and these genes may be important pathogenic targets for Alzheimer

  17. In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma.

    PubMed

    Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

    2016-12-01

    As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites.

  18. In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma

    PubMed Central

    Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

    2016-01-01

    As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites. PMID:28261627

  19. Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

    PubMed Central

    Righetti, Karima; Vu, Joseph Ly; Pelletier, Sandra; Vu, Benoit Ly; Glaab, Enrico; Lalanne, David; Pasha, Asher; Patel, Rohan V.; Provart, Nicholas J.; Verdier, Jerome; Leprince, Olivier

    2015-01-01

    Seed longevity, the maintenance of viability during storage, is a crucial factor for preservation of genetic resources and ensuring proper seedling establishment and high crop yield. We used a systems biology approach to identify key genes regulating the acquisition of longevity during seed maturation of Medicago truncatula. Using 104 transcriptomes from seed developmental time courses obtained in five growth environments, we generated a robust, stable coexpression network (MatNet), thereby capturing the conserved backbone of maturation. Using a trait-based gene significance measure, a coexpression module related to the acquisition of longevity was inferred from MatNet. Comparative analysis of the maturation processes in M. truncatula and Arabidopsis thaliana seeds and mining Arabidopsis interaction databases revealed conserved connectivity for 87% of longevity module nodes between both species. Arabidopsis mutant screening for longevity and maturation phenotypes demonstrated high predictive power of the longevity cross-species network. Overrepresentation analysis of the network nodes indicated biological functions related to defense, light, and auxin. Characterization of defense-related wrky3 and nf-x1-like1 (nfxl1) transcription factor mutants demonstrated that these genes regulate some of the network nodes and exhibit impaired acquisition of longevity during maturation. These data suggest that seed longevity evolved by co-opting existing genetic pathways regulating the activation of defense against pathogens. PMID:26410298

  20. Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to ‘Candidatus Liberibacter asiaticus’ infection

    PubMed Central

    2013-01-01

    Background Huanglongbing (HLB) is arguably the most destructive disease for the citrus industry. HLB is caused by infection of the bacterium, Candidatus Liberibacter spp. Several citrus GeneChip studies have revealed thousands of genes that are up- or down-regulated by infection with Ca. Liberibacter asiaticus. However, whether and how these host genes act to protect against HLB remains poorly understood. Results As a first step towards a mechanistic view of citrus in response to the HLB bacterial infection, we performed a comparative transcriptome analysis and found that a total of 21 Probesets are commonly up-regulated by the HLB bacterial infection. In addition, a number of genes are likely regulated specifically at early, late or very late stages of the infection. Furthermore, using Pearson correlation coefficient-based gene coexpression analysis, we constructed a citrus HLB response network consisting of 3,507 Probesets and 56,287 interactions. Genes involved in carbohydrate and nitrogen metabolic processes, transport, defense, signaling and hormone response were overrepresented in the HLB response network and the subnetworks for these processes were constructed. Analysis of the defense and hormone response subnetworks indicates that hormone response is interconnected with defense response. In addition, mapping the commonly up-regulated HLB responsive genes into the HLB response network resulted in a core subnetwork where transport plays a key role in the citrus response to the HLB bacterial infection. Moreover, analysis of a phloem protein subnetwork indicates a role for this protein and zinc transporters or zinc-binding proteins in the citrus HLB defense response. Conclusion Through integrating transcriptome comparison and gene coexpression network analysis, we have provided for the first time a systems view of citrus in response to the Ca. Liberibacter spp. infection causing HLB. PMID:23324561

  1. A developmental transcriptional network for maize defines coexpression modules.

    PubMed

    Downs, Gregory S; Bi, Yong-Mei; Colasanti, Joseph; Wu, Wenqing; Chen, Xi; Zhu, Tong; Rothstein, Steven J; Lukens, Lewis N

    2013-04-01

    Here, we present a genome-wide overview of transcriptional circuits in the agriculturally significant crop species maize (Zea mays). We examined transcript abundance data at 50 developmental stages, from embryogenesis to senescence, for 34,876 gene models and classified genes into 24 robust coexpression modules. Modules were strongly associated with tissue types and related biological processes. Sixteen of the 24 modules (67%) have preferential transcript abundance within specific tissues. One-third of modules had an absence of gene expression in specific tissues. Genes within a number of modules also correlated with the developmental age of tissues. Coexpression of genes is likely due to transcriptional control. For a number of modules, key genes involved in transcriptional control have expression profiles that mimic the expression profiles of module genes, although the expression of transcriptional control genes is not unusually representative of module gene expression. Known regulatory motifs are enriched in several modules. Finally, of the 13 network modules with more than 200 genes, three contain genes that are notably clustered (P < 0.05) within the genome. This work, based on a carefully selected set of major tissues representing diverse stages of maize development, demonstrates the remarkable power of transcript-level coexpression networks to identify underlying biological processes and their molecular components.

  2. Protein Co-Expression Network Analysis (ProCoNA)

    SciTech Connect

    Gibbs, David L.; Baratt, Arie; Baric, Ralph; Kawaoka, Yoshihiro; Smith, Richard D.; Orwoll, Eric S.; Katze, Michael G.; Mcweeney, Shannon K.

    2013-06-01

    Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

  3. Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

    PubMed Central

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database. PMID:25392692

  4. Gene coexpression measures in large heterogeneous samples using count statistics.

    PubMed

    Wang, Y X Rachel; Waterman, Michael S; Huang, Haiyan

    2014-11-18

    With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.

  5. A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways

    PubMed Central

    Musungu, Bryan M.; Bhatnagar, Deepak; Brown, Robert L.; Payne, Gary A.; OBrian, Greg; Fakhoury, Ahmad M.; Geisler, Matt

    2016-01-01

    A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus. PMID:27917194

  6. A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways.

    PubMed

    Musungu, Bryan M; Bhatnagar, Deepak; Brown, Robert L; Payne, Gary A; OBrian, Greg; Fakhoury, Ahmad M; Geisler, Matt

    2016-01-01

    A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus.

  7. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction*

    PubMed Central

    Wang, Jing; Ma, Zihao; Carr, Steven A.; Mertins, Philipp; Zhang, Hui; Zhang, Zhen; Chan, Daniel W.; Ellis, Matthew J. C.; Townsend, R. Reid; Smith, Richard D.; McDermott, Jason E.; Chen, Xian; Paulovich, Amanda G.; Boja, Emily S.; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Rodland, Karin D.; Liebler, Daniel C.; Zhang, Bing

    2017-01-01

    Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies. PMID

  8. Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis.

    PubMed

    Xu, Xinsen; Zhou, Yanyan; Miao, Runchen; Chen, Wei; Qu, Kai; Pang, Qing; Liu, Chang

    2016-06-01

    We performed weighted gene coexpression network analysis (WGCNA) to gain insights into the molecular aspects of hepatocellular carcinoma (HCC). Raw microarray datasets (including 488 samples) were downloaded from the Gene Expression Omnibus (GEO) website. Data were normalized using the RMA algorithm. We utilized the WGCNA to identify the coexpressed genes (modules) after non-specific filtering. Correlation and survival analyses were conducted using the modules, and gene ontology (GO) enrichment was applied to explore the possible mechanisms. Eight distinct modules were identified by the WGCNA. Pink and red modules were associated with liver function, whereas turquoise and black modules were inversely correlated with tumor staging. Poor outcomes were found in the low expression group in the turquoise module and in the high expression group in the red module. In addition, GO enrichment analysis suggested that inflammation, immune, virus-related, and interferon-mediated pathways were enriched in the turquoise module. Several potential biomarkers, such as cyclin-dependent kinase 1 (CDK1), topoisomerase 2α (TOP2A), and serpin peptidase inhibitor clade C (antithrombin) member 1 (SERPINC1), were also identified. In conclusion, gene signatures identified from the genome-based assays could contribute to HCC stratification. WGCNA was able to identify significant groups of genes associated with cancer prognosis.

  9. Weighted gene co-expression network analysis of colorectal cancer liver metastasis genome sequencing data and screening of anti-metastasis drugs.

    PubMed

    Gao, Bo; Shao, Qin; Choudhry, Hani; Marcus, Victoria; Dong, Kung; Ragoussis, Jiannis; Gao, Zu-Hua

    2016-09-01

    Approximately 9% of cancer-related deaths are caused by colorectal cancer (CRC). CRC patients are prone to liver metastasis, which is the most important cause for the high CRC mortality rate. Understanding the molecular mechanism of CRC liver metastasis could help us to find novel targets for the effective treatment of this deadly disease. Using weighted gene co-expression network analysis on the sequencing data of CRC with and with metastasis, we identified 5 colorectal cancer liver metastasis related modules which were labeled as brown, blue, grey, yellow and turquoise. In the brown module, which represents the metastatic tumor in the liver, gene ontology (GO) analysis revealed functions including the G-protein coupled receptor protein signaling pathway, epithelial cell differentiation and cell surface receptor linked signal transduction. In the blue module, which represents the primary CRC that has metastasized, GO analysis showed that the genes were mainly enriched in GO terms including G-protein coupled receptor protein signaling pathway, cell surface receptor linked signal transduction, and negative regulation of cell differentiation. In the yellow and turquoise modules, which represent the primary non-metastatic CRC, 13 downregulated CRC liver metastasis-related candidate miRNAs were identified (e.g. hsa-miR-204, hsa-miR-455, etc.). Furthermore, analyzing the DrugBank database and mining the literature identified 25 and 12 candidate drugs that could potentially block the metastatic processes of the primary tumor and inhibit the progression of metastatic tumors in the liver, respectively. Data generated from this study not only furthers our understanding of the genetic alterations that drive the metastatic process, but also guides the development of molecular-targeted therapy of colorectal cancer liver metastasis.

  10. Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

    PubMed Central

    Rahmatallah, Yasir; Emmert-Streib, Frank; Glazko, Galina

    2014-01-01

    Motivation: To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes. Results: In GSNCA, weight factors are assigned to genes in proportion to the genes’ cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA captures changes in the structure of genes’ cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses. Availability and implementation: Implementation of the GSNCA test in R is available upon request from the authors. Contact: YRahmatallah@uams.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24292935

  11. ImmuCo: a database of gene co-expression in immune cells

    PubMed Central

    Wang, Pingzhang; Qi, Huiying; Song, Shibin; Li, Shuang; Huang, Ningyu; Han, Wenling; Ma, Dalong

    2015-01-01

    Current gene co-expression databases and correlation networks do not support cell-specific analysis. Gene co-expression and expression correlation are subtly different phenomena, although both are likely to be functionally significant. Here, we report a new database, ImmuCo (http://immuco.bjmu.edu.cn), which is a cell-specific database that contains information about gene co-expression in immune cells, identifying co-expression and correlation between any two genes. The strength of co-expression of queried genes is indicated by signal values and detection calls, whereas expression correlation and strength are reflected by Pearson correlation coefficients. A scatter plot of the signal values is provided to directly illustrate the extent of co-expression and correlation. In addition, the database allows the analysis of cell-specific gene expression profile across multiple experimental conditions and can generate a list of genes that are highly correlated with the queried genes. Currently, the database covers 18 human cell groups and 10 mouse cell groups, including 20 283 human genes and 20 963 mouse genes. More than 8.6 × 108 and 7.4 × 108 probe set combinations are provided for querying each human and mouse cell group, respectively. Sample applications support the distinctive advantages of the database. PMID:25326331

  12. Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm

    PubMed Central

    YUE, HONG; YANG, BO; YANG, FANG; HU, XIAO-LI; KONG, FAN-BIN

    2016-01-01

    Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis. PMID:27168792

  13. Canonical correlation analysis for RNA-seq co-expression networks

    PubMed Central

    Hong, Shengjun; Chen, Xiangning; Jin, Li; Xiong, Momiao

    2013-01-01

    Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels. PMID:23460206

  14. Weighted gene co-expression based biomarker discovery for psoriasis detection.

    PubMed

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-11-15

    Psoriasis is a chronic inflammatory disease of the skin with an unknown aetiology. The disease manifests itself as red and silvery scaly plaques distributed over the scalp, lower back and extensor aspects of the limbs. After receiving scant consideration for quite a few years, psoriasis has now become a prominent focus for new drug development. A group of closely connected and differentially co-expressed genes may act in a network and may serve as molecular signatures for an underlying phenotype. A weighted gene coexpression network analysis (WGCNA), a system biology approach has been utilized for identification of new molecular targets for psoriasis. Gene coexpression relationships were investigated in 58 psoriatic lesional samples resulting in five gene modules, clustered based on the gene coexpression patterns. The coexpression pattern was validated using three psoriatic datasets. 10 highly connected and informative genes from each module was selected and termed as psoriasis specific hub signatures. A random forest based binary classifier built using the expression profiles of signature genes robustly distinguished psoriatic samples from the normal samples in the validation set with an accuracy of 0.95 to 1. These signature genes may serve as potential candidates for biomarker discovery leading to new therapeutic targets. WGCNA, the network based approach has provided an alternative path to mine out key controllers and drivers of psoriasis. The study principle from the current work can be extended to other pathological conditions.

  15. An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction.

    PubMed

    Piro, Rosario Michael; Ala, Ugo; Molineris, Ivan; Grassi, Elena; Bracco, Chiara; Perego, Gian Paolo; Provero, Paolo; Di Cunto, Ferdinando

    2011-11-01

    Gene coexpression relationships that are phylogenetically conserved between human and mouse have been shown to provide important clues about gene function that can be efficiently used to identify promising candidate genes for human hereditary disorders. In the past, such approaches have considered mostly generic gene expression profiles that cover multiple tissues and organs. The individual genes of multicellular organisms, however, can participate in different transcriptional programs, operating at scales as different as single-cell types, tissues, organs, body regions or the entire organism. Therefore, systematic analysis of tissue-specific coexpression could be, in principle, a very powerful strategy to dissect those functional relationships among genes that emerge only in particular tissues or organs. In this report, we show that, in fact, conserved coexpression as determined from tissue-specific and condition-specific data sets can predict many functional relationships that are not detected by analyzing heterogeneous microarray data sets. More importantly, we find that, when combined with disease networks, the simultaneous use of both generic (multi-tissue) and tissue-specific conserved coexpression allows a more efficient prediction of human disease genes than the use of generic conserved coexpression alone. Using this strategy, we were able to identify high-probability candidates for 238 orphan disease loci. We provide proof of concept that this combined use of generic and tissue-specific conserved coexpression can be very useful to prioritize the mutational candidates obtained from deep-sequencing projects, even in the case of genetic disorders as heterogeneous as XLMR.

  16. Differential Regulatory Analysis Based on Coexpression Network in Cancer Research.

    PubMed

    Li, Junyi; Li, Yi-Xue; Li, Yuan-Yuan

    2016-01-01

    With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

  17. Co-expression analysis reveals a group of genes potentially involved in regulation of plant response to iron-deficiency.

    PubMed

    Li, Hua; Wang, Lei; Yang, Zhi Min

    2015-01-01

    Iron (Fe) is an essential element for plant growth and development. Iron deficiency results in abnormal metabolisms from respiration to photosynthesis. Exploration of Fe-deficient responsive genes and their networks is critically important to understand molecular mechanisms leading to the plant adaptation to soil Fe-limitation. Co-expression genes are a cluster of genes that have a similar expression pattern to execute relatively biological functions at a stage of development or under a certain environmental condition. They may share a common regulatory mechanism. In this study, we investigated Fe-starved-related co-expression genes from Arabidopsis. From the biological process GO annotation of TAIR (The Arabidopsis Information Resource), 180 iron-deficient responsive genes were detected. Using ATTED-II database, we generated six gene co-expression networks. Among these, two modules of PYE and IRT1 were successfully constructed. There are 30 co-expression genes that are incorporated in the two modules (12 in PYE-module and 18 in IRT1-module). Sixteen of the co-expression genes were well characterized. The remaining genes (14) are poorly or not functionally identified with iron stress. Validation of the 14 genes using real-time PCR showed differential expression under iron-deficiency. Most of the co-expression genes (23/30) could be validated in pye and fit mutant plants with iron-deficiency. We further identified iron-responsive cis-elements upstream of the co-expression genes and found that 22 out of 30 genes contain the iron-responsive motif IDE1. Furthermore, some auxin and ethylene-responsive elements were detected in the promoters of the co-expression genes. These results suggest that some of the genes can be also involved in iron stress response through the phytohormone-responsive pathways.

  18. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis.

    PubMed

    Clarke, Colin; Madden, Stephen F; Doolan, Padraig; Aherne, Sinead T; Joyce, Helena; O'Driscoll, Lorraine; Gallagher, William M; Hennessy, Bryan T; Moriarty, Michael; Crown, John; Kennedy, Susan; Clynes, Martin

    2013-10-01

    Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

  19. Lentiviral vector system for coordinated constitutive and drug controlled tetracycline-regulated gene co-expression.

    PubMed

    Stahlhut, Maike; Schwarzer, Adrian; Eder, Matthias; Yang, Min; Li, Zhixiong; Morgan, Michael; Schambach, Axel; Kustikova, Olga S

    2015-09-01

    Constitutive co-expression of cooperating transgenes using retroviral integrating vectors is frequently used for genetic modification of different cell types to establish therapeutic or cancer models. However, such approaches are unable to dissect the influence of dose, order and reversibility of transgene expression on the fate of newly developed therapeutic/malignant phenotypes. We present a modular lentiviral vector system, which provides expression of constitutive and inducible components. To demonstrate its functionality, we constitutively expressed the well-described transcription factor Meis1 followed by inducible co-expression of collaborating partner Hoxa9 under the control of tetracycline responsive promoters in murine fibroblasts and primary hematopoietic progenitor cells (HPCs). Fluorescent markers to track transgene co-expression revealed tightly controlled, efficiently inducible and reversible but cell type dependent gene transfer over time. We demonstrated dose-dependent blockade of myeloid differentiation when both Meis1/Hoxa9 were concomitantly overexpressed in primary HPCs in vitro, but the absence of the transformed phenotype in non-induced samples or when Hoxa9 expression was down-regulated. This system combines the advantages of lentiviral gene transfer and the opportunity for drug-controlled co-expression of multiple transgenes to dissect, among others, gene networks governing complex cell behavior, such as proto-oncogene dose-dependent leukemogenic pathways or collaborating mechanisms of genes enhancing competitive fitness of hematopoietic cells.

  20. Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia

    PubMed Central

    Chan, Lawrence WC; Lin, Xihong; Yung, Godwin; Lui, Thomas; Chiu, Ya Ming; Wang, Fengfeng; Tsui, Nancy BY; Cho, William CS; Yip, SP; Siu, Parco M.; Wong, SC Cesar; Yung, Benjamin YM

    2015-01-01

    Co-expression analysis reveals useful dysregulation patterns of gene cooperativeness for understanding cancer biology and identifying new targets for treatment. We developed a structural strategy to identify co-expressed gene networks that are important for chronic myelogenous leukemia (CML). This strategy compared the distributions of expressional correlations between CML and normal states, and it identified a data-driven threshold to classify strongly co-expressed networks that had the best coherence with CML. Using this strategy, we found a transcriptome-wide reduction of co-expression connectivity in CML, reflecting potentially loosened molecular regulation. Conversely, when we focused on nucleophosmin 1 (NPM1) associated networks, NPM1 established more co-expression linkages with BCR-ABL pathways and ribosomal protein networks in CML than normal. This finding implicates a new role of NPM1 in conveying tumorigenic signals from the BCR-ABL oncoprotein to ribosome biogenesis, affecting cellular growth. Transcription factors may be regulators of the differential co-expression patterns between CML and normal. PMID:26205693

  1. Genetic architecture of wood properties based on association analysis and co-expression networks in white spruce.

    PubMed

    Lamara, Mebarek; Raherison, Elie; Lenz, Patrick; Beaulieu, Jean; Bousquet, Jean; MacKay, John

    2016-04-01

    Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P < 0.05 to maximize discovery. Over-representation of genes associated for nearly all traits was found in a xylem preferential co-expression group developed in independent experiments. A xylem co-expression network was reconstructed with 180 wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits.

  2. CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra.

    PubMed

    Santos, Suzana de Siqueira; Galatro, Thais Fernanda de Almeida; Watanabe, Rodrigo Akira; Oba-Shinjo, Sueli Mieko; Nagahashi Marie, Suely Kazue; Fujita, André

    2015-01-01

    Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correlation (association) structure among the genes. In this work, we present CoGA (Co-expression Graph Analyzer), an R package for the identification of groups of differentially associated genes between two phenotypes. The analysis is based on concepts of Information Theory applied to the spectral distributions of the gene co-expression graphs, such as the spectral entropy to measure the randomness of a graph structure and the Jensen-Shannon divergence to discriminate classes of graphs. The package also includes common measures to compare gene co-expression networks in terms of their structural properties, such as centrality, degree distribution, shortest path length, and clustering coefficient. Besides the structural analyses, CoGA also includes graphical interfaces for visual inspection of the networks, ranking of genes according to their "importance" in the network, and the standard differential expression analysis. We show by both simulation experiments and analyses of real data that the statistical tests performed by CoGA indeed control the rate of false positives and is able to identify differentially co-expressed genes that other methods failed.

  3. Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

    PubMed Central

    2010-01-01

    Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy. PMID:20950417

  4. DTW-MIC Coexpression Networks from Time-Course Data

    PubMed Central

    Riccadonna, Samantha; Jurman, Giuseppe; Visintainer, Roberto; Filosi, Michele; Furlanello, Cesare

    2016-01-01

    When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy. PMID:27031641

  5. Construction and application of a co-expression network in Mycobacterium tuberculosis

    PubMed Central

    Jiang, Jun; Sun, Xian; Wu, Wei; Li, Li; Wu, Hai; Zhang, Lu; Yu, Guohua; Li, Yao

    2016-01-01

    Because of its high pathogenicity and infectivity, tuberculosis is a serious threat to human health. Some information about the functions of the genes in Mycobacterium tuberculosis genome was currently available, but it was not enough to explore transcriptional regulatory mechanisms. Here, we applied the WGCNA (Weighted Gene Correlation Network Analysis) algorithm to mine pooled microarray datasets for the M. tuberculosis H37Rv strain. We constructed a co-expression network that was subdivided into 78 co-expression gene modules. The different response to two kinds of vitro models (a constant 0.2% oxygen hypoxia model and a Wayne model) were explained based on these modules. We identified potential transcription factors based on high Pearson’s correlation coefficients between the modules and genes. Three modules that may be associated with hypoxic stimulation were identified, and their potential transcription factors were predicted. In the validation experiment, we determined the expression levels of genes in the modules under hypoxic condition and under overexpression of potential transcription factors (Rv0081, furA (Rv1909c), Rv0324, Rv3334, and Rv3833). The experimental results showed that the three identified modules related to hypoxia and that the overexpression of transcription factors could significantly change the expression levels of genes in the corresponding modules. PMID:27328747

  6. Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

    DTIC Science & Technology

    2014-09-16

    Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules Gregory J. Tawa1*, Mohamed Diwan M. AbdulHameed1, Xueping Yu1...Abstract Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due...identify groups of co-expressed genes (modules) specific to injury endpoints in the liver . We identified 78 such gene co-expression modules associated

  7. Protein-protein interaction and gene co-expression maps of ARFs and Aux/IAAs in Arabidopsis

    PubMed Central

    Piya, Sarbottam; Shrestha, Sandesh K.; Binder, Brad; Stewart, C. Neal; Hewezi, Tarek

    2014-01-01

    The phytohormone auxin regulates nearly all aspects of plant growth and development. Based on the current model in Arabidopsis thaliana, Auxin/indole-3-acetic acid (Aux/IAA) proteins repress auxin-inducible genes by inhibiting auxin response transcription factors (ARFs). Experimental evidence suggests that heterodimerization between Aux/IAA and ARF proteins are related to their unique biological functions. The objective of this study was to generate the Aux/IAA-ARF protein-protein interaction map using full length sequences and locate the interacting protein pairs to specific gene co-expression networks in order to define tissue-specific responses of the Aux/IAA-ARF interactome. Pairwise interactions between 19 ARFs and 29 Aux/IAAs resulted in the identification of 213 specific interactions of which 79 interactions were previously unknown. The incorporation of co-expression profiles with protein-protein interaction data revealed a strong correlation of gene co-expression for 70% of the ARF-Aux/IAA interacting pairs in at least one tissue/organ, indicative of the biological significance of these interactions. Importantly, ARF4-8 and 19, which were found to interact with almost all Aux-Aux/IAA showed broad co-expression relationships with Aux/IAA genes, thus, formed the central hubs of the co-expression network. Our analyses provide new insights into the biological significance of ARF-Aux/IAA associations in the morphogenesis and development of various plant tissues and organs. PMID:25566309

  8. Dissecting nutrient-related co-expression networks in phosphate starved poplars

    PubMed Central

    Kavka, Mareike; Polle, Andrea

    2017-01-01

    Phosphorus (P) is an essential plant nutrient, but its availability is often limited in soil. Here, we studied changes in the transcriptome and in nutrient element concentrations in leaves and roots of poplars (Populus × canescens) in response to P deficiency. P starvation resulted in decreased concentrations of S and major cations (K, Mg, Ca), in increased concentrations of N, Zn and Al, while C, Fe and Mn were only little affected. In roots and leaves >4,000 and >9,000 genes were differently expressed upon P starvation. These genes clustered in eleven co-expression modules of which seven were correlated with distinct elements in the plant tissues. One module (4.7% of all differentially expressed genes) was strongly correlated with changes in the P concentration in the plant. In this module the GO term “response to P starvation” was enriched with phosphoenolpyruvate carboxylase kinases, phosphatases and pyrophosphatases as well as regulatory domains such as SPX, but no phosphate transporters. The P-related module was also enriched in genes of the functional category “galactolipid synthesis”. Galactolipids substitute phospholipids in membranes under P limitation. Two modules, one correlated with C and N and the other with biomass, S and Mg, were connected with the P-related module by co-expression. In these modules GO terms indicating “DNA modification” and “cell division” as well as “defense” and “RNA modification” and “signaling” were enriched; they contained phosphate transporters. Bark storage proteins were among the most strongly upregulated genes in the growth-related module suggesting that N, which could not be used for growth, accumulated in typical storage compounds. In conclusion, weighted gene coexpression network analysis revealed a hierarchical structure of gene clusters, which separated phosphate starvation responses correlated with P tissue concentrations from other gene modules, which most likely represented

  9. Co-expression networks in generation of induced pluripotent stem cells.

    PubMed

    Paul, Sharan; Pflieger, Lance; Dansithong, Warunee; Figueroa, Karla P; Gao, Fuying; Coppola, Giovanni; Pulst, Stefan M

    2016-02-18

    We developed an adenoviral vector, in which Yamanaka's four reprogramming factors (RFs) were controlled by individual CMV promoters in a single cassette (Ad-SOcMK). This permitted coordinated expression of RFs (SOX2, OCT3/4, c-MYC and KLF4) in a cell for a transient period of time, synchronizing the reprogramming process with the majority of transduced cells assuming induced pluripotent stem cell (iPSC)-like characteristics as early as three days post-transduction. These reprogrammed cells resembled human embryonic stem cells (ESCs) with regard to morphology, biomarker expression, and could be differentiated into cells of the germ layers in vitro and in vivo. These iPSC-like cells, however, failed to expand into larger iPSC colonies. The short and synchronized reprogramming process allowed us to study global transcription changes within short time intervals. Weighted gene co-expression network analysis (WGCNA) identified sixteen large gene co-expression modules, each including members of gene ontology categories involved in cell differentiation and development. In particular, the brown module contained a significant number of ESC marker genes, whereas the turquoise module contained cell-cycle-related genes that were downregulated in contrast to upregulation in human ESCs. Strong coordinated expression of all four RFs via adenoviral transduction may constrain stochastic processes and lead to silencing of genes important for cellular proliferation.

  10. Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

    DOE PAGES

    Baldwin, Nicole E.; Chesler, Elissa J.; Kirov, Stefan; ...

    2005-01-01

    Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis -regulatory element discovery.more » The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.« less

  11. Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

    PubMed Central

    Shoichet, Brian K.; Gillis, Jesse

    2016-01-01

    The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63–0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited. PMID:27467773

  12. Co-expression network analyses identify functional modules associated with development and stress response in Gossypium arboreum

    PubMed Central

    You, Qi; Zhang, Liwei; Yi, Xin; Zhang, Kang; Yao, Dongxia; Zhang, Xueyan; Wang, Qianhua; Zhao, Xinhua; Ling, Yi; Xu, Wenying; Li, Fuguang; Su, Zhen

    2016-01-01

    Cotton is an economically important crop, essential for the agriculture and textile industries. Through integrating transcriptomic data, we discovered that multi-dimensional co-expression network analysis was powerful for predicting cotton gene functions and functional modules. Here, the recently available transcriptomic data on Gossypium arboreum, including data on multiple growth stages of tissues and stress treatment samples were applied to construct a co-expression network exploring multi-dimensional expression (development and stress) through multi-layered approaches. Based on differential gene expression and network analysis, a fibre development regulatory module of the gene GaKNL1 was found to regulate the second cell wall through repressing the activity of REVOLUTA, and a tissue-selective module of GaJAZ1a was examined in response to water stress. Moreover, comparative genomics analysis of the JAZ1-related regulatory module revealed high conservation across plant species. In addition, 1155 functional modules were identified through integrating the co-expression network, module classification and function enrichment tools, which cover functions such as metabolism, stress responses, and transcriptional regulation. In the end, an online platform was built for network analysis (http://structuralbiology.cau.edu.cn/arboreum), which could help to refine the annotation of cotton gene function and establish a data mining system to identify functional genes or modules with important agronomic traits. PMID:27922095

  13. Recursive Indirect-Paths Modularity (RIP-M) for Detecting Community Structure in RNA-Seq Co-expression Networks.

    PubMed

    Rahmani, Bahareh; Zimmermann, Michael T; Grill, Diane E; Kennedy, Richard B; Oberg, Ann L; White, Bill C; Poland, Gregory A; McKinney, Brett A

    2016-01-01

    Clusters of genes in co-expression networks are commonly used as functional units for gene set enrichment detection and increasingly as features (attribute construction) for statistical inference and sample classification. One of the practical challenges of clustering for these purposes is to identify an optimal partition of the network where the individual clusters are neither too large, prohibiting interpretation, nor too small, precluding general inference. Newman Modularity is a spectral clustering algorithm that automatically finds the number of clusters, but for many biological networks the cluster sizes are suboptimal. In this work, we generalize Newman Modularity to incorporate information from indirect paths in RNA-Seq co-expression networks. We implement a merge-and-split algorithm that allows the user to constrain the range of cluster sizes: large enough to capture genes in relevant pathways, yet small enough to resolve distinct functions. We investigate the properties of our recursive indirect-pathways modularity (RIP-M) and compare it with other clustering methods using simulated co-expression networks and RNA-seq data from an influenza vaccine response study. RIP-M had higher cluster assignment accuracy than Newman Modularity for finding clusters in simulated co-expression networks for all scenarios, and RIP-M had comparable accuracy to Weighted Gene Correlation Network Analysis (WGCNA). RIP-M was more accurate than WGCNA for modest hard thresholds and comparable for high, while WGCNA was slightly more accurate for soft thresholds. In the vaccine study data, RIP-M and WGCNA enriched for a comparable number of immunologically relevant pathways.

  14. Analysis of the dynamic co-expression network of heart regeneration in the zebrafish

    NASA Astrophysics Data System (ADS)

    Rodius, Sophie; Androsova, Ganna; Götz, Lou; Liechti, Robin; Crespo, Isaac; Merz, Susanne; Nazarov, Petr V.; de Klein, Niek; Jeanty, Céline; González-Rosa, Juan M.; Muller, Arnaud; Bernardin, Francois; Niclou, Simone P.; Vallar, Laurent; Mercader, Nadia; Ibberson, Mark; Xenarios, Ioannis; Azuaje, Francisco

    2016-05-01

    The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

  15. ccNET: Database of co-expression networks with functional modules for diploid and polyploid Gossypium

    PubMed Central

    You, Qi; Xu, Wenying; Zhang, Kang; Zhang, Liwei; Yi, Xin; Yao, Dongxia; Wang, Chunchao; Zhang, Xueyan; Zhao, Xinhua; Provart, Nicholas J.; Li, Fuguang; Su, Zhen

    2017-01-01

    Plant genera with both diploid and polyploid species are a common evolutionary occurrence. Polyploids, especially allopolyploids such as cotton and wheat, are a great model system for heterosis research. Here, we have integrated genome sequences and transcriptome data of Gossypium species to construct co-expression networks and identified functional modules from different cotton species, including 1155 and 1884 modules in G. arboreum and G. hirsutum, respectively. We overlayed the gene expression results onto the co-expression network. We further provided network comparison analysis for orthologous genes across the diploid and allotetraploid Gossypium. We also constructed miRNA-target networks and predicted PPI networks for both cotton species. Furthermore, we integrated in-house ChIP-seq data of histone modification (H3K4me3) together with cis-element analysis and gene sets enrichment analysis tools for studying possible gene regulatory mechanism in Gossypium species. Finally, we have constructed an online ccNET database (http://structuralbiology.cau.edu.cn/gossypium) for comparative gene functional analyses at a multi-dimensional network and epigenomic level across diploid and polyploid Gossypium species. The ccNET database will be beneficial for community to yield novel insights into gene/module functions during cotton development and stress response, and might be useful for studying conservation and diversity in other polyploid plants, such as T. aestivum and Brassica napus. PMID:28053168

  16. A Gene Recommender Algorithm to Identify Coexpressed Genes in C. elegans

    PubMed Central

    Owen, Art B.; Stuart, Josh; Mach, Kathy; Villeneuve, Anne M.; Kim, Stuart

    2003-01-01

    One of the most important uses of whole-genome expression data is for the discovery of new genes with similar function to a given list of genes (the query) already known to have closely related function. We have developed an algorithm, called the gene recommender, that ranks genes according to how strongly they correlate with a set of query genes in those experiments for which the query genes are most strongly coregulated. We used the gene recommender to find other genes coexpressed with several sets of query genes, including genes known to function in the retinoblastoma complex. Genetic experiments confirmed that one gene (JC8.6) identified by the gene recommender acts with lin-35 Rb to regulate vulval cell fates, and that another gene (wrm-1) acts antagonistically. We find that the gene recommender returns lists of genes with better precision, for fixed levels of recall, than lists generated using the C. elegans expression topomap. PMID:12902378

  17. An integrative approach predicted co-expression sub-networks regulating properties of stem cells and differentiation.

    PubMed

    Sahu, Mousumi; Mallick, Bibekanand

    2016-10-01

    The differentiation of human Embryonic Stem Cells (hESCs) is accompanied by the formation of different intermediary cells, gradually losing its stemness and acquiring differentiation. The precise mechanisms underlying hESCs integrity and its differentiation into fibroblast (Fib) are still elusive. Here, we aimed to assess important genes and co-expression sub-networks responsible for stemness, early differentiation of hESCs into embryoid bodies (EBs) and its lineage specification into Fibs. To achieve this, we compared transcriptional profiles of hESCs-EBs and EBs-Fibs and obtained differentially expressed genes (DEGs) exclusive to hESCs-EBs (early differentiation), EBs-Fibs (late differentiation) and common DEGs in hESCs-EBs and EBs-Fibs. Then, we performed gene set enrichment analysis (GSEA) followed by overrepresentation study and identified key genes for each gene category. The regulations of these genes were studied by integrating ChIP-Seq data of core transcription factors (TFs) and histone methylation marks in hESCs. Finally, we identified co-expression sub-networks from key genes of each gene category using k-clique sub-network extraction method. Our study predicted seven genes edicting core stemness properties forming a co-expression network. From the pathway analysis of sub-networks of hESCs-EBs, we hypothesize that FGF2 is contributing to pluripotent transcription network of hESCs in association with DNMT3B and JARID2 thereby facilitating cell proliferation. On the contrary, FGF2 is found to promote cell migration in Fibs along with DDR2, CAV1, DAB2, and PARVA. Moreover, our study identified three k-clique sub-networks regulating TGF-β signaling pathway thereby promoting EBs to Fibs differentiation by: (i) modulating extracellular matrix involving ITGB1, TGFB1I1 and GBP1, (ii) regulating cell cycle remodeling involving CDKN1A, JUNB and DUSP1 and (iii) helping in epithelial to mesenchymal transition (EMT) involving THBS1, INHBA and LOX. This study put

  18. Co-expression network analysis of Down's syndrome based on microarray data

    PubMed Central

    Zhao, Jianping; Zhang, Zhengguo; Ren, Shumin; Zong, Yanan; Kong, Xiangdong

    2016-01-01

    Down's syndrome (DS) is a type of chromosome disease. The present study aimed to explore the underlying molecular mechanisms of DS. GSE5390 microarray data downloaded from the gene expression omnibus database was used to identify differentially expressed genes (DEGs) in DS. Pathway enrichment analysis of the DEGs was performed, followed by co-expression network construction. Significant differential modules were mined by mutual information, followed by functional analysis. The accuracy of sample classification for the significant differential modules of DEGs was evaluated by leave-one-out cross-validation. A total of 997 DEGs, including 638 upregulated and 359 downregulated genes, were identified. Upregulated DEGs were enriched in 15 pathways, such as cell adhesion molecules, whereas downregulated DEGs were enriched in maturity onset diabetes of the young. Three significant differential modules with the highest discriminative scores (mutual information>0.35) were selected from a co-expression network. The classification accuracy of GSE16677 expression profile samples was 54.55% and 72.73% when characterized by 12 DEGs and 3 significant differential modules, respectively. Genes in significant differential modules were significantly enriched in 5 functions, including the endoplasmic reticulum (P=0.018) and regulation of apoptosis (P=0.061). The identified DEGs, in particular the 12 DEGs in the significant differential modules, such as B-cell lymphoma 2-associated transcription factor 1, heat shock protein 90 kDa beta member 1, UBX domain-containing protein 2 and transmembrane protein 50B, may serve important roles in the pathogenesis of DS. PMID:27588071

  19. Gene coexpression as Hebbian learning in prokaryotic genomes.

    PubMed

    Vey, Gregory

    2013-12-01

    Biological interaction networks represent a powerful tool for characterizing intracellular functional relationships, such as transcriptional regulation and protein interactions. Although artificial neural networks are routinely employed for a broad range of applications across computational biology, their underlying connectionist basis has not been extensively applied to modeling biological interaction networks. In particular, the Hopfield network offers nonlinear dynamics that represent the minimization of a system energy function through temporally distinct rewiring events. Here, a scaled energy minimization model is presented to test the feasibility of deriving a composite biological interaction network from multiple constituent data sets using the Hebbian learning principle. The performance of the scaled energy minimization model is compared against the standard Hopfield model using simulated data. Several networks are also derived from real data, compared to one another, and then combined to produce an aggregate network. The utility and limitations of the proposed model are discussed, along with possible implications for a genomic learning analogy where the fundamental Hebbian postulate is rendered into its genomic equivalent: Genes that function together junction together.

  20. Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

    NASA Technical Reports Server (NTRS)

    Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

    2000-01-01

    Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

  1. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism

    PubMed Central

    Willsey, A. Jeremy; Sanders, Stephan J.; Li, Mingfeng; Dong, Shan; Tebbenkamp, Andrew T.; Muhle, Rebecca A.; Reilly, Steven K.; Lin, Leon; Fertuzinhos, Sofia; Miller, Jeremy A.; Murtha, Michael T.; Bichsel, Candace; Niu, Wei; Cotney, Justin; Ercan-Sencicek, A. Gulhan; Gockley, Jake; Gupta, Abha; Han, Wenqi; He, Xin; Hoffman, Ellen; Klei, Lambertus; Lei, Jing; Liu, Wenzhong; Liu, Li; Lu, Cong; Xu, Xuming; Zhu, Ying; Mane, Shrikant M.; Lein, Edward S.; Wei, Liping; Noonan, James P.; Roeder, Kathryn; Devlin, Bernie; Šestan, Nenad; State, Matthew W.

    2013-01-01

    SUMMARY Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD “seed” genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology. PMID:24267886

  2. Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants

    PubMed Central

    Weston, David J; Gunter, Lee E; Rogers, Alistair; Wullschleger, Stan D

    2008-01-01

    Background One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g., diagnosis) and predict phenotypic outcome (e.g., patient survival). Ecology currently lacks an analogous approach where genomic information can be used to diagnose the presence of a given physiological state (e.g., stress response) and then predict likely phenotypic outcomes (e.g., stress duration and tolerance, fitness). Results Here, we demonstrate that a compendium of genomic signatures can be used to classify the plant abiotic stress phenotype in Arabidopsis according to the architecture of the transcriptome, and then be linked with gene coexpression network analysis to determine the underlying genes governing the phenotypic response. Using this approach, we confirm the existence of known stress responsive pathways and marker genes, report a common abiotic stress responsive transcriptome and relate phenotypic classification to stress duration. Conclusion Linking genomic signatures to gene coexpression analysis provides a unique method of relating an observed plant phenotype to changes in gene expression that underlie that phenotype. Such information is critical to current and future investigations in plant biology and, in particular, to evolutionary ecology, where a mechanistic understanding of adaptive physiological responses to abiotic stress can provide researchers with a tool of great predictive value in understanding species and population level adaptation to climate change. PMID:18248680

  3. Connecting genes, coexpression modules, and molecular signitures to environmental stress phenotypes in plants

    SciTech Connect

    Weston, David; Gunter, Lee E; Rogers, Alistair; Wullschleger, Stan D

    2008-01-01

    Background One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g., diagnosis) and predict phenotypic outcome (e.g., patient survival). Ecology currently lacks an analogous approach where genomic information can be used to diagnose the presence of a given physiological state (e.g., stress response) and then predict likely phenotypic outcomes (e.g., stress duration and tolerance, fitness). Results Here, we demonstrate that a compendium of genomic signatures can be used to classify the plant abiotic stress phenotype in Arabidopsis according to the architecture of the transcriptome, and then be linked with gene coexpression network analysis to determine the underlying genes governing the phenotypic response. Using this approach, we confirm the existence of known stress responsive pathways and marker genes, report a common abiotic stress responsive transcriptome and relate phenotypic classification to stress duration. Conclusion Linking genomic signatures to gene coexpression analysis provides a unique method of relating an observed plant phenotype to changes in gene expression that underlie that phenotype. Such information is critical to current and future investigations in plant biology and, in particular, to evolutionary ecology, where a mechanistic understanding of adaptive physiological responses to abiotic stress can provide researchers with a tool of great predictive value in understanding species and population level adaptation to climate change.

  4. Analysis of functional and pathway association of differential co-expressed genes: a case study in drug addiction.

    PubMed

    Li, Zi-hui; Liu, Yu-feng; Li, Ke-ning; Duanmu, Hui-zi; Chang, Zhi-qiang; Li, Zhen-qi; Zhang, Shan-zhen; Xu, Yan

    2012-02-01

    Drug addiction has been considered as a kind of chronic relapsing brain disease influenced by both genetic and environmental factors. At present, many causative genes and pathways related to diverse kinds of drug addiction have been discovered, while less attention has been paid to common mechanisms shared by different drugs underlying addiction. By applying a co-expression meta-analysis method to mRNA expression profiles of alcohol, cocaine, heroin addicted and normal samples, we identified significant gene co-expression pairs. As co-expression networks of drug group and control group constructed, associated function term pairs and pathway pairs reflected by co-expression pattern changes were discovered by integrating functional and pathway information respectively. The results indicated that respiratory electron transport chain, synaptic transmission, mitochondrial electron transport, signal transduction, locomotory behavior, response to amphetamine, negative regulation of cell migration, glucose regulation of insulin secretion, signaling by NGF, diabetes pathways, integration of energy metabolism, dopamine receptors may play an important role in drug addiction. In addition, the results can provide theory support for studies of addiction mechanisms.

  5. A specific variant of the PHR1 binding site is highly enriched in the Arabidopsis phosphate-responsive phospholipase DZ2 coexpression network.

    PubMed

    Acevedo-Hernández, Gustavo; Oropeza-Aburto, Araceli; Herrera-Estrella, Luis

    2012-08-01

    PLDZ2 is a member of the Arabidopsis phospholipase D gene family that is induced in both shoot and root in response to phosphate (Pi) starvation. Recently, through deletion and gain-of-function analyses of the PLDZ2 promoter, we identified a 65 bp region (denominated enhancer EZ2) capable of conferring tissue-specific and low-Pi responses to a minimal inactive promoter. The EZ2 element contains two P1BS motifs, each of which is the binding site for PHR1 and related transcription factors. This structural organization is evolutionarily conserved in orthologous promoters within the rosid clade. To determine whether EZ2 is significantly over-represented in Arabidopsis genes coexpressed with PLDZ2, we constructed a PLDZ2 coexpression network containing 26 genes, almost half of them encoding enzymes or regulatory proteins involved in Pi recycling. A variant of the P1BS motif was found to be highly enriched in the promoter regions of these coexpressed genes, showing an EZ2-like arrangement in seven of them. No other motifs were significantly enriched. The over-representation of the EZ2 arrangement of P1BS motifs in the promoters of genes coexpressed with PLDZ2, suggests this unit has a particularly important role as a regulatory element in a coexpression network involved in the release of Pi from phospholipids and other molecules under Pi-limiting conditions.

  6. A specific variant of the PHR1 binding site is highly enriched in the Arabidopsis phosphate-responsive phospholipase DZ2 coexpression network

    PubMed Central

    Acevedo-Hernández, Gustavo; Oropeza-Aburto, Araceli; Herrera-Estrella, Luis

    2012-01-01

    PLDZ2 is a member of the Arabidopsis phospholipase D gene family that is induced in both shoot and root in response to phosphate (Pi) starvation. Recently, through deletion and gain-of-function analyses of the PLDZ2 promoter, we identified a 65 bp region (denominated enhancer EZ2) capable of conferring tissue-specific and low-Pi responses to a minimal inactive promoter. The EZ2 element contains two P1BS motifs, each of which is the binding site for PHR1 and related transcription factors. This structural organization is evolutionarily conserved in orthologous promoters within the rosid clade. To determine whether EZ2 is significantly over-represented in Arabidopsis genes coexpressed with PLDZ2, we constructed a PLDZ2 coexpression network containing 26 genes, almost half of them encoding enzymes or regulatory proteins involved in Pi recycling. A variant of the P1BS motif was found to be highly enriched in the promoter regions of these coexpressed genes, showing an EZ2-like arrangement in seven of them. No other motifs were significantly enriched. The over-representation of the EZ2 arrangement of P1BS motifs in the promoters of genes coexpressed with PLDZ2, suggests this unit has a particularly important role as a regulatory element in a coexpression network involved in the release of Pi from phospholipids and other molecules under Pi-limiting conditions. PMID:22836502

  7. EXPath tool-a system for comprehensively analyzing regulatory pathways and coexpression networks from high-throughput transcriptome data.

    PubMed

    Zheng, Han-Qin; Wu, Nai-Yun; Chow, Chi-Nga; Tseng, Kuan-Chieh; Chien, Chia-Hung; Hung, Yu-Cheng; Li, Guan-Zhen; Chang, Wen-Chi

    2017-03-13

    Next generation sequencing (NGS) has become the mainstream approach for monitoring gene expression levels in parallel with various experimental treatments. Unfortunately, there is no systematical webserver to comprehensively perform further analysis based on the huge amount of preliminary data that is obtained after finishing the process of gene annotation. Therefore, a user-friendly and effective system is required to mine important genes and regulatory pathways under specific conditions from high-throughput transcriptome data. EXPath Tool (available at: http://expathtool.itps.ncku.edu.tw/) was developed for the pathway annotation and comparative analysis of user-customized gene expression profiles derived from microarray or NGS platforms under various conditions to infer metabolic pathways for all organisms in the KEGG database. EXPath Tool contains several functions: access the gene expression patterns and the candidates of co-expression genes; dissect differentially expressed genes (DEGs) between two conditions (DEGs search), functional grouping with pathway and GO (Pathway/GO enrichment analysis), and correlation networks (co-expression analysis), and view the expression patterns of genes involved in specific pathways to infer the effects of the treatment. Additionally, the effectively of EXPath Tool has been performed by a case study on IAA-responsive genes. The results demonstrated that critical hub genes under IAA treatment could be efficiently identified.

  8. From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation Among Gene Classes from Large-Scale Expression Data

    NASA Technical Reports Server (NTRS)

    Mjolsness, Eric; Castano, Rebecca; Mann, Tobias; Wold, Barbara

    2000-01-01

    We provide preliminary evidence that existing algorithms for inferring small-scale gene regulation networks from gene expression data can be adapted to large-scale gene expression data coming from hybridization microarrays. The essential steps are (I) clustering many genes by their expression time-course data into a minimal set of clusters of co-expressed genes, (2) theoretically modeling the various conditions under which the time-courses are measured using a continuous-time analog recurrent neural network for the cluster mean time-courses, (3) fitting such a regulatory model to the cluster mean time courses by simulated annealing with weight decay, and (4) analysing several such fits for commonalities in the circuit parameter sets including the connection matrices. This procedure can be used to assess the adequacy of existing and future gene expression time-course data sets for determining transcriptional regulatory relationships such as coregulation.

  9. Differentially co-expressed genes in postmortem prefrontal cortex of individuals with alcohol use disorders: Influence on alcohol metabolism-related pathways

    PubMed Central

    Zhang, Huiping; Wang, Fan; Xu, Hongqin; Liu, Yawen; Liu, Jin; Zhao, Hongyu; Gelernter, Joel

    2014-01-01

    Chronic alcohol consumption may induce gene expression alterations in brain reward regions such as the prefrontal cortex (PFC), modulating the risk of alcohol use disorders (AUDs). Transcriptome profiles of 23 AUD cases and 23 matched controls (16 pairs of males and 7 pairs of females) in postmortem PFC were generated using Illumina’s HumanHT-12 v4 Expression BeadChip. Probe-level differentially expressed genes and gene modules in AUD subjects were identified using multiple linear regression and weighted gene co-expression network analyses. The enrichment of differentially co-expressed genes in alcohol dependence-associated genes identified by genome-wide association studies (GWAS) was examined using gene set enrichment analysis. Biological pathways overrepresented by differentially co-expressed genes were uncovered using DAVID bioinformatics resources. Three AUD-associated gene modules in males [Module 1 (561 probes mapping to 505 genes): r=0.42, Pcorrelation=0.020; Module 2 (815 probes mapping to 713 genes): r=0.41, Pcorrelation=0.020; Module 3 (1,446 probes mapping to 1,305 genes): r=−0.38, Pcorrelation=0.030] and one AUD-associated gene module in females [Module 4 (683 probes mapping to 652 genes): r=0.64, Pcorrelation=0.010] were identified. Differentially expressed genes mapped by significant expression probes (Pnominal≤0.05) clustered in Modules 1 and 2 were enriched in GWAS-identified alcohol dependence-associated genes [Module 1 (134 genes): P=0.028; Module 2 (243 genes): P=0.004]. These differentially expressed genes, including ALDH2, ALDH7A1, and ALDH9A1, are involved in cellular functions such as aldehyde detoxification, mitochondrial function, and fatty acid metabolism. Our study revealed differentially co-expressed genes in postmortem PFC of AUD subjects and demonstrated that some of these differentially co-expressed genes participate in alcohol metabolism. PMID:25073604

  10. Co-expression network analysis reveals transcription factors associated to cell wall biosynthesis in sugarcane.

    PubMed

    Ferreira, Savio Siqueira; Hotta, Carlos Takeshi; Poelking, Viviane Guzzo de Carli; Leite, Debora Chaves Coelho; Buckeridge, Marcos Silveira; Loureiro, Marcelo Ehlers; Barbosa, Marcio Henrique Pereira; Carneiro, Monalisa Sampaio; Souza, Glaucia Mendes

    2016-05-01

    Sugarcane is a hybrid of Saccharum officinarum and Saccharum spontaneum, with minor contributions from other species in Saccharum and other genera. Understanding the molecular basis of cell wall metabolism in sugarcane may allow for rational changes in fiber quality and content when designing new energy crops. This work describes a comparative expression profiling of sugarcane ancestral genotypes: S. officinarum, S. spontaneum and S. robustum and a commercial hybrid: RB867515, linking gene expression to phenotypes to identify genes for sugarcane improvement. Oligoarray experiments of leaves, immature and intermediate internodes, detected 12,621 sense and 995 antisense transcripts. Amino acid metabolism was particularly evident among pathways showing natural antisense transcripts expression. For all tissues sampled, expression analysis revealed 831, 674 and 648 differentially expressed genes in S. officinarum, S. robustum and S. spontaneum, respectively, using RB867515 as reference. Expression of sugar transporters might explain sucrose differences among genotypes, but an unexpected differential expression of histones were also identified between high and low Brix° genotypes. Lignin biosynthetic genes and bioenergetics-related genes were up-regulated in the high lignin genotype, suggesting that these genes are important for S. spontaneum to allocate carbon to lignin, while S. officinarum allocates it to sucrose storage. Co-expression network analysis identified 18 transcription factors possibly related to cell wall biosynthesis while in silico analysis detected cis-elements involved in cell wall biosynthesis in their promoters. Our results provide information to elucidate regulatory networks underlying traits of interest that will allow the improvement of sugarcane for biofuel and chemicals production.

  11. Understanding developmental and adaptive cues in pine through metabolite profiling and co-expression network analysis

    PubMed Central

    Cañas, Rafael A.; Canales, Javier; Muñoz-Hernández, Carmen; Granados, Jose M.; Ávila, Concepción; García-Martín, María L.; Cánovas, Francisco M.

    2015-01-01

    Conifers include long-lived evergreen trees of great economic and ecological importance, including pines and spruces. During their long lives conifers must respond to seasonal environmental changes, adapt to unpredictable environmental stresses, and co-ordinate their adaptive adjustments with internal developmental programmes. To gain insights into these responses, we examined metabolite and transcriptomic profiles of needles from naturally growing 25-year-old maritime pine (Pinus pinaster L. Aiton) trees over a year. The effect of environmental parameters such as temperature and rain on needle development were studied. Our results show that seasonal changes in the metabolite profiles were mainly affected by the needles’ age and acclimation for winter, but changes in transcript profiles were mainly dependent on climatic factors. The relative abundance of most transcripts correlated well with temperature, particularly for genes involved in photosynthesis or winter acclimation. Gene network analysis revealed relationships between 14 co-expressed gene modules and development and adaptation to environmental stimuli. Novel Myb transcription factors were identified as candidate regulators during needle development. Our systems-based analysis provides integrated data of the seasonal regulation of maritime pine growth, opening new perspectives for understanding the complex regulatory mechanisms underlying conifers’ adaptive responses. Taken together, our results suggest that the environment regulates the transcriptome for fine tuning of the metabolome during development. PMID:25873654

  12. Functional architecture and evolution of transcriptional elements that drive gene coexpression.

    PubMed

    Brown, Christopher D; Johnson, David S; Sidow, Arend

    2007-09-14

    Transcriptional coexpression of interacting gene products is required for complex molecular processes; however, the function and evolution of cis-regulatory elements that orchestrate coexpression remain largely unexplored. We mutagenized 19 regulatory elements that drive coexpression of Ciona muscle genes and obtained quantitative estimates of the cis-regulatory activity of the 77 motifs that comprise these elements. We found that individual motif activity ranges broadly within and among elements, and among different instantiations of the same motif type. The activity of orthologous motifs is strongly constrained, although motif arrangement, type, and activity vary greatly among the elements of different co-regulated genes. Thus, the syntactical rules governing this regulatory function are flexible but become highly constrained evolutionarily once they are established in a particular element.

  13. Gene differential coexpression analysis based on biweight correlation and maximum clique.

    PubMed

    Zheng, Chun-Hou; Yuan, Lin; Sha, Wen; Sun, Zhan-Li

    2014-01-01

    Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.

  14. WeGET: predicting new genes for molecular systems by weighted co-expression

    PubMed Central

    Szklarczyk, Radek; Megchelenbrink, Wout; Cizek, Pavel; Ledent, Marie; Velemans, Gonny; Szklarczyk, Damian; Huynen, Martijn A.

    2016-01-01

    We have developed the Weighted Gene Expression Tool and database (WeGET, http://weget.cmbi.umcn.nl) for the prediction of new genes of a molecular system by correlated gene expression. WeGET utilizes a compendium of 465 human and 560 murine gene expression datasets that have been collected from multiple tissues under a wide range of experimental conditions. It exploits this abundance of expression data by assigning a high weight to datasets in which the known genes of a molecular system are harmoniously up- and down-regulated. WeGET ranks new candidate genes by calculating their weighted co-expression with that system. A weighted rank is calculated for human genes and their mouse orthologs. Then, an integrated gene rank and p-value is computed using a rank-order statistic. We applied our method to predict novel genes that have a high degree of co-expression with Gene Ontology terms and pathways from KEGG and Reactome. For each query set we provide a list of predicted novel genes, computed weights for transcription datasets used and cell and tissue types that contributed to the final predictions. The performance for each query set is assessed by 10-fold cross-validation. Finally, users can use the WeGET to predict novel genes that co-express with a custom query set. PMID:26582928

  15. Normalized lmQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers

    PubMed Central

    Zhang, Jie; Huang, Kun

    2014-01-01

    In this paper, we present a new approach for mining weighted networks to identify densely connected modules such as quasi-cliques. Quasi-cliques are densely connected subnetworks in a network. Detecting quasi-cliques is an important topic in data mining, with applications such as social network study and biomedicine. Our approach has two major improvements upon previous work. The first is the use of local maximum edges to initialize the search in order to avoid excessive overlaps among the modules, thereby greatly reducing the computing time. The second is the inclusion of a weight normalization procedure to enable discovery of “subtle” modules with more balanced sizes. We carried out careful tests on multiple parameters and settings using two large cancer datasets. This approach allowed us to identify a large number of gene modules enriched in both biological functions and chromosomal bands in cancer data, suggesting potential roles of copy number variations (CNVs) involved in the cancer development. We then tested the genes in selected modules with enriched chromosomal bands using The Cancer Genome Atlas data, and the results strongly support our hypothesis that the coexpression in these modules are associated with CNVs. While gene coexpression network analyses have been widely adopted in disease studies, most of them focus on the functional relationships of coexpressed genes. The relationship between coexpression gene modules and CNVs are much less investigated despite the potential advantage that we can infer from such relationship without genotyping data. Our new approach thus provides a means to carry out deep mining of the gene coexpression network to obtain both functional and genetic information from the expression data. PMID:27486298

  16. Constitutive and inducible co-expression systems for non-viral osteoinductive gene therapy.

    PubMed

    Feichtinger, G A; Hacobian, A; Hofmann, A T; Wassermann, K; Zimmermann, A; van Griensven, M; Redl, H

    2014-02-19

    Tissue regenerative gene therapy requires expression strategies that deliver therapeutic effective amounts of transgenes. As physiological expression patterns are more complex than high-level expression of a singular therapeutic gene, we aimed at constitutive or inducible co-expression of 2 transgenes simultaneously. Co-expression of human bone morphogenetic protein 2 and 7 (BMP2/7) from constitutively expressing and doxycycline inducible plasmids was evaluated in vitro in C2C12 cells with osteocalcin reporter gene assays and standard assays for osteogenic differentiation. The constitutive systems were additionally tested in an in vivo pilot for ectopic bone formation after repeated naked DNA injection to murine muscle tissue. Inductor controlled differentiation was demonstrated in vitro for inducible co-expression. Both co-expression systems, inducible and constitutive, achieved significantly better osteogenic differentiation than single factor expression. The potency of the constitutive co-expression systems was dependent on relative expression cassette topology. In vivo, ectopic bone formation was demonstrated in 6/13 animals (46% bone formation efficacy) at days 14 and 28 in hind limb muscles as proven by in vivo µCT and histological evaluation. In vitro findings demonstrated that the devised single vector BMP2/7 co-expression strategy mediates superior osteoinduction, can be applied in an inductor controlled fashion and that its efficiency is dependent on expression cassette topology. In vivo results indicatethatco-expression of BMP2/7 applied by non-viral naked DNA gene transfer effectively mediates bone formation without the application of biomaterials, cells or recombinant growth factors, offering a promising alternative to current treatment strategies with potential for clinical translation in the future.

  17. Construction of a promoter collection for genes co-expression in filamentous fungus Trichoderma reesei.

    PubMed

    Wang, Wei; Meng, Fanju; Liu, Pei; Yang, Shengli; Wei, Dongzhi

    2014-11-01

    Trichoderma reesei is the preferred organism for producing industrial cellulases. However, cellulases derived from T. reesei have their highest activity at acidic pH. When the pH value increased above 7, the enzyme activities almost disappeared, thereby limiting the application of fungal cellulases under neutral or alkaline conditions. A lot of heterologous alkaline cellulases have been successfully expressed in T. reesei to improve its cellulolytic profile. To our knowledge, there are few reports describing the co-expression of two or more heterologous cellulases in T. reesei. We designed and constructed a promoter collection for gene expression and co-expression in T. reesei. Taking alkaline cellulase as a reporter gene, we assessed our promoters with strengths ranging from 4 to 106 % as compared to the pWEF31 expression vector (Lv D, Wang W, Wei D (2012) Construction of two vectors for gene expression in Trichoderma reesei. Plasmid 67(1):67-71). The promoter collection was used in a proof-of-principle approach to achieve the co-expression of an alkaline endoglucanase and an alkaline cellobiohydrolase. We observed higher activities of both cellulose degradation and biostoning by the co-expression of an endoglucanase and a cellobiohydrolase than the activities obtained by the expression of only endoglucanase or cellobiohydrolase. This study makes the process of engineering expression of multiple genes easier in T. reesei.

  18. Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

    PubMed Central

    Tawa, Gregory J.; AbdulHameed, Mohamed Diwan M.; Yu, Xueping; Kumar, Kamal; Ippolito, Danielle L.; Lewis, John A.; Stallings, Jonathan D.; Wallqvist, Anders

    2014-01-01

    Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects. PMID:25226513

  19. Use of transcriptomics and co-expression networks to analyze the interconnections between nitrogen assimilation and photorespiratory metabolism

    PubMed Central

    Pérez-Delgado, Carmen M.; Moyano, Tomás C.; García-Calderón, Margarita; Canales, Javier; Gutiérrez, Rodrigo A.; Márquez, Antonio J.; Betti, Marco

    2016-01-01

    Nitrogen is one of the most important nutrients for plants and, in natural soils, its availability is often a major limiting factor for plant growth. Here we examine the effect of different forms of nitrogen nutrition and of photorespiration on gene expression in the model legume Lotus japonicus with the aim of identifying regulatory candidate genes co-ordinating primary nitrogen assimilation and photorespiration. The transcriptomic changes produced by the use of different nitrogen sources in leaves of L. japonicus plants combined with the transcriptomic changes produced in the same tissue by different photorespiratory conditions were examined. The results obtained provide novel information on the possible role of plastidic glutamine synthetase in the response to different nitrogen sources and in the C/N balance of L. japonicus plants. The use of gene co-expression networks establishes a clear relationship between photorespiration and primary nitrogen assimilation and identifies possible transcription factors connected to the genes of both routes. PMID:27117340

  20. Coexpression of two closely linked avian genes for purine nucleotide synthesis from a bidirectional promoter.

    PubMed Central

    Gavalas, A; Dixon, J E; Brayton, K A; Zalkin, H

    1993-01-01

    Two avian genes encoding essential steps in the purine nucleotide biosynthetic pathway are transcribed divergently from a bidirectional promoter element. The bidirectional promoter, embedded in a CpG island, directs coexpression of GPAT and AIRC genes from distinct transcriptional start sites 229 bp apart. The bidirectional promoter can be divided in half, with each half retaining partial activity towards the cognate gene. GPAT and AIRC genes encode the enzymes that catalyze step 1 and steps 6 plus 7, respectively, in the de novo purine biosynthetic pathway. This is the first report of genes coding for structurally unrelated enzymes of the same pathway that are tightly linked and transcribed divergently from a bidirectional promoter. This arrangement has the potential to provide for regulated coexpression comparable to that in a prokaryotic operon. Images PMID:8336716

  1. Gene co-expression analyses: an overview from microarray collections in Arabidopsis thaliana.

    PubMed

    Di Salle, Pasquale; Incerti, Guido; Colantuono, Chiara; Chiusano, Maria Luisa

    2016-02-18

    Bioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the 'guilty by association' principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.

  2. Co-expression of FBN1 with mesenchyme-specific genes in mouse cell lines: implications for phenotypic variability in Marfan syndrome

    PubMed Central

    Summers, Kim M; Raza, Sobia; van Nimwegen, Erik; Freeman, Thomas C; Hume, David A

    2010-01-01

    Mutations in the human FBN1 gene cause Marfan syndrome, a complex disease affecting connective tissues but with a highly variable phenotype. To identify genes that might participate in epistatic interactions with FBN1, and could therefore explain the observed phenotypic variability, we have looked for genes that are co-expressed with Fbn1 in the mouse. Microarray expression data derived from a range of primary mouse cells and cell lines were analysed using the network analysis tool BioLayout Express3D. A cluster of 205 genes, including Fbn1, were selectively expressed by mouse cell lines of different mesenchymal lineages and by mouse primary mesenchymal cells (preadipocytes, myoblasts, fibroblasts, osteoblasts). Promoter analysis of this gene set identified several candidate transcriptional regulators. Genes within this co-expressed cluster are candidate genetic modifiers for Marfan syndrome and for other connective tissue diseases. PMID:20551991

  3. Increased co-expression of genes harboring the damaging de novo mutations in Chinese schizophrenic patients during prenatal development.

    PubMed

    Wang, Qiang; Li, Miaoxin; Yang, Zhenxing; Hu, Xun; Wu, Hei-Man; Ni, Peiyan; Ren, Hongyan; Deng, Wei; Li, Mingli; Ma, Xiaohong; Guo, Wanjun; Zhao, Liansheng; Wang, Yingcheng; Xiang, Bo; Lei, Wei; Sham, Pak C; Li, Tao

    2015-12-15

    Schizophrenia is a heritable, heterogeneous common psychiatric disorder. In this study, we evaluated the hypothesis that de novo variants (DNVs) contribute to the pathogenesis of schizophrenia. We performed exome sequencing in Chinese patients (N = 45) with schizophrenia and their unaffected parents (N = 90). Forty genes were found to contain DNVs. These genes had enriched transcriptional co-expression profile in prenatal frontal cortex (Bonferroni corrected p < 9.1 × 10(-3)), and in prenatal temporal and parietal regions (Bonferroni corrected p < 0.03). Also, four prenatal anatomical subregions (VCF, MFC, OFC and ITC) have shown significant enrichment of connectedness in co-expression networks. Moreover, four genes (LRP1, MACF1, DICER1 and ABCA2) harboring the damaging de novo mutations are strongly prioritized as susceptibility genes by multiple evidences. Our findings in Chinese schizophrenic patients indicate the pathogenic role of DNVs, supporting the hypothesis that schizophrenia is a neurodevelopmental disease.

  4. Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co-expression analysis.

    PubMed

    Bing, Zhi-Tong; Yang, Guang-Hui; Xiong, Jie; Guo, Ling; Yang, Lei

    2016-12-01

    Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival-related individual genes (or miRNAs) and miRNA -mRNA pairs in GBM using a multi-step approach. First, the weighted gene co-expression network analysis and survival analysis are applied to identify survival-related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival-related miRNA -mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient's survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin-1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA -mRNA regulatory networks that comprised 38 survival-related miRNA -mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.

  5. Transcriptional coexpression network reveals the involvement of varying stem cell features with different dysregulations in different gastric cancer subtypes.

    PubMed

    Kalamohan, Kalaivani; Periasamy, Jayaprakash; Bhaskar Rao, Divya; Barnabas, Georgina D; Ponnaiyan, Srigayatri; Ganesan, Kumaresan

    2014-10-01

    Despite the advancements in the cancer therapeutics, gastric cancer ranks as the second most common cancers with high global mortality rate. Integrative functional genomic investigation is a powerful approach to understand the major dysregulations and to identify the potential targets toward the development of targeted therapeutics for various cancers. Intestinal and diffuse type gastric tumors remain the major subtypes and the molecular determinants and drivers of these distinct subtypes remain unidentified. In this investigation, by exploring the network of gene coexpression association in gastric tumors, mRNA expressions of 20,318 genes across 200 gastric tumors were categorized into 21 modules. The genes and the hub genes of the modules show gastric cancer subtype specific expression. The expression patterns of the modules were correlated with intestinal and diffuse subtypes as well as with the differentiation status of gastric tumors. Among these, G1 module has been identified as a major driving force of diffuse type gastric tumors with the features of (i) enriched mesenchymal, mesenchymal stem cell like, and mesenchymal derived multiple lineages, (ii) elevated OCT1 mediated transcription, (iii) involvement of Notch activation, and (iv) reduced polycomb mediated epigenetic repression. G13 module has been identified as key factor in intestinal type gastric tumors and found to have the characteristic features of (i) involvement of embryonic stem cell like properties, (ii) Wnt, MYC and E2F mediated transcription programs, and (iii) involvement of polycomb mediated repression. Thus the differential transcription programs, differential epigenetic regulation and varying stem cell features involved in two major subtypes of gastric cancer were delineated by exploring the gene coexpression network. The identified subtype specific dysregulations could be optimally employed in developing subtype specific therapeutic targeting strategies for gastric cancer.

  6. Phenotype-Dependent Coexpression Gene Clusters: Application to Normal and Premature Ageing.

    PubMed

    Wang, Kun; Das, Avinash; Xiong, Zheng-Mei; Cao, Kan; Hannenhalli, Sridhar

    2015-01-01

    Hutchinson Gilford progeria syndrome (HGPS) is a rare genetic disease with symptoms of aging at a very early age. Its molecular basis is not entirely clear, although profound gene expression changes have been reported, and there are some known and other presumed overlaps with normal aging process. Identification of genes with agingor HGPS-associated expression changes is thus an important problem. However, standard regression approaches are currently unsuitable for this task due to limited sample sizes, thus motivating development of alternative approaches. Here, we report a novel iterative multiple regression approach that leverages co-expressed gene clusters to identify gene clusters whose expression co-varies with age and/or HGPS. We have applied our approach to novel RNA-seq profiles in fibroblast cell cultures at three different cellular ages, both from HGPS patients and normal samples. After establishing the robustness of our approach, we perform a comparative investigation of biological processes underlying normal aging and HGPS. Our results recapitulate previously known processes underlying aging as well as suggest numerous unique processes underlying aging and HGPS. The approach could also be useful in detecting phenotype-dependent co-expression gene clusters in other contexts with limited sample sizes.

  7. A Systems Approach Implicates a Brain Mitochondrial Oxidative Homeostasis Co-expression Network in Genetic Vulnerability to Alcohol Withdrawal

    PubMed Central

    Walter, Nicole A. R.; Denmark, DeAunne L.; Kozell, Laura B.; Buck, Kari J.

    2017-01-01

    Genetic factors significantly affect vulnerability to alcohol dependence (alcoholism). We previously identified quantitative trait loci on distal mouse chromosome 1 with large effects on predisposition to alcohol physiological dependence and associated withdrawal following both chronic and acute alcohol exposure in mice (Alcdp1 and Alcw1, respectively). We fine-mapped these loci to a 1.1–1.7 Mb interval syntenic with human 1q23.2-23.3. Alcw1/Alcdp1 interval genes show remarkable genetic variation among mice derived from the C57BL/6J and DBA/2J strains, the two most widely studied genetic animal models for alcohol-related traits. Here, we report the creation of a novel recombinant Alcw1/Alcdp1 congenic model (R2) in which the Alcw1/Alcdp1 interval from a donor C57BL/6J strain is introgressed onto a uniform, inbred DBA/2J genetic background. As expected, R2 mice demonstrate significantly less severe alcohol withdrawal compared to wild-type littermates. Additionally, comparing R2 and background strain animals, as well as reciprocal congenic (R8) and appropriate background strain animals, we assessed Alcw1/Alcdp1 dependent brain gene expression using microarray and quantitative PCR analyses. To our knowledge this includes the first Weighted Gene Co-expression Network Analysis using reciprocal congenic models. Importantly, this allows detection of co-expression patterns limited to one or common to both genetic backgrounds with high or low predisposition to alcohol withdrawal severity. The gene expression patterns (modules) in common contain genes related to oxidative phosphorylation, building upon human and animal model studies that implicate involvement of oxidative phosphorylation in alcohol use disorders (AUDs). Finally, we demonstrate that administration of N-acetylcysteine, an FDA-approved antioxidant, significantly reduces symptoms of alcohol withdrawal (convulsions) in mice, thus validating a phenotypic role for this network. Taken together, these studies

  8. Identifying the optimal gene and gene set in hepatocellular carcinoma based on differential expression and differential co-expression algorithm.

    PubMed

    Dong, Li-Yang; Zhou, Wei-Zhong; Ni, Jun-Wei; Xiang, Wei; Hu, Wen-Hao; Yu, Chang; Li, Hai-Yan

    2017-02-01

    The objective of this study was to identify the optimal gene and gene set for hepatocellular carcinoma (HCC) utilizing differential expression and differential co-expression (DEDC) algorithm. The DEDC algorithm consisted of four parts: calculating differential expression (DE) by absolute t-value in t-statistics; computing differential co-expression (DC) based on Z-test; determining optimal thresholds on the basis of Chi-squared (χ2) maximization and the corresponding gene was the optimal gene; and evaluating functional relevance of genes categorized into different partitions to determine the optimal gene set with highest mean minimum functional information (FI) gain (Δ*G). The optimal thresholds divided genes into four partitions, high DE and high DC (HDE-HDC), high DE and low DC (HDE-LDC), low DE and high DC (LDE‑HDC), and low DE and low DC (LDE-LDC). In addition, the optimal gene was validated by conducting reverse transcription-polymerase chain reaction (RT-PCR) assay. The optimal threshold for DC and DE were 1.032 and 1.911, respectively. Using the optimal gene, the genes were divided into four partitions including: HDE-HDC (2,053 genes), HED-LDC (2,822 genes), LDE-HDC (2,622 genes), and LDE-LDC (6,169 genes). The optimal gene was microtubule‑associated protein RP/EB family member 1 (MAPRE1), and RT-PCR assay validated the significant difference between the HCC and normal state. The optimal gene set was nucleoside metabolic process (GO\\GO:0009116) with Δ*G = 18.681 and 24 HDE-HDC partitions in total. In conclusion, we successfully investigated the optimal gene, MAPRE1, and gene set, nucleoside metabolic process, which may be potential biomarkers for targeted therapy and provide significant insight for revealing the pathological mechanism underlying HCC.

  9. MGMT enrichment and second gene co-expression in hematopoietic progenitor cells using separate or dual-gene lentiviral vectors.

    PubMed

    Roth, Justin C; Alberti, Michael O; Ismail, Mourad; Lingas, Karen T; Reese, Jane S; Gerson, Stanton L

    2015-01-22

    The DNA repair gene O(6)-methylguanine-DNA methyltransferase (MGMT) allows efficient in vivo enrichment of transduced hematopoietic stem cells (HSC). Thus, linking this selection strategy to therapeutic gene expression offers the potential to reconstitute diseased hematopoietic tissue with gene-corrected cells. However, different dual-gene expression vector strategies are limited by poor expression of one or both transgenes. To evaluate different co-expression strategies in the context of MGMT-mediated HSC enrichment, we compared selection and expression efficacies in cells cotransduced with separate single-gene MGMT and GFP lentivectors to those obtained with dual-gene vectors employing either encephalomyocarditis virus (EMCV) internal ribosome entry site (IRES) or foot and mouth disease virus (FMDV) 2A elements for co-expression strategies. Each strategy was evaluated in vitro and in vivo using equivalent multiplicities of infection (MOI) to transduce 5-fluorouracil (5-FU) or Lin(-)Sca-1(+)c-kit(+) (LSK)-enriched murine bone marrow cells (BMCs). The highest dual-gene expression (MGMT(+)GFP(+)) percentages were obtained with the FMDV-2A dual-gene vector, but half of the resulting gene products existed as fusion proteins. Following selection, dual-gene expression percentages in single-gene vector cotransduced and dual-gene vector transduced populations were similar. Equivalent MGMT expression levels were obtained with each strategy, but GFP expression levels derived from the IRES dual-gene vector were significantly lower. In mice, vector-insertion averages were similar among cells enriched after dual-gene vectors and those cotransduced with single-gene vectors. These data demonstrate the limitations and advantages of each strategy in the context of MGMT-mediated selection, and may provide insights into vector design with respect to a particular therapeutic gene or hematologic defect.

  10. MIrExpress: A Database for Gene Coexpression Correlation in Immune Cells Based on Mutual Information and Pearson Correlation.

    PubMed

    Wang, Luman; Mo, Qiaochu; Wang, Jianxin

    2015-01-01

    Most current gene coexpression databases support the analysis for linear correlation of gene pairs, but not nonlinear correlation of them, which hinders precisely evaluating the gene-gene coexpression strengths. Here, we report a new database, MIrExpress, which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. For a given gene pair or probe set pair input by web users, both mutual information (MI) and Pearson correlation coefficient (r) are calculated, and several corresponding values are reported to reflect their coexpression correlation nature, including MI and r values, their respective rank orderings, their rank comparison, and their hybrid correlation value. Furthermore, for a given gene, the top 10 most relevant genes to it are displayed with the MI, r, or their hybrid perspective, respectively. Currently, the database totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches.

  11. Gene co-expression modules as clinically relevant hallmarks of breast cancer diversity.

    PubMed

    Wolf, Denise M; Lenburg, Marc E; Yau, Christina; Boudreau, Aaron; van 't Veer, Laura J

    2014-01-01

    Co-expression modules are groups of genes with highly correlated expression patterns. In cancer, differences in module activity potentially represent the heterogeneity of phenotypes important in carcinogenesis, progression, or treatment response. To find gene expression modules active in breast cancer subpopulations, we assembled 72 breast cancer-related gene expression datasets containing ∼5,700 samples altogether. Per dataset, we identified genes with bimodal expression and used mixture-model clustering to ultimately define 11 modules of genes that are consistently co-regulated across multiple datasets. Functionally, these modules reflected estrogen signaling, development/differentiation, immune signaling, histone modification, ERBB2 signaling, the extracellular matrix (ECM) and stroma, and cell proliferation. The Tcell/Bcell immune modules appeared tumor-extrinsic, with coherent expression in tumors but not cell lines; whereas most other modules, interferon and ECM included, appeared intrinsic. Only four of the eleven modules were represented in the PAM50 intrinsic subtype classifier and other well-established prognostic signatures; although the immune modules were highly correlated to previously published immune signatures. As expected, the proliferation module was highly associated with decreased recurrence-free survival (RFS). Interestingly, the immune modules appeared associated with RFS even after adjustment for receptor subtype and proliferation; and in a multivariate analysis, the combination of Tcell/Bcell immune module down-regulation and proliferation module upregulation strongly associated with decreased RFS. Immune modules are unusual in that their upregulation is associated with a good prognosis without chemotherapy and a good response to chemotherapy, suggesting the paradox of high immune patients who respond to chemotherapy but would do well without it. Other findings concern the ECM/stromal modules, which despite common themes were associated

  12. Module Based Differential Coexpression Analysis Method for Type 2 Diabetes

    PubMed Central

    Yuan, Lin; Zheng, Chun-Hou; Xia, Jun-Feng; Huang, De-Shuang

    2015-01-01

    More and more studies have shown that many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional biological pathway or network and are highly correlated. Differential coexpression analysis, as a more comprehensive technique to the differential expression analysis, was raised to research gene regulatory networks and biological pathways of phenotypic changes through measuring gene correlation changes between disease and normal conditions. In this paper, we propose a gene differential coexpression analysis algorithm in the level of gene sets and apply the algorithm to a publicly available type 2 diabetes (T2D) expression dataset. Firstly, we calculate coexpression biweight midcorrelation coefficients between all gene pairs. Then, we select informative correlation pairs using the “differential coexpression threshold” strategy. Finally, we identify the differential coexpression gene modules using maximum clique concept and k-clique algorithm. We apply the proposed differential coexpression analysis method on simulated data and T2D data. Two differential coexpression gene modules about T2D were detected, which should be useful for exploring the biological function of the related genes. PMID:26339648

  13. Characterization of Tusc5, an adipocyte gene co-expressed in peripheral neurons.

    PubMed

    Oort, Pieter J; Warden, Craig H; Baumann, Thomas K; Knotts, Trina A; Adams, Sean H

    2007-09-30

    Tumor suppressor candidate 5 (Tusc5, also termed brain endothelial cell derived gene-1 or BEC-1), a CD225 domain-containing, cold-repressed gene identified during brown adipose tissue (BAT) transcriptome analyses was found to be robustly-expressed in mouse white adipose tissue (WAT) and BAT, with similarly high expression in human adipocytes. Tusc5 mRNA was markedly increased from trace levels in pre-adipocytes to significant levels in developing 3T3-L1 adipocytes, coincident with several mature adipocyte markers (phosphoenolpyruvate carboxykinase 1, GLUT4, adipsin, leptin). The Tusc5 transcript levels were increased by the peroxisome proliferator activated receptor-gamma (PPARgamma) agonist GW1929 (1microg/mL, 18h) by >10-fold (pre-adipocytes) to approximately 1.5-fold (mature adipocytes) versus controls (p<0.0001). Taken together, these results suggest an important role for Tusc5 in maturing adipocytes. Intriguingly, we discovered robust co-expression of the gene in peripheral nerves (primary somatosensory neurons). In light of the marked repression of the gene observed after cold exposure, these findings may point to participation of Tusc5 in shared adipose-nervous system functions linking environmental cues, CNS signals, and WAT-BAT physiology. Characterization of such links is important for clarifying the molecular basis for adipocyte proliferation and could have implications for understanding the biology of metabolic disease-related neuropathies.

  14. Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

    PubMed

    Oliver, Karen L; Lukic, Vesna; Thorne, Natalie P; Berkovic, Samuel F; Scheffer, Ingrid E; Bahlo, Melanie

    2014-01-01

    We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.

  15. Gene Co-Expression Modules as Clinically Relevant Hallmarks of Breast Cancer Diversity

    PubMed Central

    Yau, Christina; Boudreau, Aaron; van ‘t Veer, Laura J.

    2014-01-01

    Co-expression modules are groups of genes with highly correlated expression patterns. In cancer, differences in module activity potentially represent the heterogeneity of phenotypes important in carcinogenesis, progression, or treatment response. To find gene expression modules active in breast cancer subpopulations, we assembled 72 breast cancer-related gene expression datasets containing ∼5,700 samples altogether. Per dataset, we identified genes with bimodal expression and used mixture-model clustering to ultimately define 11 modules of genes that are consistently co-regulated across multiple datasets. Functionally, these modules reflected estrogen signaling, development/differentiation, immune signaling, histone modification, ERBB2 signaling, the extracellular matrix (ECM) and stroma, and cell proliferation. The Tcell/Bcell immune modules appeared tumor-extrinsic, with coherent expression in tumors but not cell lines; whereas most other modules, interferon and ECM included, appeared intrinsic. Only four of the eleven modules were represented in the PAM50 intrinsic subtype classifier and other well-established prognostic signatures; although the immune modules were highly correlated to previously published immune signatures. As expected, the proliferation module was highly associated with decreased recurrence-free survival (RFS). Interestingly, the immune modules appeared associated with RFS even after adjustment for receptor subtype and proliferation; and in a multivariate analysis, the combination of Tcell/Bcell immune module down-regulation and proliferation module upregulation strongly associated with decreased RFS. Immune modules are unusual in that their upregulation is associated with a good prognosis without chemotherapy and a good response to chemotherapy, suggesting the paradox of high immune patients who respond to chemotherapy but would do well without it. Other findings concern the ECM/stromal modules, which despite common themes were associated

  16. Mining Temporal Protein Complex Based on the Dynamic PIN Weighted with Connected Affinity and Gene Co-Expression.

    PubMed

    Shen, Xianjun; Yi, Li; Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Yang, Jincai

    2016-01-01

    The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.

  17. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    PubMed

    Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

    2014-01-01

    Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed

  18. Coexpression of bile salt hydrolase gene and catalase gene remarkably improves oxidative stress and bile salt resistance in Lactobacillus casei.

    PubMed

    Wang, Guohong; Yin, Sheng; An, Haoran; Chen, Shangwu; Hao, Yanling

    2011-08-01

    Lactic acid bacteria (LAB) encounter various types of stress during industrial processes and gastrointestinal transit. Catalase (CAT) and bile salt hydrolase (BSH) can protect bacteria from oxidative stress or damage caused by bile salts by decomposing hydrogen peroxide (H(2)O(2)) or deconjugating the bile salts, respectively. Lactobacillus casei is a valuable probiotic strain and is often deficient in both CAT and BSH. In order to improve the resistance of L. casei to both oxidative and bile salts stress, the catalase gene katA from L. sakei and the bile salt hydrolase gene bsh1 from L. plantarum were coexpressed in L. casei HX01. The enzyme activities of CAT and BSH were 2.41 μmol H(2)O(2)/min/10(8) colony-forming units (CFU) and 2.11 μmol glycine/min/ml in the recombinant L. casei CB, respectively. After incubation with 8 mM H(2)O(2), survival ratio of L. casei CB was 40-fold higher than that of L. casei CK. Treatment of L. casei CB with various concentrations of sodium glycodeoxycholate (GDCA) showed that ~10(5) CFU/ml cells survived after incubation with 0.5% GDCA, whereas almost all the L. casei CK cells were killed when treaded with 0.4% GDCA. These results indicate that the coexpression of CAT and BSH confers high-level resistance to both oxidative and bile salts stress conditions in L. casei HX01.

  19. Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas.

    PubMed

    Eising, Else; Huisman, Sjoerd M H; Mahfouz, Ahmed; Vijfhuizen, Lisanne S; Anttila, Verneri; Winsvold, Bendik S; Kurth, Tobias; Ikram, M Arfan; Freilinger, Tobias; Kaprio, Jaakko; Boomsma, Dorret I; van Duijn, Cornelia M; Järvelin, Marjo-Riitta R; Zwart, John-Anker; Quaye, Lydia; Strachan, David P; Kubisch, Christian; Dichgans, Martin; Davey Smith, George; Stefansson, Kari; Palotie, Aarno; Chasman, Daniel I; Ferrari, Michel D; Terwindt, Gisela M; de Vries, Boukje; Nyholt, Dale R; Lelieveldt, Boudewijn P F; van den Maagdenberg, Arn M J M; Reinders, Marcel J T

    2016-04-01

    Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.

  20. lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia

    PubMed Central

    Pan, Jia-Qi; Zhang, Yan-Qing; Wang, Jing-Hua; Xu, Ping; Wang, Wei

    2017-01-01

    Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of lncRNAs as prognostic biomarkers in AML and their detailed mechanisms of action in this disease have not yet been well characterized. For this purpose, in the present study, the expression levels of lncRNAs and mRNAs were calculated using the RNA-seq V2 data for AML, following which a lncRNA-lncRNA co-expression network (LLCN) was constructed. This revealed a total of 8 AML prognosis-related lncRNA modules were identified, which displayed a significant correlation with patient survival (p≤0.05). Subsequently, a prognosis-related lncRNA module pathway network was constructed to interpret the functional mechanism of the prognostic modules in AML. The results indicated that these prognostic modules were involved in the AML pathway, chemokine signaling pathway and WNT signaling pathway, all of which play important roles in AML. Furthermore, the investigation of lncRNAs in these prognostic modules suggested that an lncRNA (ZNF571-AS1) may be involved in AML via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway by regulating KIT and STAT5. The results of the present study not only provide potential lncRNA modules as prognostic biomarkers, but also provide further insight into the molecular mechanisms of action of lncRNAs. PMID:28204819

  1. Ubiquinone-10 production using Agrobacterium tumefaciens dps gene in Escherichia coli by coexpression system.

    PubMed

    Zhang, Dawei; Shrestha, Binaya; Li, Zhaopeng; Tan, Tianwei

    2007-01-01

    Ubiquinone (Coenzyme Q; abbreviation, UQ) acts as a mobile component of the respiratory chain by playing an essential role in the electron transport system, and has been widely used in pharmaceuticals. The biosynthesis of UQ involves 10 sequential reactions brought about by various enzymes. In this study we have cloned, expressed the decaprenyl diphosphate synthase, designated dps gene, from Agrobacterium tumefaciens, and succeeded in detecting UQ-10 in addition to innate UQ-8 in Escherichia coli. Furthermore, the production of UQ-10 was higher than UQ-8. To establish an efficient expression system for UQ- 10 production, we used genes, including ubiC, ubiA, and ubiG involved in UQ biosynthesis in E. coli, to construct a better co-expression system. The expression coupled by dps and ubiCA was effective for increasing UQ-10 production by five times than that by expressing single dps gene in the shake flask culture. To study for a large-scale production of UQ-10 in E. coli, fed-batch fermentations were implemented to achieve a high cell density culture. A cell concentration of 85.40 g/L and 94.58 g/L dry cell weight (DCW), and UQ-10 content of 50.29 mg/L and 45.86 mg/L was obtained after 32.5 h and 27.5 h of cultivation, subsequent to isopropyl-beta-D-thiogalactopyranoside and lactose induction, respectively. In addition, plasmid stability was maintained at high level throughout the fermentation.

  2. FAD2-DGAT2 Genes Coexpressed in Endophytic Aspergillus fumigatus Derived from Tung Oilseeds

    PubMed Central

    Chen, Yi-Cun; Wang, Yang-Dong; Cui, Qin-Qin; Zhan, Zhi-Yong

    2012-01-01

    Recent efforts to genetically engineer plants that contain fatty acid desaturases to produce valuable fatty acids have made only modest progress. Diacylglycerol acyltransferase 2 (DGAT2), which catalyzes the final step in triacylglycerol (TAG) assembly, might potentially regulate the biosynthesis of desired fatty acids in TAGs. To study the effects of tung tree (Vernicia fordii) vfDGAT2 in channeling the desired fatty acids into TAG, vfDGAT2 combined with the tung tree fatty acid desaturase-2 (vfFAD2) gene was co-introduced into Aspergillus fumigatus, an endophytic fungus isolated from healthy tung oilseed. Two transformants coexpressing vfFAD2 and vfDGAT2 showed a more than 6-fold increase in linoleic acid production compared to the original A. fumigatus strain, while a nearly 2-fold increase was found in the transformant expressing only vfFAD2. Our data suggest that vfDGAT2 plays a pivotal role in promoting linoleic acid accumulation in TAGs. This holds great promise for further genetic engineering aimed at producing valuable fatty acids. PMID:22919314

  3. Preservation Analysis of Macrophage Gene Coexpression Between Human and Mouse Identifies PARK2 as a Genetically Controlled Master Regulator of Oxidative Phosphorylation in Humans

    PubMed Central

    Codoni, Veronica; Blum, Yuna; Civelek, Mete; Proust, Carole; Franzén, Oscar; Björkegren, Johan L. M.; Le Goff, Wilfried; Cambien, Francois; Lusis, Aldons J.; Trégouët, David-Alexandre

    2016-01-01

    Macrophages are key players involved in numerous pathophysiological pathways and an in-depth characterization of their gene regulatory networks can help in better understanding how their dysfunction may impact on human diseases. We here conducted a cross-species network analysis of macrophage gene expression data between human and mouse to identify conserved networks across both species, and assessed whether such networks could reveal new disease-associated regulatory mechanisms. From a sample of 684 individuals processed for genome-wide macrophage gene expression profiling, we identified 27 groups of coexpressed genes (modules). Six modules were found preserved (P < 10−4) in macrophages from 86 mice of the Hybrid Mouse Diversity Panel. One of these modules was significantly [false discovery rate (FDR) = 8.9 × 10−11] enriched for genes belonging to the oxidative phosphorylation (OXPHOS) pathway. This pathway was also found significantly (FDR < 10−4) enriched in susceptibility genes for Alzheimer, Parkinson, and Huntington diseases. We further conducted an expression quantitative trait loci analysis to identify SNP that could regulate macrophage OXPHOS gene expression in humans. This analysis identified the PARK2 rs192804963 as a trans-acting variant influencing (minimal P-value = 4.3 × 10−8) the expression of most OXPHOS genes in humans. Further experimental work demonstrated that PARK2 knockdown expression was associated with increased OXPHOS gene expression in THP1 human macrophages. This work provided strong new evidence that PARK2 participates to the regulatory networks associated with oxidative phosphorylation and suggested that PARK2 genetic variations could act as a trans regulator of OXPHOS gene macrophage expression in humans. PMID:27558669

  4. Dynamic co-expression network analysis of lncRNAs and mRNAs associated with venous congestion

    PubMed Central

    Li, Jinshun; Xu, Yuqin; Xu, Jia; Wang, Jinhua; Wu, Liying

    2016-01-01

    Venous congestion and volume overload are important in cardiorenal syndromes, in which multiple regulated factors are involved, including long non-coding RNAs (lncRNAs). To investigate the underlying role of lncRNAs in regulating the development of venous congestion, an Affymetrix microarray associated with peripheral venous congestion was annotated, then a bipartite dynamic lncRNA-mRNA co-expression network was constructed in which nodes indicated lncRNAs or mRNAs. The nodes were connected when the lncRNAs or mRNAs were dynamically co-expressed. Following functional analysis of this network, several dynamic alternative pathways were identified, including the calcium signaling pathway during venous congestion development. Additionally, certain lncRNAs (LINC00523, LINC01210 and RP11-435O5.5) were identified that may potentially dynamically regulate certain proteins, including plasma membrane calcium ATPase (PMCA) and G protein-coupled receptor (GPCR), in the calcium signaling pathway. Particularly, the dynamically regulated switch of LINC00523 from co-expression with PMCA to GPCR may be involved in damage to steady state intracellular calcium. In brief, the current study demonstrated a potential novel mechanism of lncRNA function during venous congestion. PMID:27431002

  5. Inferring gene correlation networks from transcription factor binding sites.

    PubMed

    Mahdevar, Ghasem; Nowzari-Dalini, Abbas; Sadeghi, Mehdi

    2013-01-01

    Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting co-expressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.

  6. Coexpression of chitinase and the cry11Aa1 toxin genes in Bacillus thuringiensis serovar israelensis.

    PubMed

    Sirichotpakorn, N; Rongnoparut, P; Choosang, K; Panbangred, W

    2001-10-01

    At the spore stage, a cloned chitinase gene was coexpressed with the regulatory gene p19 and the toxin gene cry11Aa1 in the hosts Bacillus thuringiensis serovar israelensis strains 4Q2-72 and c4Q2-72. The chitinase gene was derived from a high-chitinase producer, Bacillus licheniformis TP-1. Two transcriptional fusion plasmids between the p19 or p19-cry11Aa1 genes and the promoterless chitinase gene were constructed. In transcription order, the p16-19CHI construct contained the p19 gene together with the chitinase gene only while the p16-1968CHI construct contained p19 together with the toxin gene cry11Aa1 and the chitinase gene. The inserted sequences were regulated by a spore-specific promoter located upstream of p19. The recombinant chitinase of all transformed B. thuringiensis serovar israelensis strains was initially synthesized at low level at about 9 h of growth when a portion of the cells started to sporulate. It increased thereafter and reached maximum levels of 5.5, 4.9, and 4.7 mU/ml at 48 h, for strain 4Q2-72 transformed with p16-19CHI and p16-1968CHI and strain c4Q2-72 transformed with p16-19CHI, respectively. This activity was approximately 2 times higher than the maximum activity (2.7 mU/ml) of the parental strain, B. licheniformis TP-1. Although crude chitinase alone from B. thuringiensis serovar israelensis c4Q2-72 (p16-19CHI) at 4.5 mU/ml caused 40% mortality in second instar Aedes aegypti larvae, transformants containing the chitinase alone or in combination with cry11Aa1 resulted in lower toxicity to A. aegypti larvae than the untransformed 4Q2-72 host. For example the LC(50) for the transformed 4Q2-72 harboring the chitinase gene only (p16-19CHI) was 5.6 x 10(4) +/- 0.7 x 10(4) cells, 40 times higher than that of the untransformed host at 1.4 x 10(3) +/- 0.19 x 10(3). The lower toxicity correlated with poor sporulation in the transformants (i.e., 35 times lower than that in the untransformed host). However, the transformed 4Q2-72 strain

  7. Enhanced production of ε-caprolactone by coexpression of bacterial hemoglobin gene in recombinant Escherichia coli expressing cyclohexanone monooxygenase gene.

    PubMed

    Lee, Won-Heong; Park, Eun-Hee; Kim, Myoung-Dong

    2014-12-28

    Baeyer-Villiger (BV) oxidation of cyclohexanone to epsilon-caprolactone in a microbial system expressing cyclohexanone monooxygenase (CHMO) can be influenced by not only the efficient regeneration of NADPH but also a sufficient supply of oxygen. In this study, the bacterial hemoglobin gene from Vitreoscilla stercoraria (vhb) was introduced into the recombinant Escherichia coli expressing CHMO to investigate the effects of an oxygen-carrying protein on microbial BV oxidation of cyclohexanone. Coexpression of Vhb allowed the recombinant E. coli strain to produce a maximum epsilon-caprolactone concentration of 15.7 g/l in a fed-batch BV oxidation of cyclohexanone, which corresponded to a 43% improvement compared with the control strain expressing CHMO only under the same conditions.

  8. Coexpression Network Analysis of Benign and Malignant Phenotypes of SIV-Infected Sooty Mangabey and Rhesus Macaque.

    PubMed

    Yang, Zhao-Wan; Jiang, Yan-Hua; Ma, Chuang; Silvestri, Guido; Bosinger, Steven E; Li, Bai-Lian; Jong, Ambrose; Zhou, Yan-Hong; Huang, Sheng-He

    2016-01-01

    To explore the differences between the extreme SIV infection phenotypes, nonprogression (BEN: benign) to AIDS in sooty mangabeys (SMs) and progression to AIDS (MAL: malignant) in rhesus macaques (RMs), we performed an integrated dual positive-negative connectivity (DPNC) analysis of gene coexpression networks (GCN) based on publicly available big data sets in the GEO database of NCBI. The microarray-based gene expression data sets were generated, respectively, from the peripheral blood of SMs and RMs at several time points of SIV infection. Significant differences of GCN changes in DPNC values were observed in SIV-infected SMs and RMs. There are three groups of enriched genes or pathways (EGPs) that are associated with three SIV infection phenotypes (BEN+, MAL+ and mixed BEN+/MAL+). The MAL+ phenotype in SIV-infected RMs is specifically associated with eight EGPs, including the protein ubiquitin proteasome system, p53, granzyme A, gramzyme B, polo-like kinase, Glucocorticoid receptor, oxidative phosyphorylation and mitochondrial signaling. Mitochondrial (endosymbiotic) dysfunction is solely present in RMs. Specific BEN+ pattern changes in four EGPs are identified in SIV-infected SMs, including the pathways contributing to interferon signaling, BRCA1/DNA damage response, PKR/INF induction and LGALS8. There are three enriched pathways (PRR-activated IRF signaling, RIG1-like receptor and PRR pathway) contributing to the mixed (BEN+/MAL+) phenotypes of SIV infections in RMs and SMs, suggesting that these pathways play a dual role in the host defense against viral infections. Further analysis of Hub genes in these GCNs revealed that the genes LGALS8 and IL-17RA, which positively regulate the barrier function of the gut mucosa and the immune homeostasis with the gut microbiota (exosymbiosis), were significantly differentially expressed in RMs and SMs. Our data suggest that there exists an exo- (dysbiosis of the gut microbiota) and endo- (mitochondrial dysfunction

  9. Systems Toxicology of Chemically Induced Liver and Kidney Injuries: Histopathology-Associated Gene Co-Expression Modules

    DTIC Science & Technology

    2016-01-04

    well as determine whether the injury module activation was specific to the tissue of origin (liver and kidney). The generated modules provide a link...modules to different types of cellular and tissue damage caused by different classes of toxicants. In our previous work, wewere able to conceptually... connect mo- lecular toxicity pathways to co-expressed gene modules and link these pathways to specific injuries in the liver with the objective of

  10. Enhanced production of shikimic acid using a multi-gene co-expression system in Escherichia coli.

    PubMed

    Liu, Xiang-Lei; Lin, Jun; Hu, Hai-Feng; Zhou, Bin; Zhu, Bao-Quan

    2016-04-01

    Shikimic acid (SA) is the key synthetic material for the chemical synthesis of Oseltamivir, which is prescribed as the front-line treatment for serious cases of influenza. Multi-gene expression vector can be used for expressing the plurality of the genes in one plasmid, so it is widely applied to increase the yield of metabolites. In the present study, on the basis of a shikimate kinase genetic defect strain Escherichia coli BL21 (ΔaroL/aroK, DE3), the key enzyme genes aroG, aroB, tktA and aroE of SA pathway were co-expressed and compared systematically by constructing a series of multi-gene expression vectors. The results showed that different gene co-expression combinations (two, three or four genes) or gene orders had different effects on the production of SA. SA production of the recombinant BL21-GBAE reached to 886.38 mg·L(-1), which was 17-fold (P < 0.05) of the parent strain BL21 (ΔaroL/aroK, DE3).

  11. Bioinformatics and co-expression network analysis of differentially expressed lncRNAs and mRNAs in hippocampus of APP/PS1 transgenic mice with Alzheimer disease

    PubMed Central

    Fang, Min; Zhang, Pei; Zhao, Yanxin; Liu, Xueyuan

    2017-01-01

    APP/PS1 transgenic mice with Alzheimer disease (AD) are widely used as a reliable animal model in studies about behaviors, physiology, biochemistry and histomorphology of AD, but few studies have been conducted to investigate the role of lncRNAs in this model. In this study, lncRNA microarray was employed to detect the gene expression profile and lncRNA expression profile in the mouse brain. Then, bioinformatics was used to predict the differentially expressed genes related to AD (n=20). Among different lncRNAs (n=249), 99 were downregulated and 150 upregulated. Co-expression network was applied to analyze the co-expression of differential lncRNAs and different genes. In network, lncRNA Gm13498 and lncRNA 1700030L20Rik correlated with the most genes and their degrees were 6 and 5, respectively. Then, the function and signal transduction pathways related to the differentially co-expressed lncRNAs were analyzed with bioinformatics, and results showed that these lncRNAs were involved in the systemic development of neurons, intercellular communication, regulation of action potential of neurons, development and differentiation of oligodendrocytes, neurotransmitters transmission, and neuronal regeneration. Realtime PCR was employed to detect the expression of relevant lncRNAs and differentially expressed RNAs in 10 samples, and results were consistent with above findings from microarray. PMID:28386363

  12. The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells

    PubMed Central

    Gambardella, Gennaro; Carissimo, Annamaria; Chen, Amy; Cutillo, Luisa; Nowakowski, Tomasz J.; di Bernardo, Diego; Blelloch, Robert

    2017-01-01

    MicroRNAs act posttranscriptionally to suppress multiple target genes within a cell population. To what extent this multi-target suppression occurs in individual cells and how it impacts transcriptional heterogeneity and gene co-expression remains unknown. Here we used single-cell sequencing combined with introduction of individual microRNAs. miR-294 and let-7c were introduced into otherwise microRNA-deficient Dgcr8 knockout mouse embryonic stem cells. Both microRNAs induce suppression and correlated expression of their respective gene targets. The two microRNAs had opposing effects on transcriptional heterogeneity within the cell population, with let-7c increasing and miR-294 decreasing the heterogeneity between cells. Furthermore, let-7c promotes, whereas miR-294 suppresses, the phasing of cell cycle genes. These results show at the individual cell level how a microRNA simultaneously has impacts on its many targets and how that in turn can influence a population of cells. The findings have important implications in the understanding of how microRNAs influence the co-expression of genes and pathways, and thus ultimately cell fate. PMID:28102192

  13. Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

    NASA Astrophysics Data System (ADS)

    Wang, Yongcui; Zhao, Weiling; Zhou, Xiaobo

    2016-10-01

    Accurate identification of coherent transcriptional modules (subnetworks) in adipose and muscle tissues is important for revealing the related mechanisms and co-regulated pathways involved in the development of aging-related diseases. Here, we proposed a systematically computational approach, called ICEGM, to Identify the Co-Expression Gene Modules through a novel mathematical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD). ICEGM was applied on the adipose, and heart and skeletal muscle tissues in old and young female African green vervet monkeys. The genes associated with the development of inflammation, cardiovascular and skeletal disorder diseases, and cancer were revealed by the ICEGM. Meanwhile, genes in the ICEGM modules were also enriched in the adipocytes, smooth muscle cells, cardiac myocytes, and immune cells. Comprehensive disease annotation and canonical pathway analysis indicated that immune cells, adipocytes, cardiomyocytes, and smooth muscle cells played a synergistic role in cardiac and physical functions in the aged monkeys by regulation of the biological processes associated with metabolism, inflammation, and atherosclerosis. In conclusion, the ICEGM provides an efficiently systematic framework for decoding the co-expression gene modules in multiple tissues. Analysis of genes in the ICEGM module yielded important insights on the cooperative role of multiple tissues in the development of diseases.

  14. Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

    PubMed Central

    Wang, Yongcui; Zhao, Weiling; Zhou, Xiaobo

    2016-01-01

    Accurate identification of coherent transcriptional modules (subnetworks) in adipose and muscle tissues is important for revealing the related mechanisms and co-regulated pathways involved in the development of aging-related diseases. Here, we proposed a systematically computational approach, called ICEGM, to Identify the Co-Expression Gene Modules through a novel mathematical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD). ICEGM was applied on the adipose, and heart and skeletal muscle tissues in old and young female African green vervet monkeys. The genes associated with the development of inflammation, cardiovascular and skeletal disorder diseases, and cancer were revealed by the ICEGM. Meanwhile, genes in the ICEGM modules were also enriched in the adipocytes, smooth muscle cells, cardiac myocytes, and immune cells. Comprehensive disease annotation and canonical pathway analysis indicated that immune cells, adipocytes, cardiomyocytes, and smooth muscle cells played a synergistic role in cardiac and physical functions in the aged monkeys by regulation of the biological processes associated with metabolism, inflammation, and atherosclerosis. In conclusion, the ICEGM provides an efficiently systematic framework for decoding the co-expression gene modules in multiple tissues. Analysis of genes in the ICEGM module yielded important insights on the cooperative role of multiple tissues in the development of diseases. PMID:27703186

  15. Co-expression of two heterologous lactate dehydrogenases genes in Kluyveromyces marxianus for l-lactic acid production.

    PubMed

    Lee, Jae Won; In, Jung Hoon; Park, Joon-Bum; Shin, Jonghyeok; Park, Jin Hwan; Sung, Bong Hyun; Sohn, Jung-Hoon; Seo, Jin-Ho; Park, Jin-Byoung; Kim, Soo Rin; Kweon, Dae-Hyuk

    2017-01-10

    Lactic acid (LA) is a versatile compound used in the food, pharmaceutical, textile, leather, and chemical industries. Biological production of LA is possible by yeast strains expressing a bacterial gene encoding l-lactate dehydrogenase (LDH). Kluyveromyces marxianus is an emerging non-conventional yeast with various phenotypes of industrial interest. However, it has not been extensively studied for LA production. In this study, K. marxianus was engineered to express and co-express various heterologous LDH enzymes that were reported to have different pH optimums. Specifically, three LDH enzymes originating from Staphylococcus epidermidis (SeLDH; optimal at pH 5.6), Lactobacillus acidophilus (LaLDH; optimal at pH 5.3), and Bos taurus (BtLDH; optimal at pH 9.8) were functionally expressed individually and in combination in K. marxianus, and the resulting strains were compared in terms of LA production. A strain co-expressing SeLDH and LaLDH (KM5 La+SeLDH) produced 16.0g/L LA, whereas the strains expressing those enzymes individually produced only 8.4 and 6.8g/L, respectively. This co-expressing strain produced 24.0g/L LA with a yield of 0.48g/g glucose in the presence of CaCO3. Our results suggest that co-expression of LDH enzymes with different pH optimums provides sufficient LDH activity under dynamic intracellular pH conditions, leading to enhanced production of LA compared to individual expression of the LDH enzymes.

  16. The Brassica rapa FLC homologue FLC2 is a key regulator of flowering time, identified through transcriptional co-expression networks

    PubMed Central

    Xiao, Dong; Zhao, Jian J.; Bonnema, Guusje

    2013-01-01

    The role of many genes and interactions among genes involved in flowering time have been studied extensively in Arabidopsis, and the purpose of this study was to investigate how effectively results obtained with the model species Arabidopsis can be applied to the Brassicacea with often larger and more complex genomes. Brassica rapa represents a very close relative, with its triplicated genome, with subgenomes having evolved by genome fractionation. The question of whether this genome fractionation is a random process, or whether specific genes are preferentially retained, such as flowering time (Ft) genes that play a role in the extreme morphological variation within the B. rapa species (displayed by the diverse morphotypes), is addressed. Data are presented showing that indeed Ft genes are preferentially retained, so the next intriguing question is whether these different orthologues of Arabidopsis Ft genes play similar roles compared with Arabidopsis, and what is the role of these different orthologues in B. rapa. Using a genetical–genomics approach, co-location of flowering quantitative trait loci (QTLs) and expression QTLs (eQTLs) resulted in identification of candidate genes for flowering QTLs and visualization of co-expression networks of Ft genes and flowering time. A major flowering QTL on A02 at the BrFLC2 locus co-localized with cis eQTLs for BrFLC2, BrSSR1, and BrTCP11, and trans eQTLs for the photoperiod gene BrCO and two paralogues of the floral integrator genes BrSOC1 and BrFT. It is concluded that the BrFLC2 Ft gene is a major regulator of flowering time in the studied doubled haploid population. PMID:24078668

  17. The Brassica rapa FLC homologue FLC2 is a key regulator of flowering time, identified through transcriptional co-expression networks.

    PubMed

    Xiao, Dong; Zhao, Jian J; Hou, Xi L; Basnet, Ram K; Carpio, Dunia P D; Zhang, Ning W; Bucher, Johan; Lin, Ke; Cheng, Feng; Wang, Xiao W; Bonnema, Guusje

    2013-11-01

    The role of many genes and interactions among genes involved in flowering time have been studied extensively in Arabidopsis, and the purpose of this study was to investigate how effectively results obtained with the model species Arabidopsis can be applied to the Brassicacea with often larger and more complex genomes. Brassica rapa represents a very close relative, with its triplicated genome, with subgenomes having evolved by genome fractionation. The question of whether this genome fractionation is a random process, or whether specific genes are preferentially retained, such as flowering time (Ft) genes that play a role in the extreme morphological variation within the B. rapa species (displayed by the diverse morphotypes), is addressed. Data are presented showing that indeed Ft genes are preferentially retained, so the next intriguing question is whether these different orthologues of Arabidopsis Ft genes play similar roles compared with Arabidopsis, and what is the role of these different orthologues in B. rapa. Using a genetical-genomics approach, co-location of flowering quantitative trait loci (QTLs) and expression QTLs (eQTLs) resulted in identification of candidate genes for flowering QTLs and visualization of co-expression networks of Ft genes and flowering time. A major flowering QTL on A02 at the BrFLC2 locus co-localized with cis eQTLs for BrFLC2, BrSSR1, and BrTCP11, and trans eQTLs for the photoperiod gene BrCO and two paralogues of the floral integrator genes BrSOC1 and BrFT. It is concluded that the BrFLC2 Ft gene is a major regulator of flowering time in the studied doubled haploid population.

  18. Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes.

    PubMed

    Baskerville, Scott; Bartel, David P

    2005-03-01

    MicroRNAs (miRNAs) are short endogenous RNAs known to post-transcriptionally repress gene expression in animals and plants. A microarray profiling survey revealed the expression patterns of 175 human miRNAs across 24 different human organs. Our results show that proximal pairs of miRNAs are generally coexpressed. In addition, an abrupt transition in the correlation between pairs of expressed miRNAs occurs at a distance of 50 kb, implying that miRNAs separated by <50 kb typically derive from a common transcript. Some microRNAs are within the introns of host genes. Intronic miRNAs are usually coordinately expressed with their host gene mRNA, implying that they also generally derive from a common transcript, and that in situ analyses of host gene expression can be used to probe the spatial and temporal localization of intronic miRNAs.

  19. Gene Coexpression Analysis Reveals Complex Metabolism of the Monoterpene Alcohol Linalool in Arabidopsis Flowers[W][OPEN

    PubMed Central

    Ginglinger, Jean-François; Boachon, Benoit; Höfer, René; Paetz, Christian; Köllner, Tobias G.; Miesch, Laurence; Lugan, Raphael; Baltenweck, Raymonde; Mutterer, Jérôme; Ullmann, Pascaline; Beran, Franziska; Claudel, Patricia; Verstappen, Francel; Fischer, Marc J.C.; Karst, Francis; Bouwmeester, Harro; Miesch, Michel; Schneider, Bernd; Gershenzon, Jonathan; Ehlting, Jürgen; Werck-Reichhart, Danièle

    2013-01-01

    The cytochrome P450 family encompasses the largest family of enzymes in plant metabolism, and the functions of many of its members in Arabidopsis thaliana are still unknown. Gene coexpression analysis pointed to two P450s that were coexpressed with two monoterpene synthases in flowers and were thus predicted to be involved in monoterpenoid metabolism. We show that all four selected genes, the two terpene synthases (TPS10 and TPS14) and the two cytochrome P450s (CYP71B31 and CYP76C3), are simultaneously expressed at anthesis, mainly in upper anther filaments and in petals. Upon transient expression in Nicotiana benthamiana, the TPS enzymes colocalize in vesicular structures associated with the plastid surface, whereas the P450 proteins were detected in the endoplasmic reticulum. Whether they were expressed in Saccharomyces cerevisiae or in N. benthamiana, the TPS enzymes formed two different enantiomers of linalool: (−)-(R)-linalool for TPS10 and (+)-(S)-linalool for TPS14. Both P450 enzymes metabolize the two linalool enantiomers to form different but overlapping sets of hydroxylated or epoxidized products. These oxygenated products are not emitted into the floral headspace, but accumulate in floral tissues as further converted or conjugated metabolites. This work reveals complex linalool metabolism in Arabidopsis flowers, the ecological role of which remains to be determined. PMID:24285789

  20. Genome-Wide Identification, Evolution, and Co-expression Network Analysis of Mitogen-Activated Protein Kinase Kinase Kinases in Brachypodium distachyon

    PubMed Central

    Feng, Kewei; Liu, Fuyan; Zou, Jinwei; Xing, Guangwei; Deng, Pingchuan; Song, Weining; Tong, Wei; Nie, Xiaojun

    2016-01-01

    Mitogen-activated protein kinase (MAPK) cascades are the conserved and universal signal transduction modules in all eukaryotes, which play the vital roles in plant growth, development, and in response to multiple stresses. In this study, we used bioinformatics methods to identify 86 MAPKKK protein encoded by 73 MAPKKK genes in Brachypodium. Phylogenetic analysis of MAPKKK family from Arabidopsis, rice, and Brachypodium has classified them into three subfamilies, of which 28 belonged to MEKK, 52 to Raf, and 6 to ZIK subfamily, respectively. Conserved protein motif, exon-intron organization, and splicing intron phase in kinase domains supported the evolutionary relationships inferred from the phylogenetic analysis. And gene duplication analysis suggested the chromosomal segment duplication happened before the divergence of the rice and Brachypodium, while all of three tandem duplicated gene pairs happened after their divergence. We further demonstrated that the MAPKKKs have evolved under strong purifying selection, implying the conservation of them. The splicing transcripts expression analysis showed that the splicesome translating longest protein tended to be adopted. Furthermore, the expression analysis of BdMAPKKKs in different organs and development stages as well as heat, virus and drought stresses revealed that the MAPKKK genes were involved in various signaling pathways. And the circadian analysis suggested there were 41 MAPKKK genes in Brachypodium showing cycled expression in at least one condition, of which seven MAPKKK genes expressed in all conditions and the promoter analysis indicated these genes possessed many cis-acting regulatory elements involved in circadian and light response. Finally, the co-expression network of MAPK, MAPKK, and MAPKKK in Brachypodium was constructed using 144 microarray and RNA-seq datasets, and ten potential MAPK cascades pathway were predicted. To conclude, our study provided the important information for evolutionary and

  1. The biosynthetic gene cluster for the cyanogenic glucoside dhurrin in Sorghum bicolor contains its co-expressed vacuolar MATE transporter

    PubMed Central

    Darbani, Behrooz; Motawia, Mohammed Saddik; Olsen, Carl Erik; Nour-Eldin, Hussam H.; Møller, Birger Lindberg; Rook, Fred

    2016-01-01

    Genomic gene clusters for the biosynthesis of chemical defence compounds are increasingly identified in plant genomes. We previously reported the independent evolution of biosynthetic gene clusters for cyanogenic glucoside biosynthesis in three plant lineages. Here we report that the gene cluster for the cyanogenic glucoside dhurrin in Sorghum bicolor additionally contains a gene, SbMATE2, encoding a transporter of the multidrug and toxic compound extrusion (MATE) family, which is co-expressed with the biosynthetic genes. The predicted localisation of SbMATE2 to the vacuolar membrane was demonstrated experimentally by transient expression of a SbMATE2-YFP fusion protein and confocal microscopy. Transport studies in Xenopus laevis oocytes demonstrate that SbMATE2 is able to transport dhurrin. In addition, SbMATE2 was able to transport non-endogenous cyanogenic glucosides, but not the anthocyanin cyanidin 3-O-glucoside or the glucosinolate indol-3-yl-methyl glucosinolate. The genomic co-localisation of a transporter gene with the biosynthetic genes producing the transported compound is discussed in relation to the role self-toxicity of chemical defence compounds may play in the formation of gene clusters. PMID:27841372

  2. Co-expression of Cyanobacterial Genes for Arsenic Methylation and Demethylation in Escherichia coli Offers Insights into Arsenic Resistance

    PubMed Central

    Yan, Yu; Xue, Xi-Mei; Guo, Yu-Qing; Zhu, Yong-Guan; Ye, Jun

    2017-01-01

    Arsenite [As(III)] and methylarsenite [MAs(III)] are the most toxic inorganic and methylated arsenicals, respectively. As(III) and MAs(III) can be interconverted in the unicellular cyanobacterium Nostoc sp. PCC 7120 (Nostoc), which has both the arsM gene (NsarsM), which is responsible for arsenic methylation, and the arsI gene (NsarsI), which is responsible for MAs(III) demethylation. It is not clear how the cells prevent a futile cycle of methylation and demethylation. To investigate the relationship between arsenic methylation and demethylation, we constructed strains of Escherichia coli AW3110 (ΔarsRBC) expressing NsarsM or/and NsarsI. Expression of NsarsI conferred MAs(III) resistance through MAs(III) demethylation. Compared to NsArsI, NsArsM conferred higher resistance to As(III) and lower resistance to MAs(III) by methylating both As(III) and MAs(III). The major species found in solution was dimethylarsenate [DMAs(V)]. Co-expression of NsarsM and NsarsI conferred As(III) resistance at levels similar to that with NsarsM alone, although the main species found in solution after As(III) biotransformation was methylarsenate [MAs(V)] rather than DMAs(V). Co-expression of NsarsM and NsarsI conferred a higher level of resistance to MAs(III) than found with expression of NsarsM alone but lower than expression of only NsarsI. Cells co-expressing both genes converted MAs(III) to a mixture of As(III) and DMAs(V). In Nostoc NsarsM is constitutively expressed, while NsarsI is inducible by either As(III) or MAs(III). Thus, our results suggest that at low concentrations of arsenic, NsArsM activity predominates, while NsArsI activity predominates at high concentrations. We propose that coexistence of arsM and arsI genes in Nostoc could be advantageous for several reasons. First, it confers a broader spectrum of resistance to both As(III) and MAs(III). Second, at low concentrations of arsenic, the MAs(III) produced by NsArsM will possibly have antibiotic-like properties and

  3. Genome-Wide Identification, Phylogenetic and Co-Expression Analysis of OsSET Gene Family in Rice

    PubMed Central

    Lu, Zhanhua; Huang, Xiaolong; Ouyang, Yidan; Yao, Jialing

    2013-01-01

    Background SET domain is responsible for the catalytic activity of histone lysine methyltransferases (HKMTs) during developmental process. Histone lysine methylation plays a crucial and diverse regulatory function in chromatin organization and genome function. Although several SET genes have been identified and characterized in plants, the understanding of OsSET gene family in rice is still very limited. Methodology/Principal Findings In this study, a systematic analysis was performed and revealed the presence of at least 43 SET genes in rice genome. Phylogenetic and structural analysis grouped SET proteins into five classes, and supposed that the domains out of SET domain were significant for the specific of histone lysine methylation, as well as the recognition of methylated histone lysine. Based on the global microarray, gene expression profile revealed that the transcripts of OsSET genes were accumulated differentially during vegetative and reproductive developmental stages and preferentially up or down-regulated in different tissues. Cis-elements identification, co-expression analysis and GO analysis of expression correlation of 12 OsSET genes suggested that OsSET genes might be involved in cell cycle regulation and feedback. Conclusions/Significance This study will facilitate further studies on OsSET family and provide useful clues for functional validation of OsSETs. PMID:23762371

  4. Co-expression of G2-EPSPS and glyphosate acetyltransferase GAT genes conferring high tolerance to glyphosate in soybean

    PubMed Central

    Guo, Bingfu; Guo, Yong; Hong, Huilong; Jin, Longguo; Zhang, Lijuan; Chang, Ru-Zhen; Lu, Wei; Lin, Min; Qiu, Li-Juan

    2015-01-01

    Glyphosate is a widely used non-selective herbicide with broad spectrum of weed control around the world. At present, most of the commercial glyphosate tolerant soybeans utilize glyphosate tolerant gene CP4-EPSPS or glyphosate acetyltransferase gene GAT separately. In this study, both glyphosate tolerant gene G2-EPSPS and glyphosate degraded gene GAT were co-transferred into soybean and transgenic plants showed high tolerance to glyphosate. Molecular analysis including PCR, Sothern blot, qRT-PCR, and Western blot revealed that target genes have been integrated into genome and expressed effectively at both mRNA and protein levels. Furthermore, the glyphosate tolerance analysis showed that no typical symptom was observed when compared with a glyphosate tolerant line HJ06-698 derived from GR1 transgenic soybean even at fourfold labeled rate of Roundup. Chlorophyll and shikimic acid content analysis of transgenic plant also revealed that these two indexes were not significantly altered after glyphosate application. These results indicated that co-expression of G2-EPSPS and GAT conferred high tolerance to the herbicide glyphosate in soybean. Therefore, combination of tolerant and degraded genes provides a new strategy for developing glyphosate tolerant transgenic crops. PMID:26528311

  5. Co-expression of G2-EPSPS and glyphosate acetyltransferase GAT genes conferring high tolerance to glyphosate in soybean.

    PubMed

    Guo, Bingfu; Guo, Yong; Hong, Huilong; Jin, Longguo; Zhang, Lijuan; Chang, Ru-Zhen; Lu, Wei; Lin, Min; Qiu, Li-Juan

    2015-01-01

    Glyphosate is a widely used non-selective herbicide with broad spectrum of weed control around the world. At present, most of the commercial glyphosate tolerant soybeans utilize glyphosate tolerant gene CP4-EPSPS or glyphosate acetyltransferase gene GAT separately. In this study, both glyphosate tolerant gene G2-EPSPS and glyphosate degraded gene GAT were co-transferred into soybean and transgenic plants showed high tolerance to glyphosate. Molecular analysis including PCR, Sothern blot, qRT-PCR, and Western blot revealed that target genes have been integrated into genome and expressed effectively at both mRNA and protein levels. Furthermore, the glyphosate tolerance analysis showed that no typical symptom was observed when compared with a glyphosate tolerant line HJ06-698 derived from GR1 transgenic soybean even at fourfold labeled rate of Roundup. Chlorophyll and shikimic acid content analysis of transgenic plant also revealed that these two indexes were not significantly altered after glyphosate application. These results indicated that co-expression of G2-EPSPS and GAT conferred high tolerance to the herbicide glyphosate in soybean. Therefore, combination of tolerant and degraded genes provides a new strategy for developing glyphosate tolerant transgenic crops.

  6. SiBIC: a web server for generating gene set networks based on biclusters obtained by maximal frequent itemset mining.

    PubMed

    Takahashi, Kei-ichiro; Takigawa, Ichigaku; Mamitsuka, Hiroshi

    2013-01-01

    Detecting biclusters from expression data is useful, since biclusters are coexpressed genes under only part of all given experimental conditions. We present a software called SiBIC, which from a given expression dataset, first exhaustively enumerates biclusters, which are then merged into rather independent biclusters, which finally are used to generate gene set networks, in which a gene set assigned to one node has coexpressed genes. We evaluated each step of this procedure: 1) significance of the generated biclusters biologically and statistically, 2) biological quality of merged biclusters, and 3) biological significance of gene set networks. We emphasize that gene set networks, in which nodes are not genes but gene sets, can be more compact than usual gene networks, meaning that gene set networks are more comprehensible. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/miami/faces/index.jsp.

  7. Gene cloning and soluble expression of Aspergillus niger phytase in E. coli cytosol via chaperone co-expression.

    PubMed

    Ushasree, Mrudula Vasudevan; Vidya, Jalaja; Pandey, Ashok

    2014-01-01

    A phytase gene from Aspergillus niger was isolated and two Escherichia coli expression systems, based on T7 RNA polymerase promoter and tac promoter, were used for its recombinant expression. Co-expression of molecular chaperone, GroES/EL, aided functional cytosolic expression of the phytase in E. coli BL21 (DE3). Untagged and maltose-binding protein-tagged recombinant phytase showed an activity band of ~49 and 92 kDa, respectively, on a zymogram. Heterologously-expressed phytase was fractionated from endogenous E. coli phytase by (NH4)2SO4 precipitation. The enzyme had optimum activity at 50 °C and pH 6.5.

  8. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    PubMed

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  9. Inferring gene regression networks with model trees

    PubMed Central

    2010-01-01

    Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate

  10. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond.

    PubMed

    Gama-Castro, Socorro; Salgado, Heladia; Santos-Zavaleta, Alberto; Ledezma-Tejeida, Daniela; Muñiz-Rascado, Luis; García-Sotelo, Jair Santiago; Alquicira-Hernández, Kevin; Martínez-Flores, Irma; Pannier, Lucia; Castro-Mondragón, Jaime Abraham; Medina-Rivera, Alejandra; Solano-Lira, Hilda; Bonavides-Martínez, César; Pérez-Rueda, Ernesto; Alquicira-Hernández, Shirley; Porrón-Sotelo, Liliana; López-Fuentes, Alejandra; Hernández-Koutoucheva, Anastasia; Del Moral-Chávez, Víctor; Rinaldi, Fabio; Collado-Vides, Julio

    2016-01-04

    RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for 'neighborhood' genes to known operons and regulons, and computational developments.

  11. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond

    PubMed Central

    Gama-Castro, Socorro; Salgado, Heladia; Santos-Zavaleta, Alberto; Ledezma-Tejeida, Daniela; Muñiz-Rascado, Luis; García-Sotelo, Jair Santiago; Alquicira-Hernández, Kevin; Martínez-Flores, Irma; Pannier, Lucia; Castro-Mondragón, Jaime Abraham; Medina-Rivera, Alejandra; Solano-Lira, Hilda; Bonavides-Martínez, César; Pérez-Rueda, Ernesto; Alquicira-Hernández, Shirley; Porrón-Sotelo, Liliana; López-Fuentes, Alejandra; Hernández-Koutoucheva, Anastasia; Moral-Chávez, Víctor Del; Rinaldi, Fabio; Collado-Vides, Julio

    2016-01-01

    RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for ‘neighborhood’ genes to known operons and regulons, and computational developments. PMID:26527724

  12. Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

    PubMed

    Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

    2012-07-15

    Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary

  13. Searching for coexpressed genes in three-color cDNA microarray data using a probabilistic model-based Hough Transform.

    PubMed

    Tino, Peter; Zhao, Hongya; Yan, Hong

    2011-01-01

    The effects of a drug on the genomic scale can be assessed in a three-color cDNA microarray with the three color intensities represented through the so-called hexaMplot. In our recent study, we have shown that the Hough Transform (HT) applied to the hexaMplot can be used to detect groups of coexpressed genes in the normal-disease-drug samples. However, the standard HT is not well suited for the purpose because 1) the assayed genes need first to be hard-partitioned into equally and differentially expressed genes, with HT ignoring possible information in the former group; 2) the hexaMplot coordinates are negatively correlated and there is no direct way of expressing this in the standard HT and 3) it is not clear how to quantify the association of coexpressed genes with the line along which they cluster. We address these deficiencies by formulating a dedicated probabilistic model-based HT. The approach is demonstrated by assessing effects of the drug Rg1 on homocysteine-treated human umbilical vein endothetial cells. Compared with our previous study, we robustly detect stronger natural groupings of coexpressed genes. Moreover, the gene groups show coherent biological functions with high significance, as detected by the Gene Ontology analysis.

  14. Co-expression of plasmid-mediated quinolone resistance-qnrA1 and blaVEB-1 gene in a Providencia stuartii strain.

    PubMed

    Nazik, Hasan; Bektöre, Bayhan; Öngen, Betigül; Özyurt, Mustafa; Baylan, Orhan; Haznedaroğlu, Tunçer

    2011-04-01

    An extended-spectrum B-lactamase (ESBL)-producing Providencia stuartii isolate was studied. A qnrA1 gene co-expressing blaVEB-1 gene was detected. Both genes were transferred to the recipient strain. The ciprofloxacin MIC of recipient strain increased tenfold. The blaVEB-1 gene persisted in microorganisms in Turkey but it also spread with PMQR genes to other species. The combination of PMQR with multidrug resistant isolates producing ESBLs may compromise the use of valuable antibiotics. Serious efforts are necessary to detect PMQR determinants not only with common B-lactamases in widespread pathogens but also with uncommon forms that are encountered infrequently.

  15. Coexpression of pyruvate decarboxylase and alcohol dehydrogenase genes in Lactobacillus brevis.

    PubMed

    Liu, Siqing; Dien, Bruce S; Nichols, Nancy N; Bischoff, Kenneth M; Hughes, Stephen R; Cotta, Michael A

    2007-09-01

    Lactobacillus brevis ATCC367 was engineered to express pyruvate decarboxylase (PDC) and alcohol dehydrogenase (ADH) genes in order to increase ethanol fermentation from biomass-derived residues. First, a Gram-positive Sarcina ventriculi PDC gene (Svpdc) was introduced into L. brevis ATCC 367 to obtain L. brevis bbc03. The SvPDC was detected by immunoblot using an SvPDC oligo peptide antiserum, but no increased ethanol was detected in L. brevis bbc03. Then, an ADH gene from L. brevis (Bradh) was cloned behind the Svpdc gene that generated a pdc/adh-coupled ethanol cassette pBBC04. The pBBC04 restored anaerobic growth and conferred ethanol production of Escheirichia coli NZN111 (a fermentative defective strain incapable of growing anaerobically). Approximately 58 kDa (SvPDC) and 28 kDa (BrADH) recombinant proteins were observed in L. brevis bbc04. These results indicated that the Gram-positive ethanol production genes can be expressed in L. brevis using a Gram-positive promoter and pTRKH2 shuttle vector. This work provides evidence that expressing Gram-positive ethanol genes in pentose utilizing L. brevis will further aid manipulation of this microbe toward biomass to ethanol production.

  16. Gene Co-Expression Analysis Inferring the Crosstalk of Ethylene and Gibberellin in Modulating the Transcriptional Acclimation of Cassava Root Growth in Different Seasons

    PubMed Central

    Saithong, Treenut; Saerue, Samorn; Kalapanulak, Saowalak; Sojikul, Punchapat; Narangajavana, Jarunya; Bhumiratana, Sakarindr

    2015-01-01

    Cassava is a crop of hope for the 21st century. Great advantages of cassava over other crops are not only the capacity of carbohydrates, but it is also an easily grown crop with fast development. As a plant which is highly tolerant to a poor environment, cassava has been believed to own an effective acclimation process, an intelligent mechanism behind its survival and sustainability in a wide range of climates. Herein, we aimed to investigate the transcriptional regulation underlying the adaptive development of a cassava root to different seasonal cultivation climates. Gene co-expression analysis suggests that AP2-EREBP transcription factor (ERF1) orthologue (D142) played a pivotal role in regulating the cellular response to exposing to wet and dry seasons. The ERF shows crosstalk with gibberellin, via ent-Kaurene synthase (D106), in the transcriptional regulatory network that was proposed to modulate the downstream regulatory system through a distinct signaling mechanism. While sulfur assimilation is likely to be a signaling regulation for dry crop growth response, calmodulin-binding protein is responsible for regulation in the wet crop. With our initiative study, we hope that our findings will pave the way towards sustainability of cassava production under various kinds of stress considering the future global climate change. PMID:26366737

  17. Ethanol production by Escherichia coli strains co-expressing Zymomonas PDC and ADH genes

    DOEpatents

    Ingram, Lonnie O.; Conway, Tyrrell; Alterthum, Flavio

    1991-01-01

    A novel operon and plasmids comprising genes which code for the alcohol dehydrogenase and pyruvate decarboxylase activities of Zymomonas mobilis are described. Also disclosed are methods for increasing the growth of microorganisms or eukaryotic cells and methods for reducing the accumulation of undesirable metabolic products in the growth medium of microorganisms or cells.

  18. Systems toxicology of chemically induced liver and kidney injuries: histopathology-associated gene co-expression modules.

    PubMed

    Te, Jerez A; AbdulHameed, Mohamed Diwan M; Wallqvist, Anders

    2016-09-01

    Organ injuries caused by environmental chemical exposures or use of pharmaceutical drugs pose a serious health risk that may be difficult to assess because of a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific histopathology outcomes via biomarkers will provide a foundation for designing precise and robust diagnostic tests. We identified co-expressed genes (modules) specific to injury endpoints using the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) - a toxicogenomics database containing organ-specific gene expression data matched to dose- and time-dependent chemical exposures and adverse histopathology assessments in Sprague-Dawley rats. We proposed a protocol for selecting gene modules associated with chemical-induced injuries that classify 11 liver and eight kidney histopathology endpoints based on dose-dependent activation of the identified modules. We showed that the activation of the modules for a particular chemical exposure condition, i.e., chemical-time-dose combination, correlated with the severity of histopathological damage in a dose-dependent manner. Furthermore, the modules could distinguish different types of injuries caused by chemical exposures as well as determine whether the injury module activation was specific to the tissue of origin (liver and kidney). The generated modules provide a link between toxic chemical exposures, different molecular initiating events among underlying molecular pathways and resultant organ damage. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.

  19. Molecular Cloning and Co-Expression of Phytoene Synthase Gene from Kocuria gwangalliensis in Escherichia coli.

    PubMed

    Seo, Yong Bae; Choi, Seong-Seok; Lee, Jong Kyu; Kim, Nan-Hee; Choi, Mi Jin; Kim, Jong-Myoung; Jeong, Tae Hyug; Nam, Soo-Wan; Lim, Han Kyu; Kim, Gun-Do

    2015-11-01

    A phytoene synthase gene, crtB, was isolated from Kocuria gwangalliensis. The crtB with 1,092 bp full-length has a coding sequence of 948 bp and encodes a 316-amino-acids protein. The deduced amino acid sequence showed a 70.9% identity with a putative phytoene synthase from K. rhizophila. An expression plasmid, pCcrtB, containing the crtB gene was constructed, and E. coli cells containing this plasmid produced the recombinant protein of approximately 34 kDa , corresponding to the molecular mass of phytoene synthase. Biosynthesis of lycopene was confirmed when the plasmid pCcrtB was co-transformed into E. coli containing pRScrtEI carrying the crtE and crtI genes encoding lycopene biosynthetic pathway enzymes. The results obtained from this study will provide a base of knowledge about the phytoene synthase of K. gwangalliensis and can be applied to the production of carotenoids in a non-carotenoidproducing host.

  20. Coexpression and secretion of endoglucanase and phytase genes in Lactobacillus reuteri.

    PubMed

    Wang, Lei; Yang, Yuxin; Cai, Bei; Cao, Pinghua; Yang, Mingming; Chen, Yulin

    2014-07-21

    A multifunctional transgenic Lactobacillus with probiotic characteristics and an ability to degrade β-glucan and phytic acid (phytate) was engineered to improve nutrient utilization, increase production performance and decrease digestive diseases in broiler chickens. The Bacillus subtilis WL001 endoglucanase gene (celW) and Aspergillus fumigatus WL002 phytase gene (phyW) mature peptide (phyWM) were cloned into an expression vector with the lactate dehydrogenase promoter of Lactobacillus casei and the secretion signal peptide of the Lactococcus lactis usp45 gene. This construct was then transformed into Lactobacillus reuteri XC1 that had been isolated from the gastrointestinal tract of broilers. Heterologous enzyme production and feed effectiveness of this genetically modified L. reuteri strain were investigated and evaluated. Sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis showed that the molecular mass of phyWM and celW was approximately 48.2 and 55 kDa, respectively, consistent with their predicted molecular weights. Endoglucanase and phytase activities in the extracellular fraction of the transformed L. reuteri culture were 0.68 and 0.42 U/mL, respectively. Transformed L. reuteri improved the feed conversion ratio of broilers from 21 to 42 days of age and over the whole feeding period. However, there was no effect on body weight gain and feed intake of chicks. Transformed L. reuteri supplementation improved levels of ash, calcium and phosphorus in tibiae at day 21 and of phosphorus at day 42. In addition, populations of Escherichia coli, Veillonella spp. and Bacteroides vulgatus were decreased, while populations of Bifidobacterium genus and Lactobacillus spp. were increased in the cecum at day 21.

  1. Coexpression and Secretion of Endoglucanase and Phytase Genes in Lactobacillus reuteri

    PubMed Central

    Wang, Lei; Yang, Yuxin; Cai, Bei; Cao, Pinghua; Yang, Mingming; Chen, Yulin

    2014-01-01

    A multifunctional transgenic Lactobacillus with probiotic characteristics and an ability to degrade β-glucan and phytic acid (phytate) was engineered to improve nutrient utilization, increase production performance and decrease digestive diseases in broiler chickens. The Bacillus subtilis WL001 endoglucanase gene (celW) and Aspergillus fumigatus WL002 phytase gene (phyW) mature peptide (phyWM) were cloned into an expression vector with the lactate dehydrogenase promoter of Lactobacillus casei and the secretion signal peptide of the Lactococcus lactis usp45 gene. This construct was then transformed into Lactobacillus reuteri XC1 that had been isolated from the gastrointestinal tract of broilers. Heterologous enzyme production and feed effectiveness of this genetically modified L. reuteri strain were investigated and evaluated. Sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis showed that the molecular mass of phyWM and celW was approximately 48.2 and 55 kDa, respectively, consistent with their predicted molecular weights. Endoglucanase and phytase activities in the extracellular fraction of the transformed L. reuteri culture were 0.68 and 0.42 U/mL, respectively. Transformed L. reuteri improved the feed conversion ratio of broilers from 21 to 42 days of age and over the whole feeding period. However, there was no effect on body weight gain and feed intake of chicks. Transformed L. reuteri supplementation improved levels of ash, calcium and phosphorus in tibiae at day 21 and of phosphorus at day 42. In addition, populations of Escherichia coli, Veillonella spp. and Bacteroides vulgatus were decreased, while populations of Bifidobacterium genus and Lactobacillus spp. were increased in the cecum at day 21. PMID:25050780

  2. Genome-scale co-expression network comparison across Escherichia coli and Salmonella enterica serovar Typhimurium reveals significant conservation at the regulon level of local regulators despite their dissimilar lifestyles.

    PubMed

    Zarrineh, Peyman; Sánchez-Rodríguez, Aminael; Hosseinkhan, Nazanin; Narimani, Zahra; Marchal, Kathleen; Masoudi-Nejad, Ali

    2014-01-01

    Availability of genome-wide gene expression datasets provides the opportunity to study gene expression across different organisms under a plethora of experimental conditions. In our previous work, we developed an algorithm called COMODO (COnserved MODules across Organisms) that identifies conserved expression modules between two species. In the present study, we expanded COMODO to detect the co-expression conservation across three organisms by adapting the statistics behind it. We applied COMODO to study expression conservation/divergence between Escherichia coli, Salmonella enterica, and Bacillus subtilis. We observed that some parts of the regulatory interaction networks were conserved between E. coli and S. enterica especially in the regulon of local regulators. However, such conservation was not observed between the regulatory interaction networks of B. subtilis and the two other species. We found co-expression conservation on a number of genes involved in quorum sensing, but almost no conservation for genes involved in pathogenicity across E. coli and S. enterica which could partially explain their different lifestyles. We concluded that despite their different lifestyles, no significant rewiring have occurred at the level of local regulons involved for instance, and notable conservation can be detected in signaling pathways and stress sensing in the phylogenetically close species S. enterica and E. coli. Moreover, conservation of local regulons seems to depend on the evolutionary time of divergence across species disappearing at larger distances as shown by the comparison with B. subtilis. Global regulons follow a different trend and show major rewiring even at the limited evolutionary distance that separates E. coli and S. enterica.

  3. Genome-Scale Co-Expression Network Comparison across Escherichia coli and Salmonella enterica Serovar Typhimurium Reveals Significant Conservation at the Regulon Level of Local Regulators Despite Their Dissimilar Lifestyles

    PubMed Central

    Zarrineh, Peyman; Sánchez-Rodríguez, Aminael; Hosseinkhan, Nazanin; Narimani, Zahra; Marchal, Kathleen; Masoudi-Nejad, Ali

    2014-01-01

    Availability of genome-wide gene expression datasets provides the opportunity to study gene expression across different organisms under a plethora of experimental conditions. In our previous work, we developed an algorithm called COMODO (COnserved MODules across Organisms) that identifies conserved expression modules between two species. In the present study, we expanded COMODO to detect the co-expression conservation across three organisms by adapting the statistics behind it. We applied COMODO to study expression conservation/divergence between Escherichia coli, Salmonella enterica, and Bacillus subtilis. We observed that some parts of the regulatory interaction networks were conserved between E. coli and S. enterica especially in the regulon of local regulators. However, such conservation was not observed between the regulatory interaction networks of B. subtilis and the two other species. We found co-expression conservation on a number of genes involved in quorum sensing, but almost no conservation for genes involved in pathogenicity across E. coli and S. enterica which could partially explain their different lifestyles. We concluded that despite their different lifestyles, no significant rewiring have occurred at the level of local regulons involved for instance, and notable conservation can be detected in signaling pathways and stress sensing in the phylogenetically close species S. enterica and E. coli. Moreover, conservation of local regulons seems to depend on the evolutionary time of divergence across species disappearing at larger distances as shown by the comparison with B. subtilis. Global regulons follow a different trend and show major rewiring even at the limited evolutionary distance that separates E. coli and S. enterica. PMID:25101984

  4. Identification of co-expressed gene signatures in mouse B1, marginal zone and B2 B-cell populations

    PubMed Central

    Mabbott, Neil A; Gray, David

    2014-01-01

    In mice, three major B-cell subsets have been identified with distinct functionalities: B1 B cells, marginal zone B cells and follicular B2 B cells. Here, we used the growing body of publicly available transcriptomics data to create an expression atlas of 84 gene expression microarray data sets of distinct mouse B-cell subsets. These data were subjected to network-based cluster analysis using BioLayout Express3D. Using this analysis tool, genes with related functions clustered together in discrete regions of the network graph and enabled the identification of transcriptional networks that underpinned the functional activity of distinct cell populations. Some gene clusters were expressed highly by most of the cell populations included in this analysis (such as those with activity related to house-keeping functions). Others contained genes with expression patterns specific to distinct B-cell subsets. While these clusters contained many genes typically associated with the activity of the cells they were specifically expressed in, many novel B-cell-subset-specific candidate genes were identified. A large number of uncharacterized genes were also represented in these B-cell lineage-specific clusters. Further analysis of the activities of these uncharacterized candidate genes will lead to the identification of novel B-cell lineage-specific transcription factors and regulators of B-cell function. We also analysed 36 microarray data sets from distinct human B-cell populations. These data showed that mouse and human germinal centre B cells shared similar transcriptional features, whereas mouse B1 B cells were distinct from proposed human B1 B cells. PMID:24032749

  5. Enhanced resistance to Sclerotinia sclerotiorum in Brassica napus by co-expression of defensin and chimeric chitinase genes.

    PubMed

    Zarinpanjeh, Nasim; Motallebi, Mostafa; Zamani, Mohammad Reza; Ziaei, Mahboobeh

    2016-11-01

    Sclerotinia stem rot caused by Sclerotinia sclerotiorum is one of the major fungal diseases of Brassica napus L. To develop resistance against this fungal disease, the defensin gene from Raphanus sativus and chimeric chit42 from Trichoderma atroviride with a C-terminal fused chitin-binding domain from Serratia marcescens were co-expressed in canola via Agrobacterium-mediated transformation. Twenty transformants were confirmed to carry the two transgenes as detected by polymerase chain reaction (PCR), with 4.8 % transformation efficiency. The chitinase activity of PCR-positive transgenic plants were measured in the presence of colloidal chitin, and five transgenic lines showing the highest chitinase activity were selected for checking the copy number of the transgenes through Southern blot hybridisation. Two plants carried a single copy of the transgenes, while the remainder carried either two or three copies of the transgenes. The antifungal activity of two transgenic lines that carried a single copy of the transgenes (T4 and T10) was studied by a radial diffusion assay. It was observed that the constitutive expression of these transgenes in the T4 and T10 transgenic lines suppressed the growth of S. sclerotiorum by 49 % and 47 %, respectively. The two transgenic lines were then let to self-pollinate to produce the T2 generation. Greenhouse bioassays were performed on the transgenic T2 young leaves by challenging with S. sclerotiorum and the results revealed that the expression of defensin and chimeric chitinase from a heterologous source in canola demonstrated enhanced resistance against sclerotinia stem rot disease.

  6. Dynamic Visualization of Co-expression in Systems Genetics Data

    SciTech Connect

    New, Joshua Ryan; Huang, Jian; Chesler, Elissa J

    2008-01-01

    Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.

  7. Stress-induced co-expression of two alternative oxidase (VuAox1 and 2b) genes in Vigna unguiculata.

    PubMed

    Costa, José Hélio; Mota, Erika Freitas; Cambursano, Mariana Virginia; Lauxmann, Martin Alexander; de Oliveira, Luciana Maia Nogueira; Silva Lima, Maria da Guia; Orellano, Elena Graciela; Fernandes de Melo, Dirce

    2010-05-01

    Cowpea (Vigna unguiculata) alternative oxidase is encoded by a small multigene family (Aox1, 2a and 2b) that is orthologous to the soybean Aox family. Like most of the identified Aox genes in plants, VuAox1 and VuAox2 consist of 4 exons interrupted by 3 introns. Alignment of the orthologous Aox genes revealed high identity of exons and intron variability, which is more prevalent in Aox1. In order to determine Aox gene expression in V. unguiculata, a steady-state analysis of transcripts involved in seed development (flowers, pods and dry seeds) and germination (soaked seeds) was performed and systemic co-expression of VuAox1 and VuAox2b was observed during germination. The analysis of Aox transcripts in leaves from seedlings under different stress conditions (cold, PEG, salicylate and H2O2 revealed stress-induced co-expression of both VuAox genes. Transcripts of VuAox2a and 2b were detected in all control seedlings, which was not the case for VuAox1 mRNA. Estimation of the primary transcript lengths of V. unguiculata and soybean Aox genes showed an intron length reduction for VuAox1 and 2b, suggesting that the two genes have converged in transcribed sequence length. Indeed, a bioinformatics analysis of VuAox1 and 2b promoters revealed a conserved region related to a cis-element that is responsive to oxidative stress. Taken together, the data provide evidence for co-expression of Aox1 and Aox2b in response to stress and also during the early phase of seed germination. The dual nature of VuAox2b expression (constitutive and induced) suggests that the constitutive Aox2b gene of V. unguiculata has acquired inducible regulatory elements.

  8. Linking Gene Expression and Functional Network Data in Human Heart Failure

    PubMed Central

    Camargo, Anyela; Azuaje, Francisco

    2007-01-01

    Background Gene expression profiling and the analysis of protein-protein interaction (PPI) networks may support the identification of disease bio-markers and potential drug targets. Thus, a step forward in the development of systems approaches to medicine is the integrative analysis of these data sources in specific pathological conditions. We report such an integrative bioinformatics analysis in human heart failure (HF). A global PPI network in HF was assembled, which by itself represents a useful compendium of the current status of human HF-relevant interactions. This provided the basis for the analysis of interaction connectivity patterns in relation to a HF gene expression data set. Results Relationships between the significance of the differentiation of gene expression and connectivity degrees in the PPI network were established. In addition, relationships between gene co-expression and PPI network connectivity were analysed. Highly-connected proteins are not necessarily encoded by genes significantly differentially expressed. Genes that are not significantly differentially expressed may encode proteins that exhibit diverse network connectivity patterns. Furthermore, genes that were not defined as significantly differentially expressed may encode proteins with many interacting partners. Genes encoding network hubs may exhibit weak co-expression with the genes encoding their interacting protein partners. We also found that hubs and superhubs display a significant diversity of co-expression patterns in comparison to peripheral nodes. Gene Ontology (GO) analysis established that highly-connected proteins are likely to be engaged in higher level GO biological process terms, while low-connectivity proteins tend to be engaged in more specific disease-related processes. Conclusion This investigation supports the hypothesis that the integrative analysis of differential gene expression and PPI network analysis may facilitate a better understanding of functional roles

  9. Co-expressed differentially expressed genes and long non-coding RNAs involved in the celecoxib treatment of gastric cancer: An RNA sequencing analysis

    PubMed Central

    Song, Bin; Du, Juan; Feng, Ye; Gao, Yong-Jian; Zhao, Ji-Sheng

    2016-01-01

    The aim of the present study was to investigate the mechanisms of long non-coding RNAs (lncRNAs) in a gastric cancer cell line treated with celecoxib. The human gastric carcinoma cell line NCI-N87 was treated with 15 µM celecoxib for 72 h (celecoxib group) and an equal volume of dimethylsulfoxide (control group), respectively. Libraries were constructed by NEBNext Ultra RNA Library Prep kit for Illumina. Paired-end RNA sequencing reads were aligned to a human hg19 reference genome using TopHat2. Differentially expressed genes (DEGs) and lncRNAs were identified using Cuffdiff. Enrichment analysis was performed using GO-function package and KEGG profile in Bioconductor. A protein-protein interaction network was constructed using STRING database and module analysis was performed using ClusterONE plugin of Cytoscape. ATP5G1, ATP5G3, COX8A, CYC1, NDUFS3, UQCRC1, UQCRC2 and UQCRFS1 were enriched in the oxidative phosphorylation pathway. CXCL1, CXCL3, CXCL5 and CXCL8 were enriched in the chemokine signaling and cytokine-cytokine receptor interaction pathways. ITGA3, ITGA6, ITGB4, ITGB5, ITGB6 and ITGB8 were enriched in the integrin-mediated signaling pathway. DEGs co-expressed with lnc-SCD-1:13, lnc-LRR1-1:2, lnc-PTMS-1:3, lnc-S100P-3:1, lnc-AP000974.1-1:1 and lnc-RAB3IL1-2:1 were enriched in the pathways associated with cancer, such as the basal cell carcinoma pathway in cancer. In conclusion, these DEGs and differentially expressed lncRNAs may be important in the celecoxib treatment of gastric cancer. PMID:27698747

  10. Co-expression of interleukin 12 enhances antitumor effects of a novel chimeric promoter-mediated suicide gene therapy in an immunocompetent mouse model

    SciTech Connect

    Xu, Yu; Liu, Zhengchun; Kong, Haiyan; Sun, Wenjie; Liao, Zhengkai; Zhou, Fuxiang; Xie, Conghua; and others

    2011-09-09

    Highlights: {yields} A novel chimeric promoter consisting of CArG element and hTERT promoter was developed. {yields} The promoter was characterized with radiation-inducibility and tumor-specificity. {yields} Suicide gene system driven by the promoter showed remarkable cytotoxicity in vitro. {yields} Co-expression of IL12 enhanced the promoter mediated suicide gene therapy in vivo. -- Abstract: The human telomerase reverse transcriptase (hTERT) promoter has been widely used in target gene therapy of cancer. However, low transcriptional activity limited its clinical application. Here, we designed a novel dual radiation-inducible and tumor-specific promoter system consisting of CArG elements and the hTERT promoter, resulting in increased expression of reporter genes after gamma-irradiation. Therapeutic and side effects of adenovirus-mediated horseradish peroxidase (HRP)/indole-3-acetic (IAA) system downstream of the chimeric promoter were evaluated in mice bearing Lewis lung carcinoma, combining with or without adenovirus-mediated interleukin 12 (IL12) gene driven by the cytomegalovirus promoter. The combination treatment showed more effective suppression of tumor growth than those with single agent alone, being associated with pronounced intratumoral T-lymphocyte infiltration and minor side effects. Our results suggest that the combination treatment with HRP/IAA system driven by the novel chimeric promoter and the co-expression of IL12 might be an effective and safe target gene therapy strategy of cancer.

  11. Introduction: Cancer Gene Networks.

    PubMed

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  12. Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Salmonella enterica serovar Typhimurium is a gram-negative bacterium that can colonize the gut of humans and several species of food producing farm animals to cause enteric or septicaemic salmonellosis. While many studies have looked into the host genetic response to Salmonella infection, relatively...

  13. Dynamic visualization of coexpression in systems genetics data.

    PubMed

    New, Joshua; Kendall, Wesley; Huang, Jian; Chesler, Elissa

    2008-01-01

    Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes residing in critical positions in networks and pathways. By using a graph as a universal representation of correlation in gene expression, our system employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by compound queries, dynamic level-of-detail abstraction, and template-based fuzzy classification. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.

  14. Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression phenotypes

    PubMed Central

    Gong, Bin-Sheng; Zhang, Qing-Pu; Zhang, Guang-Mei; Zhang, Shao-Jun; Zhang, Wei; Lv, Hong-Chao; Zhang, Fan; Lv, Sa-Li; Li, Chuan-Xing; Rao, Shao-Qi; Li, Xia

    2007-01-01

    Gene expression profiles and single-nucleotide polymorphism (SNP) profiles are modern data for genetic analysis. It is possible to use the two types of information to analyze the relationships among genes by some genetical genomics approaches. In this study, gene expression profiles were used as expression traits. And relationships among the genes, which were co-linked to a common SNP(s), were identified by integrating the two types of information. Further research on the co-expressions among the co-linked genes was carried out after the gene-SNP relationships were established using the Haseman-Elston sib-pair regression. The results showed that the co-expressions among the co-linked genes were significantly higher if the number of connections between the genes and a SNP(s) was more than six. Then, the genes were interconnected via one or more SNP co-linkers to construct a gene-SNP intermixed network. The genes sharing more SNPs tended to have a stronger correlation. Finally, a gene-gene network was constructed with their intensities of relationships (the number of SNP co-linkers shared) as the weights for the edges. PMID:18466544

  15. Discovering Functional Modules across Diverse Maize Transcriptomes Using COB, the Co-Expression Browser

    PubMed Central

    Schaefer, Robert J.; Briskine, Roman; Springer, Nathan M.; Myers, Chad L.

    2014-01-01

    Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks. PMID:24922320

  16. Reverse engineering transcriptional gene networks.

    PubMed

    Belcastro, Vincenzo; di Bernardo, Diego

    2014-01-01

    The aim of this chapter is a step-by-step guide on how to infer gene networks from gene expression profiles. The definition of a gene network is given in Subheading 1, where the different types of networks are discussed. The chapter then guides the readers through a data-gathering process in order to build a compendium of gene expression profiles from a public repository. Gene expression profiles are then discretized and a statistical relationship between genes, called mutual information (MI), is computed. Gene pairs with insignificant MI scores are then discarded by applying one of the described pruning steps. The retained relationships are then used to build up a Boolean adjacency matrix used as input for a clustering algorithm to divide the network into modules (or communities). The gene network can then be used as a hypothesis generator for discovering gene function and analyzing gene signatures. Some case studies are presented, and an online web-tool called Netview is described.

  17. Coexpression of platelet-derived growth factor (PDGF) and PDGF-receptor genes by primary human astrocytomas may contribute to their development and maintenance.

    PubMed Central

    Maxwell, M; Naber, S P; Wolfe, H J; Galanopoulos, T; Hedley-Whyte, E T; Black, P M; Antoniades, H N

    1990-01-01

    The present studies investigated the expression of the two PDGF genes (c-sis/PDGF-2 and PDGF-1) and the PDGF-receptor b gene (PDGF-R) in 34 primary human astrocytomas. Northern blot analysis demonstrated the coexpression of the c-sis/PDGF-2 protooncogene and the PDGF-R gene in all astrocytomas examined. The majority of the tumors also expressed the PDGF-1 gene. There was no correlation between the expression of the two PDGF genes. Nonmalignant human brain tissue expressed the PDGF-R and PDGF-1 genes but not the c-sis/PDGF-2 protooncogene. In situ hybridization of astrocytoma tissue localized the expression of the c-sis and PDGF-R mRNA's in tumor cells. Capillary endothelial cells also expressed c-sis mRNA. In contrast, nonmalignant human brain tissue expressed only PDGF-R mRNA but not c-sis/PDGF-2 mRNA. The coexpression of a potent mitogenic growth factor protooncogene (c-sis) and its receptor gene in astrocytoma tumor cells suggests the presence of an autocrine mechanism that may contribute to the development and maintenance of astrocytomas. The expression of c-sis mRNA in tumor cells but not in nonmalignant brain cells may serve as an additional diagnostic criterion for the detection of astrocytomas in small tissue specimen using in situ hybridization for the detection of c-sis mRNA and/or immunostaining for the recognition of its protein product. Images PMID:2164040

  18. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe

    PubMed Central

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies. PMID:27014338

  19. A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe.

    PubMed

    Berto, Stefano; Perdomo-Sabogal, Alvaro; Gerighausen, Daniel; Qin, Jing; Nowick, Katja

    2016-01-01

    Cognitive abilities, such as memory, learning, language, problem solving, and planning, involve the frontal lobe and other brain areas. Not much is known yet about the molecular basis of cognitive abilities, but it seems clear that cognitive abilities are determined by the interplay of many genes. One approach for analyzing the genetic networks involved in cognitive functions is to study the coexpression networks of genes with known importance for proper cognitive functions, such as genes that have been associated with cognitive disorders like intellectual disability (ID) or autism spectrum disorders (ASD). Because many of these genes are gene regulatory factors (GRFs) we aimed to provide insights into the gene regulatory networks active in the human frontal lobe. Using genome wide human frontal lobe expression data from 10 independent data sets, we first derived 10 individual coexpression networks for all GRFs including their potential target genes. We observed a high level of variability among these 10 independently derived networks, pointing out that relying on results from a single study can only provide limited biological insights. To instead focus on the most confident information from these 10 networks we developed a method for integrating such independently derived networks into a consensus network. This consensus network revealed robust GRF interactions that are conserved across the frontal lobes of different healthy human individuals. Within this network, we detected a strong central module that is enriched for 166 GRFs known to be involved in brain development and/or cognitive disorders. Interestingly, several hubs of the consensus network encode for GRFs that have not yet been associated with brain functions. Their central role in the network suggests them as excellent new candidates for playing an essential role in the regulatory network of the human frontal lobe, which should be investigated in future studies.

  20. A network of genes regulated by light in cyanobacteria.

    PubMed

    Aurora, Rajeev; Hihara, Yukako; Singh, Abhay K; Pakrasi, Himadri B

    2007-01-01

    Oxygenic photosynthetic organisms require light for their growth and development. However, exposure to high light is detrimental to them. Using time series microarray data from a model cyanobacterium, Synechocystis 6803 transferred from low to high light, we generated a gene co-expression network. The network has twelve sub-networks connected hierarchically, each consisting of an interconnected hub-and-spoke architecture. Within each sub-network, edges formed between genes that recapitulate known pathways. Analysis of the expression profiles shows that the cells undergo a phase transition 6-hours post-shift to high light, characterized by core sub-network. The core sub-network is enriched in proteins that (putatively) bind Fe-S clusters and proteins that mediate iron and sulfate homeostasis. At the center of this core is a sulfate permease, suggesting sulfate is rate limiting for cells grown in high light. To validate this novel finding, we demonstrate the limited ability of cell growth in sulfate-depleted medium in high light. This study highlights how understanding the organization of the networks can provide insights into the coordination of physiologic responses.

  1. Neutralization of Bacterial YoeBSpn Toxicity and Enhanced Plant Growth in Arabidopsis thaliana via Co-Expression of the Toxin-Antitoxin Genes

    PubMed Central

    Abu Bakar, Fauziah; Yeo, Chew Chieng; Harikrishna, Jennifer Ann

    2016-01-01

    Bacterial toxin-antitoxin (TA) systems have various cellular functions, including as part of the general stress response. The genome of the Gram-positive human pathogen Streptococcus pneumoniae harbors several putative TA systems, including yefM-yoeBSpn, which is one of four systems that had been demonstrated to be biologically functional. Overexpression of the yoeBSpn toxin gene resulted in cell stasis and eventually cell death in its native host, as well as in Escherichia coli. Our previous work showed that induced expression of a yoeBSpn toxin-Green Fluorescent Protein (GFP) fusion gene apparently triggered apoptosis and was lethal in the model plant, Arabidopsis thaliana. In this study, we investigated the effects of co-expression of the yefMSpn antitoxin and yoeBSpn toxin-GFP fusion in transgenic A. thaliana. When co-expressed in Arabidopsis, the YefMSpn antitoxin was found to neutralize the toxicity of YoeBSpn-GFP. Interestingly, the inducible expression of both yefMSpn antitoxin and yoeBSpn toxin-GFP fusion in transgenic hybrid Arabidopsis resulted in larger rosette leaves and taller plants with a higher number of inflorescence stems and increased silique production. To our knowledge, this is the first demonstration of a prokaryotic antitoxin neutralizing its cognate toxin in plant cells. PMID:27104531

  2. Identification and Network-Enabled Characterization of Auxin Response Factor Genes in Medicago truncatula

    PubMed Central

    Burks, David J.; Azad, Rajeev K.

    2016-01-01

    The Auxin Response Factor (ARF) family of transcription factors is an important regulator of environmental response and symbiotic nodulation in the legume Medicago truncatula. While previous studies have identified members of this family, a recent spurt in gene expression data coupled with genome update and reannotation calls for a reassessment of the prevalence of ARF genes and their interaction networks in M. truncatula. We performed a comprehensive analysis of the M. truncatula genome and transcriptome that entailed search for novel ARF genes and the co-expression networks. Our investigation revealed 8 novel M. truncatula ARF (MtARF) genes, of the total 22 identified, and uncovered novel gene co-expression networks as well. Furthermore, the topological clustering and single enrichment analysis of several network models revealed the roles of individual members of the MtARF family in nitrogen regulation, nodule initiation, and post-embryonic development through a specialized protein packaging and secretory pathway. In summary, this study not just shines new light on an important gene family, but also provides a guideline for identification of new members of gene families and their functional characterization through network analyses. PMID:28018393

  3. Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data.

    PubMed

    Farber, Charles R

    2010-11-01

    Bone mineral density (BMD) is influenced by a complex network of gene interactions; therefore, elucidating the relationships between genes and how those genes, in turn, influence BMD is critical for developing a comprehensive understanding of osteoporosis. To investigate the role of transcriptional networks in the regulation of BMD, we performed a weighted gene coexpression network analysis (WGCNA) using microarray expression data on monocytes from young individuals with low or high BMD. WGCNA groups genes into modules based on patterns of gene coexpression. and our analysis identified 11 gene modules. We observed that the overall expression of one module (referred to as module 9) was significantly higher in the low-BMD group (p = .03). Module 9 was highly enriched for genes belonging to the immune system-related gene ontology (GO) category "response to virus" (p = 7.6 × 10(-11)). Using publically available genome-wide association study data, we independently validated the importance of module 9 by demonstrating that highly connected module 9 hubs were more likely, relative to less highly connected genes, to be genetically associated with BMD. This study highlights the advantages of systems-level analyses to uncover coexpression modules associated with bone mass and suggests that particular monocyte expression patterns may mediate differences in BMD.

  4. Enhancement of γ-aminobutyric acid production in recombinant Corynebacterium glutamicum by co-expressing two glutamate decarboxylase genes from Lactobacillus brevis.

    PubMed

    Shi, Feng; Jiang, Junjun; Li, Yongfu; Li, Youxin; Xie, Yilong

    2013-11-01

    γ-Aminobutyric acid (GABA), a non-protein amino acid, is a bioactive component in the food, feed and pharmaceutical fields. To establish an effective single-step production system for GABA, a recombinant Corynebacterium glutamicum strain co-expressing two glutamate decarboxylase (GAD) genes (gadB1 and gadB2) derived from Lactobacillus brevis Lb85 was constructed. Compared with the GABA production of the gadB1 or gadB2 single-expressing strains, GABA production by the gadB1-gadB2 co-expressing strain increased more than twofold. By optimising urea supplementation, the total production of L-glutamate and GABA increased from 22.57 ± 1.24 to 30.18 ± 1.33 g L⁻¹, and GABA production increased from 4.02 ± 0.95 to 18.66 ± 2.11 g L⁻¹ after 84-h cultivation. Under optimal urea supplementation, L-glutamate continued to be consumed, GABA continued to accumulate after 36 h of fermentation, and the pH level fluctuated. GABA production increased to a maximum level of 27.13 ± 0.54 g L⁻¹ after 120-h flask cultivation and 26.32 g L⁻¹ after 60-h fed-batch fermentation. The conversion ratio of L-glutamate to GABA reached 0.60-0.74 mol mol⁻¹. By co-expressing gadB1 and gadB2 and optimising the urea addition method, C. glutamicum was genetically improved for de novo biosynthesis of GABA from its own accumulated L-glutamate.

  5. Detection of gene communities in multi-networks reveals cancer drivers

    NASA Astrophysics Data System (ADS)

    Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

    2015-12-01

    We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

  6. A moth pheromone brewery: production of (Z)-11-hexadecenol by heterologous co-expression of two biosynthetic genes from a noctuid moth in a yeast cell factory

    PubMed Central

    2013-01-01

    Background Moths (Lepidoptera) are highly dependent on chemical communication to find a mate. Compared to conventional unselective insecticides, synthetic pheromones have successfully served to lure male moths as a specific and environmentally friendly way to control important pest species. However, the chemical synthesis and purification of the sex pheromone components in large amounts is a difficult and costly task. The repertoire of enzymes involved in moth pheromone biosynthesis in insecta can be seen as a library of specific catalysts that can be used to facilitate the synthesis of a particular chemical component. In this study, we present a novel approach to effectively aid in the preparation of semi-synthetic pheromone components using an engineered vector co-expressing two key biosynthetic enzymes in a simple yeast cell factory. Results We first identified and functionally characterized a ∆11 Fatty-Acyl Desaturase and a Fatty-Acyl Reductase from the Turnip moth, Agrotis segetum. The ∆11-desaturase produced predominantly Z11-16:acyl, a common pheromone component precursor, from the abundant yeast palmitic acid and the FAR transformed a series of saturated and unsaturated fatty acids into their corresponding alcohols which may serve as pheromone components in many moth species. Secondly, when we co-expressed the genes in the Brewer’s yeast Saccharomyces cerevisiae, a set of long-chain fatty acids and alcohols that are not naturally occurring in yeast were produced from inherent yeast fatty acids, and the presence of (Z)-11-hexadecenol (Z11-16:OH), demonstrated that both heterologous enzymes were active in concert. A 100 ml batch yeast culture produced on average 19.5 μg Z11-16:OH. Finally, we demonstrated that oxidized extracts from the yeast cells containing (Z)-11-hexadecenal and other aldehyde pheromone compounds elicited specific electrophysiological activity from male antennae of the Tobacco budworm, Heliothis virescens, supporting the idea that

  7. Reconstruction of Gene Networks of Iron Response in Shewanella oneidensis

    SciTech Connect

    Yang, Yunfeng; Harris, Daniel P; Luo, Feng; Joachimiak, Marcin; Wu, Liyou; Dehal, Paramvir; Jacobsen, Janet; Yang, Zamin Koo; Gao, Haichun; Arkin, Adam; Palumbo, Anthony Vito; Zhou, Jizhong

    2009-01-01

    It is of great interest to study the iron response of the -proteobacterium Shewanella oneidensis since it possesses a high content of iron and is capable of utilizing iron for anaerobic respiration. We report here that the iron response in S. oneidensis is a rapid process. To gain more insights into the bacterial response to iron, temporal gene expression profiles were examined for iron depletion and repletion, resulting in identification of iron-responsive biological pathways in a gene co-expression network. Iron acquisition systems, including genes unique to S. oneidensis, were rapidly and strongly induced by iron depletion, and repressed by iron repletion. Some were required for iron depletion, as exemplified by the mutational analysis of the putative siderophore biosynthesis protein SO3032. Unexpectedly, a number of genes related to anaerobic energy metabolism were repressed by iron depletion and induced by repletion, which might be due to the iron storage potential of their protein products. Other iron-responsive biological pathways include protein degradation, aerobic energy metabolism and protein synthesis. Furthermore, sequence motifs enriched in gene clusters as well as their corresponding DNA-binding proteins (Fur, CRP and RpoH) were identified, resulting in a regulatory network of iron response in S. oneidensis. Together, this work provides an overview of iron response and reveals novel features in S. oneidensis, including Shewanella-specific iron acquisition systems, and suggests the intimate relationship between anaerobic energy metabolism and iron response.

  8. Gene networks controlling petal organogenesis.

    PubMed

    Huang, Tengbo; Irish, Vivian F

    2016-01-01

    One of the biggest unanswered questions in developmental biology is how growth is controlled. Petals are an excellent organ system for investigating growth control in plants: petals are dispensable, have a simple structure, and are largely refractory to environmental perturbations that can alter their size and shape. In recent studies, a number of genes controlling petal growth have been identified. The overall picture of how such genes function in petal organogenesis is beginning to be elucidated. This review will focus on studies using petals as a model system to explore the underlying gene networks that control organ initiation, growth, and final organ morphology.

  9. Buffering in cyclic gene networks

    NASA Astrophysics Data System (ADS)

    Glyzin, S. D.; Kolesov, A. Yu.; Rozov, N. Kh.

    2016-06-01

    We consider cyclic chains of unidirectionally coupled delay differential-difference equations that are mathematical models of artificial oscillating gene networks. We establish that the buffering phenomenon is realized in these system for an appropriate choice of the parameters: any given finite number of stable periodic motions of a special type, the so-called traveling waves, coexist.

  10. HEN1 and HEN2: a subgroup of basic helix-loop-helix genes that are coexpressed in a human neuroblastoma.

    PubMed Central

    Brown, L; Espinosa, R; Le Beau, M M; Siciliano, M J; Baer, R

    1992-01-01

    An important family of regulatory molecules is made up of proteins that possess the DNA-binding and dimerization motif known as the basic helix-loop-helix (bHLH) domain. The bHLH family includes subgroups of closely related proteins that share common functional properties and overlapping patterns of expression (e.g., the MyoD1 and achaete-scute subgroups). In this report we describe HEN1 and HEN2, mammalian genes that encode a distinct subgroup of bHLH proteins. The HEN1 gene was identified on the basis of cross-hybridization with TAL1, a known bHLH gene implicated in T-cell acute lymphoblastic leukemia. In situ fluorescence hybridization was used to localize the human HEN1 gene to chromosome band 1q22. HEN1 and HEN2 are coexpressed in the IMR-32 human neuroblastoma cell line, and they encode highly related proteins of 133 and 135 residues, respectively, that share 98% amino acid identity in their hHLH domains. These data imply that the bHLH protein subgroup encoded by HEN1 and HEN2 may serve important regulatory functions in the developing nervous system. Images PMID:1528853

  11. Co-expression of Achaete-Scute Homologue-1 and Calcitonin Gene-Related Peptide during NNK-Induced Pulmonary Neuroendocrine Hyperplasia and Carcinogenesis in Hamsters

    PubMed Central

    Naizhen, Xu; Linnoila, R. Ilona; Kimura, Shioko

    2016-01-01

    Achaete-scute homologue-1 or ASCL1 (MASH1, hASH1) plays roles in neural development and pulmonary neuroendocrine (NE) differentiation, and it is expressed in certain lung cancers. This study was aimed to assess whether and/or how ASCL1 plays a role in 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)-induced pulmonary NE hyperplasia and carcinogenesis in hamsters. Hamsters were injected 3 times weekly with either NNK or solvent alone (control) for treatment periods of 6 and 24 weeks, both without and with 6-week recovery. Immunohistochemical analysis was carried out to examine the expressions of ASCL1, CGRP (calcitonin gene-related peptide), secretoglobin SCGB1A1 (club [Clara] cell specific 10 kD protein, CC10, CCSP), synaptophysin (SYP), and PCNA (proliferating cell nuclear antigen). The number of ASCL1-expressing NE foci per airway increased from 0.8 in controls to 1.6 and 2.0 during NNK exposure for 6 and 24 weeks, respectively, and the number of cells per foci doubled after NNK exposure. Most ASCL1-expressing cells in NEBs (neuroepithelial bodies) were also CGRP immunoreactive; NNK enhanced this co-expression with CGRP, a NE marker with known proliferation-promoting properties. NNK also increased PCNA expression within NE foci. NNK-induced tumors showed no immunoreactivity for NE markers. This study confirms ASCL1 as an excellent marker for pulmonary NE cells and demonstrates CGRP co-expression in ASCL1-positive NEB cells participating in NNK-induced NE hyperplasia. PMID:27877229

  12. Plant Evolution: Evolving Antagonistic Gene Regulatory Networks.

    PubMed

    Cooper, Endymion D

    2016-06-20

    Developing a structurally complex phenotype requires a complex regulatory network. A new study shows how gene duplication provides a potential source of antagonistic interactions, an important component of gene regulatory networks.

  13. Co-Expression Analysis of Blood Cell Genome Expression to Preliminary Investigation of Regulatory Mechanisms in Uremia

    PubMed Central

    Cheng, Liu; Yonggui, Wu

    2017-01-01

    Background Uremia involves a series of clinical manifestations and is a common syndrome that occurs in nearly all end-stage kidney diseases. However, the exact genetic and/or molecular mechanisms that underlie uremia remain poorly understood. Material/Methods In this case-control study, we analyzed whole-genome microarray of 75 uremia patients and 20 healthy controls to investigate changes in gene expression and cellular mechanisms relevant to uremia. Gene co-expression network analysis was performed to construct co-expression networks using differentially expressed genes (DEGs) in uremia. We then determined hub models of co-expressed gene networks by MCODE, and we used miRNA enrichment analysis to detect key miRNAs in each hub module. Results We found nine co-expressed hub modules implicated in uremia. These modules were enriched in specific biological functions, including “proteolysis”, “membrane-enclosed lumen”, and “apoptosis”. Finally, miRNA enrichment analysis to detect key miRNAs in each hub module found 15 miRNAs that were specifically targeted to uremia-related hub modules. Of these, miRNA-21-3p and miRNA-210-3p have been identified in other studies as being important for uremia. Conclusions In summary, our study connected biological functions, genes, and miRNAs that underpin the network modules that can be used to elucidate the molecular mechanisms involved in uremia. PMID:28050009

  14. Co-expressed miRNAs in gastric adenocarcinoma.

    PubMed

    Yepes, Sally; López, Rocío; Andrade, Rafael E; Rodriguez-Urrego, Paula A; López-Kleine, Liliana; Torres, Maria Mercedes

    2016-08-01

    Co-expression networks may provide insights into the patterns of molecular interactions that underlie cellular processes. To obtain a better understanding of miRNA expression patterns in gastric adenocarcinoma and to provide markers that can be associated with histopathological findings, we performed weighted gene correlation network analysis (WGCNA) and compare it with a supervised analysis. Integrative analysis of target predictions and miRNA expression profiles in gastric cancer samples was also performed. WGCNA identified a module of co-expressed miRNAs that were associated with histological traits and tumor condition. Hub genes were identified based on statistical analysis and network centrality. The miRNAs 100, let-7c, 125b and 99a stood out for their association with the diffuse histological subtype. The 181 miRNA family and miRNA 21 highlighted for their association with the tumoral phenotype. The integrated analysis of miRNA and gene expression profiles showed the let-7 miRNA family playing a central role in the regulatory relationships.

  15. Uncovering co-expression gene network regulating fruit acidity in diverse apples

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that encodes an Aluminum-activated Malate Transporter1 (...

  16. Dynamical properties of gene regulatory networks involved in long-term potentiation

    PubMed Central

    Nido, Gonzalo S.; Ryan, Margaret M.; Benuskova, Lubica; Williams, Joanna M.

    2015-01-01

    The long-lasting enhancement of synaptic effectiveness known as long-term potentiation (LTP) is considered to be the cellular basis of long-term memory. LTP elicits changes at the cellular and molecular level, including temporally specific alterations in gene networks. LTP can be seen as a biological process in which a transient signal sets a new homeostatic state that is “remembered” by cellular regulatory systems. Previously, we have shown that early growth response (Egr) transcription factors are of fundamental importance to gene networks recruited early after LTP induction. From a systems perspective, we hypothesized that these networks will show less stable architecture, while networks recruited later will exhibit increased stability, being more directly related to LTP consolidation. Using random Boolean network (RBN) simulations we found that the network derived at 24 h was markedly more stable than those derived at 20 min or 5 h post-LTP. This temporal effect on the vulnerability of the networks is mirrored by what is known about the vulnerability of LTP and memory itself. Differential gene co-expression analysis further highlighted the importance of the Egr family and found a rapid enrichment in connectivity at 20 min, followed by a systematic decrease, providing a potential explanation for the down-regulation of gene expression at 24 h documented in our preceding studies. We also found that the architecture exhibited by a control and the 24 h LTP co-expression networks fit well to a scale-free distribution, known to be robust against perturbations. By contrast the 20 min and 5 h networks showed more truncated distributions. These results suggest that a new homeostatic state is achieved 24 h post-LTP. Together, these data present an integrated view of the genomic response following LTP induction by which the stability of the networks regulated at different times parallel the properties observed at the synapse. PMID:26300724

  17. Co-expression of the transcription factors CEH-14 and TTX-1 regulates AFD neuron-specific genes gcy-8 and gcy-18 in C. elegans.

    PubMed

    Kagoshima, Hiroshi; Kohara, Yuji

    2015-03-15

    A wide variety of cells are generated by the expression of characteristic sets of genes, primarily those regulated by cell-specific transcription. To elucidate the mechanism regulating cell-specific gene expression in a highly specialized cell, AFD thermosensory neuron in Caenorhabditis elegans, we analyzed the promoter sequences of guanylyl cyclase genes, gcy-8 and gcy-18, exclusively expressed in AFD. In this study, we showed that AFD-specific expression of gcy-8 and gcy-18 requires the co-expression of homeodomain proteins, CEH-14/LHX3 and TTX-1/OTX1. We observed that mutation of ttx-1 or ceh-14 caused a reduction in the expression of gcy-8 and gcy-18 and that the expression was completely lost in double mutants. This synergy effect was also observed with other AFD marker genes, such as ntc-1, nlp-21and cng-3. Electrophoretic mobility shift assays revealed direct interaction of CEH-14 and TTX-1 proteins with gcy-8 and gcy-18 promoters in vitro. The binding sites of CEH-14 and TTX-1 proteins were confirmed to be essential for AFD-specific expression of gcy-8 and gcy-18 in vivo. We also demonstrated that forced expression of CEH-14 and TTX-1 in AWB chemosensory neurons induced ectopic expression of gcy-8 and gcy-18 reporters in this neuron. Finally, we showed that the regulation of gcy-8 and gcy-18 expression by ceh-14 and ttx-1 is evolutionally conserved in five Caenorhabditis species. Taken together, ceh-14 and ttx-1 expression determines the fate of AFD as terminal selector genes at the final step of cell specification.

  18. A new long form of Sox5 (L-Sox5), Sox6 and Sox9 are coexpressed in chondrogenesis and cooperatively activate the type II collagen gene.

    PubMed Central

    Lefebvre, V; Li, P; de Crombrugghe, B

    1998-01-01

    Transcripts for a new form of Sox5, called L-Sox5, and Sox6 are coexpressed with Sox9 in all chondrogenic sites of mouse embryos. A coiled-coil domain located in the N-terminal part of L-Sox5, and absent in Sox5, showed >90% identity with a similar domain in Sox6 and mediated homodimerization and heterodimerization with Sox6. Dimerization of L-Sox5/Sox6 greatly increased efficiency of binding of the two Sox proteins to DNA containing adjacent HMG sites. L-Sox5, Sox6 and Sox9 cooperatively activated expression of the chondrocyte differentiation marker Col2a1 in 10T1/2 and MC615 cells. A 48 bp chondrocyte-specific enhancer in this gene, which contains several HMG-like sites that are necessary for enhancer activity, bound the three Sox proteins and was cooperatively activated by the three Sox proteins in non-chondrogenic cells. Our data suggest that L-Sox5/Sox6 and Sox9, which belong to two different classes of Sox transcription factors, cooperate with each other in expression of Col2a1 and possibly other genes of the chondrocytic program. PMID:9755172

  19. Gene regulation is governed by a core network in hepatocellular carcinoma

    PubMed Central

    2012-01-01

    Background Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, and the mechanisms that lead to the disease are still relatively unclear. However, with the development of high-throughput technologies it is possible to gain a systematic view of biological systems to enhance the understanding of the roles of genes associated with HCC. Thus, analysis of the mechanism of molecule interactions in the context of gene regulatory networks can reveal specific sub-networks that lead to the development of HCC. Results In this study, we aimed to identify the most important gene regulations that are dysfunctional in HCC generation. Our method for constructing gene regulatory network is based on predicted target interactions, experimentally-supported interactions, and co-expression model. Regulators in the network included both transcription factors and microRNAs to provide a complete view of gene regulation. Analysis of gene regulatory network revealed that gene regulation in HCC is highly modular, in which different sets of regulators take charge of specific biological processes. We found that microRNAs mainly control biological functions related to mitochondria and oxidative reduction, while transcription factors control immune responses, extracellular activity and the cell cycle. On the higher level of gene regulation, there exists a core network that organizes regulations between different modules and maintains the robustness of the whole network. There is direct experimental evidence for most of the regulators in the core gene regulatory network relating to HCC. We infer it is the central controller of gene regulation. Finally, we explored the influence of the core gene regulatory network on biological pathways. Conclusions Our analysis provides insights into the mechanism of transcriptional and post-transcriptional control in HCC. In particular, we highlight the importance of the core gene regulatory network; we propose that it is highly related to

  20. Co-expression of the mating-type genes involved in internuclear recognition is lethal in Podospora anserina.

    PubMed Central

    Coppin, E; Debuchy, R

    2000-01-01

    In the heterothallic filamentous fungus Podospora anserina, four mating-type genes encoding transcriptional factors have been characterized: FPR1 in the mat+ sequence and FMR1, SMR1, and SMR2 in the alternative mat- sequence. Fertilization is controlled by FPR1 and FMR1. After fertilization, male and female nuclei, which have divided in the same cell, form mat+/mat- pairs during migration into the ascogenous hyphae. Previous data indicate that the formation of mat+/mat- pairs is controlled by FPR1, FMR1, and SMR2. SMR1 was postulated to be necessary for initial development of ascogenous hyphae. In this study, we investigated the transcriptional control of the mat genes by seeking mat transcripts during the vegetative and sexual phase and fusing their promoter to a reporter gene. The data indicate that FMR1 and FPR1 are expressed in both mycelia and perithecia, whereas SMR1 and SMR2 are transcribed in perithecia. Increased or induced vegetative expression of the four mat genes has no effect when the recombined gene is solely in the wild-type strain. However, the combination of resident FPR1 with deregulated SMR2 and overexpressed FMR1 in the same nucleus is lethal. This lethality is suppressed by the expression of SMR1, confirming that SMR1 operates downstream of the other mat genes. PMID:10835389

  1. Integration of molecular network data reconstructs Gene Ontology

    PubMed Central

    Gligorijević, Vladimir; Janjić, Vuk; Pržulj, Nataša

    2014-01-01

    Motivation: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data. Results: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers’ yeasts protein–protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling. Availability and implementation: Supplementary Tables of new GO

  2. Network-based integration of GWAS and gene expression identifies a HOX-centric network associated with serous ovarian cancer risk

    PubMed Central

    Kar, Siddhartha P.; Tyrer, Jonathan P.; Li, Qiyuan; Lawrenson, Kate; Aben, Katja K.H.; Anton-Culver, Hoda; Antonenkova, Natalia; Chenevix-Trench, Georgia; Baker, Helen; Bandera, Elisa V.; Bean, Yukie T.; Beckmann, Matthias W.; Berchuck, Andrew; Bisogna, Maria; Bjørge, Line; Bogdanova, Natalia; Brinton, Louise; Brooks-Wilson, Angela; Butzow, Ralf; Campbell, Ian; Carty, Karen; Chang-Claude, Jenny; Chen, Yian Ann; Chen, Zhihua; Cook, Linda S.; Cramer, Daniel; Cunningham, Julie M.; Cybulski, Cezary; Dansonka-Mieszkowska, Agnieszka; Dennis, Joe; Dicks, Ed; Doherty, Jennifer A.; Dörk, Thilo; du Bois, Andreas; Dürst, Matthias; Eccles, Diana; Easton, Douglas F.; Edwards, Robert P.; Ekici, Arif B.; Fasching, Peter A.; Fridley, Brooke L.; Gao, Yu-Tang; Gentry-Maharaj, Aleksandra; Giles, Graham G.; Glasspool, Rosalind; Goode, Ellen L.; Goodman, Marc T.; Grownwald, Jacek; Harrington, Patricia; Harter, Philipp; Hein, Alexander; Heitz, Florian; Hildebrandt, Michelle A.T.; Hillemanns, Peter; Hogdall, Estrid; Hogdall, Claus K.; Hosono, Satoyo; Iversen, Edwin S.; Jakubowska, Anna; Paul, James; Jensen, Allan; Ji, Bu-Tian; Karlan, Beth Y; Kjaer, Susanne K.; Kelemen, Linda E.; Kellar, Melissa; Kelley, Joseph; Kiemeney, Lambertus A.; Krakstad, Camilla; Kupryjanczyk, Jolanta; Lambrechts, Diether; Lambrechts, Sandrina; Le, Nhu D.; Lee, Alice W.; Lele, Shashi; Leminen, Arto; Lester, Jenny; Levine, Douglas A.; Liang, Dong; Lissowska, Jolanta; Lu, Karen; Lubinski, Jan; Lundvall, Lene; Massuger, Leon; Matsuo, Keitaro; McGuire, Valerie; McLaughlin, John R.; McNeish, Iain A.; Menon, Usha; Modugno, Francesmary; Moysich, Kirsten B.; Narod, Steven A.; Nedergaard, Lotte; Ness, Roberta B.; Nevanlinna, Heli; Odunsi, Kunle; Olson, Sara H.; Orlow, Irene; Orsulic, Sandra; Weber, Rachel Palmieri; Pearce, Celeste Leigh; Pejovic, Tanja; Pelttari, Liisa M.; Permuth-Wey, Jennifer; Phelan, Catherine M.; Pike, Malcolm C.; Poole, Elizabeth M.; Ramus, Susan J.; Risch, Harvey A.; Rosen, Barry; Rossing, Mary Anne; Rothstein, Joseph H.; Rudolph, Anja; Runnebaum, Ingo B.; Rzepecka, Iwona K.; Salvesen, Helga B.; Schildkraut, Joellen M.; Schwaab, Ira; Shu, Xiao-Ou; Shvetsov, Yurii B; Siddiqui, Nadeem; Sieh, Weiva; Song, Honglin; Southey, Melissa C.; Sucheston-Campbell, Lara E.; Tangen, Ingvild L.; Teo, Soo-Hwang; Terry, Kathryn L.; Thompson, Pamela J; Timorek, Agnieszka; Tsai, Ya-Yu; Tworoger, Shelley S.; van Altena, Anne M.; Van Nieuwenhuysen, Els; Vergote, Ignace; Vierkant, Robert A.; Wang-Gohrke, Shan; Walsh, Christine; Wentzensen, Nicolas; Whittemore, Alice S.; Wicklund, Kristine G.; Wilkens, Lynne R.; Woo, Yin-Ling; Wu, Xifeng; Wu, Anna; Yang, Hannah; Zheng, Wei; Ziogas, Argyrios; Sellers, Thomas A.; Monteiro, Alvaro N. A.; Freedman, Matthew L.; Gayther, Simon A.; Pharoah, Paul D. P.

    2015-01-01

    Background Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by co-expression may also be enriched for additional EOC risk associations. Methods We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly co-expressed with each selected TF gene in the unified microarray data set of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this data set were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls). Results Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P<0.05 and FDR<0.05). These results were replicated (P<0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. Conclusion We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. Impact Network analysis integrating large, context-specific data sets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization. PMID:26209509

  3. MYC/BCL2 protein coexpression contributes to the inferior survival of activated B-cell subtype of diffuse large B-cell lymphoma and demonstrates high-risk gene expression signatures: a report from The International DLBCL Rituximab-CHOP Consortium Program.

    PubMed

    Hu, Shimin; Xu-Monette, Zijun Y; Tzankov, Alexander; Green, Tina; Wu, Lin; Balasubramanyam, Aarthi; Liu, Wei-min; Visco, Carlo; Li, Yong; Miranda, Roberto N; Montes-Moreno, Santiago; Dybkaer, Karen; Chiu, April; Orazi, Attilio; Zu, Youli; Bhagat, Govind; Richards, Kristy L; Hsi, Eric D; Choi, William W L; Zhao, Xiaoying; van Krieken, J Han; Huang, Qin; Huh, Jooryung; Ai, Weiyun; Ponzoni, Maurilio; Ferreri, Andrés J M; Zhou, Fan; Slack, Graham W; Gascoyne, Randy D; Tu, Meifeng; Variakojis, Daina; Chen, Weina; Go, Ronald S; Piris, Miguel A; Møller, Michael B; Medeiros, L Jeffrey; Young, Ken H

    2013-05-16

    Diffuse large B-cell lymphoma (DLBCL) is stratified into prognostically favorable germinal center B-cell (GCB)-like and unfavorable activated B-cell (ABC)-like subtypes based on gene expression signatures. In this study, we analyzed 893 de novo DLBCL patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone). We show that MYC/BCL2 protein coexpression occurred significantly more commonly in the ABC subtype. Patients with the ABC or GCB subtype of DLBCL had similar prognoses with MYC/BCL2 coexpression and without MYC/BCL2 coexpression. Consistent with the notion that the prognostic difference between the 2 subtypes is attributable to MYC/BCL2 coexpression, there is no difference in gene expression signatures between the 2 subtypes in the absence of MYC/BCL2 coexpression. DLBCL with MYC/BCL2 coexpression demonstrated a signature of marked downregulation of genes encoding extracellular matrix proteins, those involving matrix deposition/remodeling and cell adhesion, and upregulation of proliferation-associated genes. We conclude that MYC/BCL2 coexpression in DLBCL is associated with an aggressive clinical course, is more common in the ABC subtype, and contributes to the overall inferior prognosis of patients with ABC-DLBCL. In conclusion, the data suggest that MYC/BCL2 coexpression, rather than cell-of-origin classification, is a better predictor of prognosis in patients with DLBCL treated with R-CHOP.

  4. Protein Co-Expression Analysis as a Strategy to Complement a Standard Quantitative Proteomics Approach: Case of a Glioblastoma Multiforme Study

    PubMed Central

    Deighton, Ruth F.

    2016-01-01

    Although correlation network studies from co-expression analysis are increasingly popular, they are rarely applied to proteomics datasets. Protein co-expression analysis provides a complementary view of underlying trends, which can be overlooked by conventional data analysis. The core of the present study is based on Weighted Gene Co-expression Network Analysis applied to a glioblastoma multiforme proteomic dataset. Using this method, we have identified three main modules which are associated with three different membrane associated groups; mitochondrial, endoplasmic reticulum, and a vesicle fraction. The three networks based on protein co-expression were assessed against a publicly available database (STRING) and show a statistically significant overlap. Each of the three main modules were de-clustered into smaller networks using different strategies based on the identification of highly connected networks, hierarchical clustering and enrichment of Gene Ontology functional terms. Most of the highly connected proteins found in the endoplasmic reticulum module were associated with redox activity while a core of the unfolded protein response was identified in addition to proteins involved in oxidative stress pathways. The proteins composing the electron transfer chain were found differently affected with proteins from mitochondrial Complex I being more down-regulated than proteins from Complex III. Finally, the two pyruvate kinases isoforms show major differences in their co-expressed protein networks suggesting roles in different cellular locations. PMID:27571357

  5. Causal co-expression method with module analysis to screen drugs with specific target.

    PubMed

    Yu, Shuhao; Zheng, Lulu; Li, Yixue; Li, Chunyan; Ma, Chenchen; Yu, Yang; Li, Xuan; Hao, Pei

    2013-04-10

    The considerable increase of investment in research and development by the pharmaceutical industry over the past three decades has not added the number of approved new drugs. An important issue ignored by drug discovery practice is the multi-dimensional interaction network between drugs and their targets. Thus, it is essential to view drug actions through the lens of network biology. In the current study, based on the co-expression network of transcription factors and their downstream genes, we proposed a novel approach, called causal co-expression method with module analysis, to screen drugs with specific target and fewer side effects. We presented a causal co-expression method with module analysis and it could be used in analyzing the microarray data of different drug candidates. At first, the differential wiring value (DW) was calculated to find some causal transcription factors (TFs) by combining with differential expression genes in the regulated networks. After the discovery of the causal TFs, co-expression module analysis method was applied to mine molecular pharmacology pathways around these causal TFs at molecular level. We applied our methods to two drug candidates, Argyrin A and Bortezomib, both with anti-cancer activities. We first obtained some differentially expressed transcription factors of cells treated with Argyrin A or Bortezomib. Nearly all these transcription factors are associated with the tumor suppressor protein p27kip1. Furthermore, module analysis showed that Bortezomib inhibited tumor growth not specifically by cell cycle and cell proliferation pathway, but through many basic metabolic processes which result in cell toxicity. In contrast, Argyrin A had influence on cell cycle, and was involved in DNA damage repair at the same time, showing that Argyrin A was a more suitable drug for anti-cancer treatment. Our study revealed that the causal co-expression method with module analysis was effective and can be used as a tool to evaluate drug

  6. Efficient Production of Hydroxylated Human-Like Collagen Via the Co-Expression of Three Key Genes in Escherichia coli Origami (DE3).

    PubMed

    Tang, Yunping; Yang, Xiuliang; Hang, Baojian; Li, Jiangtao; Huang, Lei; Huang, Feng; Xu, Zhinan

    2016-04-01

    Mature collagen is abundant in human bodies and very valuable for a range of industrial and medical applications. The biosynthesis of mature collagen requires post-translational modifications to increase the stability of collagen triple helix structure. By co-expressing the human-like collagen (HLC) gene with human prolyl 4-hydroxylase (P4H) and D-arabinono-1, 4-lactone oxidase (ALO) in Escherichia coli, we have constructed a prokaryotic expression system to produce the hydroxylated HLC. Then, five different media, as well as the induction conditions were investigated with regard to the soluble expression of such protein. The results indicated that the highest soluble expression level of target HLC obtained in shaking flasks was 49.55 ± 0.36 mg/L, when recombinant cells were grew in MBL medium and induced by 0.1 mM IPTG at the middle stage of exponential growth phase. By adopting the glucose feeding strategy, the expression level of target HLC can be improved up to 260 mg/L in a 10 L bench-top fermentor. Further, HPLC analyses revealed that more than 10 % of proline residues in purified HLC were successfully hydroxylated. The present work has provided a solid base for the large-scale production of hydroxylated HLC in E. coli.

  7. Co-Expression and Co-Localization of Cartilage Glycoproteins CHI3L1 and Lubricin in Osteoarthritic Cartilage: Morphological, Immunohistochemical and Gene Expression Profiles

    PubMed Central

    Szychlinska, Marta Anna; Trovato, Francesca Maria; Di Rosa, Michelino; Malaguarnera, Lucia; Puzzo, Lidia; Leonardi, Rosy; Castrogiovanni, Paola; Musumeci, Giuseppe

    2016-01-01

    Osteoarthritis is the most common human arthritis characterized by degeneration of articular cartilage. Several studies reported that levels of human cartilage glycoprotein chitinase 3-like-1 (CHI3L1) are known as a potential marker for the activation of chondrocytes and the progression of Osteoarthritis (OA), whereas lubricin appears to be chondroprotective. The aim of this study was to investigate the co-expression and co-localization of CHI3L1 and lubricin in normal and osteoarthritic rat articular cartilage to correlate their modified expression to a specific grade of OA. Samples of normal and osteoarthritic rat articular cartilage were analyzed by the Kellgren–Lawrence OA severity scores, the Kraus’ modified Mankin score and the Histopathology Osteoarthritis Research Society International (OARSI) system for histomorphometric evaluations, and through CHI3L1 and lubricin gene expression, immunohistochemistry and double immuno-staining analysis. The immunoexpression and the mRNA levels of lubricin increased in normal cartilage and decreased in OA cartilage (normal vs. OA, p < 0.01). By contrast, the immunoexpression and the mRNA levels of CHI3L1 increased in OA cartilage and decreased in normal cartilage (normal vs. OA, p < 0.01). Our findings are consistent with reports suggesting that these two glycoproteins are functionally associated with the development of OA and in particular with grade 2/3 of OA, suggesting that in the future they could be helpful to stage the severity and progression of the disease. PMID:26978347

  8. Induction of cotton ovule culture fibre branching by co-expression of cotton BTL, cotton SIM, and Arabidopsis STI genes.

    PubMed

    Wang, Gaskin; Feng, Hongjie; Sun, Junling; Du, Xiongming

    2013-11-01

    The highly elongated single-celled cotton fibre consists of lint and fuzz, similar to the Arabidopsis trichome. Endoreduplication is an important determinant in Arabidopsis trichome initiation and morphogenesis. Fibre development is also controlled by functional homologues of Arabidopsis trichome patterning genes, although fibre cells do not have a branched shape like trichomes. The identification and characterization of the homologues of 10 key Arabidopsis trichome branching genes in Gossypium arboreum are reported here. Nuclear ploidy of fibres was determined, and gene function in cotton callus and fibre cells was investigated. The results revealed that the nuclear DNA content was constant in fuzz, whereas a limited and reversible change occurred in lint after initiation. Gossypeum arboreum branchless trichomes (GaBLT) was not transcribed in fibres. The homologue of STICHEL (STI), which is essential for trichome branching, was a pseudogene in Gossypium. Targeted expression of GaBLT, Arabidopsis STI, and the cytokinesis-repressing GaSIAMESE in G. hirsutum fibre cells cultured in vitro resulted in branching. The findings suggest that the distinctive developmental mechanism of cotton fibres does not depend on endoreduplication. This important component may be a relic function that can be activated in fibre cells.

  9. GEM2Net: from gene expression modeling to -omics networks, a new CATdb module to investigate Arabidopsis thaliana genes involved in stress response.

    PubMed

    Zaag, Rim; Tamby, Jean Philippe; Guichard, Cécile; Tariq, Zakia; Rigaill, Guillem; Delannoy, Etienne; Renou, Jean-Pierre; Balzergue, Sandrine; Mary-Huard, Tristan; Aubourg, Sébastien; Martin-Magniette, Marie-Laure; Brunaud, Véronique

    2015-01-01

    CATdb (http://urgv.evry.inra.fr/CATdb) is a database providing a public access to a large collection of transcriptomic data, mainly for Arabidopsis but also for other plants. This resource has the rare advantage to contain several thousands of microarray experiments obtained with the same technical protocol and analyzed by the same statistical pipelines. In this paper, we present GEM2Net, a new module of CATdb that takes advantage of this homogeneous dataset to mine co-expression units and decipher Arabidopsis gene functions. GEM2Net explores 387 stress conditions organized into 18 biotic and abiotic stress categories. For each one, a model-based clustering is applied on expression differences to identify clusters of co-expressed genes. To characterize functions associated with these clusters, various resources are analyzed and integrated: Gene Ontology, subcellular localization of proteins, Hormone Families, Transcription Factor Families and a refined stress-related gene list associated to publications. Exploiting protein-protein interactions and transcription factors-targets interactions enables to display gene networks. GEM2Net presents the analysis of the 18 stress categories, in which 17,264 genes are involved and organized within 681 co-expression clusters. The meta-data analyses were stored and organized to compose a dynamic Web resource.

  10. Recombinant fowlpox viruses coexpressing chicken type I IFN and Newcastle disease virus HN and F genes: influence of IFN on protective efficacy and humoral responses of chickens following in ovo or post-hatch administration of recombinant viruses.

    PubMed

    Karaca, K; Sharma, J M; Winslow, B J; Junker, D E; Reddy, S; Cochran, M; McMillen, J

    1998-10-01

    We have constructed recombinant (r) fowl pox viruses (FPVs) coexpressing chicken type I interferon (IFN) and/or hemagglutinin-neuraminidase (HN) and fusion (F) proteins of Newcastle disease virus (NDV). We administered rFPVs and FPV into embryonated chicken eggs at 17 days of embryonation or in chickens after hatch. Administration of FPV or rFPVs did not influence hatchability and survival of hatched chicks. In ovo or after hatch vaccination of chickens with the recombinant viruses resulted in protection against challenge with virulent FPV and NDV. Chickens vaccinated with FPV or FPV-NDV recombinant had significantly lower body weight 2 weeks following vaccination. This loss in body weight was not detected in chickens receiving FPV-IFN and FPV-NDV-IFN recombinants. Chickens vaccinated with FPV coexpressing IFN and NDV genes produced less antibodies against NDV in comparison with chickens vaccinated with FPV expressing NDV genes.

  11. A regulatory gene network related to the porcine umami taste receptor (TAS1R1/TAS1R3).

    PubMed

    Kim, J M; Ren, D; Reverter, A; Roura, E

    2016-02-01

    Taste perception plays an important role in the mediation of food choices in mammals. The first porcine taste receptor genes identified, sequenced and characterized, TAS1R1 and TAS1R3, were related to the dimeric receptor for umami taste. However, little is known about their regulatory network. The objective of this study was to unfold the genetic network involved in porcine umami taste perception. We performed a meta-analysis of 20 gene expression studies spanning 480 porcine microarray chips and screened 328 taste-related genes by selective mining steps among the available 12,320 genes. A porcine umami taste-specific regulatory network was constructed based on the normalized coexpression data of the 328 genes across 27 tissues. From the network, we revealed the 'taste module' and identified a coexpression cluster for the umami taste according to the first connector with the TAS1R1/TAS1R3 genes. Our findings identify several taste-related regulatory genes and extend previous genetic background of porcine umami taste.

  12. Protective immunization of horses with a recombinant canarypox virus vectored vaccine co-expressing genes encoding the outer capsid proteins of African horse sickness virus.

    PubMed

    Guthrie, Alan J; Quan, Melvyn; Lourens, Carina W; Audonnet, Jean-Christophe; Minke, Jules M; Yao, Jiansheng; He, Ling; Nordgren, Robert; Gardner, Ian A; Maclachlan, N James

    2009-07-16

    We describe the development and preliminary characterization of a recombinant canarypox virus vectored (ALVAC) vaccine for protective immunization of equids against African horse sickness virus (AHSV) infection. Horses (n=8) immunized with either of two concentrations of recombinant canarypox virus vector (ALVAC-AHSV) co-expressing synthetic genes encoding the outer capsid proteins (VP2 and VP5) of AHSV serotype 4 (AHSV-4) developed variable titres (<10-80) of virus-specific neutralizing antibodies and were completely resistant to challenge infection with a virulent strain of AHSV-4. In contrast, a horse immunized with a commercial recombinant canarypox virus vectored vaccine expressing the haemagglutinin genes of two equine influenza H3N8 viruses was seronegative to AHSV and following infection with virulent AHSV-4 developed pyrexia, thrombocytopenia and marked oedema of the supraorbital fossae typical of the "dikkop" or cardiac form of African horse sickness. AHSV was detected by virus isolation and quantitative reverse transcriptase polymerase chain reaction in the blood of the control horse from 8 days onwards after challenge infection whereas AHSV was not detected at any time in the blood of the ALVAC-AHSV vaccinated horses. The control horse seroconverted to AHSV by 2 weeks after challenge infection as determined by both virus neutralization and ELISA assays, whereas six of eight of the ALVAC-AHSV vaccinated horses did not seroconvert by either assay following challenge infection with virulent AHSV-4. These data confirm that the ALVAC-AHSV vaccine will be useful for the protective immunization of equids against African horse sickness, and avoids many of the problems inherent to live-attenuated AHSV vaccines.

  13. Evolving Robust Gene Regulatory Networks

    PubMed Central

    Noman, Nasimul; Monjo, Taku; Moscato, Pablo; Iba, Hitoshi

    2015-01-01

    Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of ‘parts’ and ‘devices’. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems. PMID:25616055

  14. Linking Hematopoietic Differentiation to Co-Expressed Sets of Pluripotency-Associated and Imprinted Genes and to Regulatory microRNA-Transcription Factor Motifs

    PubMed Central

    Hamed, Mohamed; Trumm, Johannes; Spaniol, Christian; Sethi, Riccha; Irhimeh, Mohammad R.; Fuellen, Georg; Paulsen, Martina

    2017-01-01

    Maintenance of cell pluripotency, differentiation, and reprogramming are regulated by complex gene regulatory networks (GRNs) including monoallelically-expressed imprinted genes. Besides transcriptional control, epigenetic modifications and microRNAs contribute to cellular differentiation. As a model system for studying the capacity of cells to preserve their pluripotency state and the onset of differentiation and subsequent specialization, murine hematopoiesis was used and compared to embryonic stem cells (ESCs) as a control. Using published microarray data, the expression profiles of two sets of genes, pluripotent and imprinted, were compared to a third set of known hematopoietic genes. We found that more than half of the pluripotent and imprinted genes are clearly upregulated in ESCs but subsequently repressed during hematopoiesis. The remaining genes were either upregulated in hematopoietic progenitors or in differentiated blood cells. The three gene sets each consist of three similarly behaving gene groups with similar expression profiles in various lineages of the hematopoietic system as well as in ESCs. To explain this co-regulation behavior, we explored the transcriptional and post-transcriptional mechanisms of pluripotent and imprinted genes and their regulator/target miRNAs in six different hematopoietic lineages. Therewith, lineage-specific transcription factor (TF)-miRNA regulatory networks were generated and their topologies and functional impacts during hematopoiesis were analyzed. This led to the identification of TF-miRNA co-regulatory motifs, for which we validated the contribution to the cellular development of the corresponding lineage in terms of statistical significance and relevance to biological evidence. This analysis also identified key miRNAs and TFs/genes that might play important roles in the derived lineage networks. These molecular associations suggest new aspects of the cellular regulation of the onset of cellular differentiation and

  15. Evolutionary transfers of mitochondrial genes to the nucleus in the Populus lineage and coexpression of nuclear and mitochondrial Sdh4 genes.

    PubMed

    Choi, Catherine; Liu, Zhenlan; Adams, Keith L

    2006-01-01

    The transfer of mitochondrial genes to the nucleus is an ongoing evolutionary process in flowering plants. Evolutionarily recent gene transfers provide insights into the evolutionary dynamics of the process and the way in which transferred genes become functional in the nucleus. Genes that are present in the mitochondrion of some angiosperms but have been transferred to the nucleus in the Populus lineage were identified by searches of Populus sequence databases. Sequence analyses and expression experiments were used to characterize the transferred genes. Two succinate dehydrogenase genes and six mitochondrial ribosomal protein genes have been transferred to the nucleus in the Populus lineage and have become expressed. Three transferred genes have gained an N-terminal mitochondrial targeting presequence from other pre-existing genes and two of the transferred genes do not contain an N-terminal targeting presequence. Intact copies of the succinate dehydrogenase gene Sdh4 are present in both the mitochondrion and the nucleus. Both copies of Sdh4 are expressed in multiple organs of two Populus species and RNA editing occurs in the mitochondrial copy. These results provide a genome-wide perspective on mitochondrial genes that were transferred to the nucleus and became expressed, functional genes during the evolutionary history of Populus.

  16. Modeling of hysteresis in gene regulatory networks.

    PubMed

    Hu, J; Qin, K R; Xiang, C; Lee, T H

    2012-08-01

    Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.

  17. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    PubMed

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  18. A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders.

    PubMed

    Jiang, Peng; Scarpa, Joseph R; Fitzpatrick, Karrie; Losic, Bojan; Gao, Vance D; Hao, Ke; Summa, Keith C; Yang, He S; Zhang, Bin; Allada, Ravi; Vitaterna, Martha H; Turek, Fred W; Kasarskis, Andrew

    2015-05-05

    Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders.

  19. Intracompartmental and intercompartmental transcriptional networks coordinate the expression of genes for organellar functions.

    PubMed

    Leister, Dario; Wang, Xi; Haberer, Georg; Mayer, Klaus F X; Kleine, Tatjana

    2011-09-01

    Genes for mitochondrial and chloroplast proteins are distributed between the nuclear and organellar genomes. Organelle biogenesis and metabolism, therefore, require appropriate coordination of gene expression in the different compartments to ensure efficient synthesis of essential multiprotein complexes of mixed genetic origin. Whereas organelle-to-nucleus signaling influences nuclear gene expression at the transcriptional level, organellar gene expression (OGE) is thought to be primarily regulated posttranscriptionally. Here, we show that intracompartmental and intercompartmental transcriptional networks coordinate the expression of genes for organellar functions. Nearly 1,300 ATH1 microarray-based transcriptional profiles of nuclear and organellar genes for mitochondrial and chloroplast proteins in the model plant Arabidopsis (Arabidopsis thaliana) were analyzed. The activity of genes involved in organellar energy production (OEP) or OGE in each of the organelles and in the nucleus is highly coordinated. Intracompartmental networks that link the OEP and OGE gene sets serve to synchronize the expression of nucleus- and organelle-encoded proteins. At a higher regulatory level, coexpression of organellar and nuclear OEP/OGE genes typically modulates chloroplast functions but affects mitochondria only when chloroplast functions are perturbed. Under conditions that induce energy shortage, the intercompartmental coregulation of photosynthesis genes can even override intracompartmental networks. We conclude that dynamic intracompartmental and intercompartmental transcriptional networks for OEP and OGE genes adjust the activity of organelles in response to the cellular energy state and environmental stresses, and we identify candidate cis-elements involved in the transcriptional coregulation of nuclear genes. Regarding the transcriptional regulation of chloroplast genes, novel tentative target genes of σ factors are identified.

  20. Co-expression of Arabidopsis transcription factor, AtMYB12, and soybean isoflavone synthase, GmIFS1, genes in tobacco leads to enhanced biosynthesis of isoflavones and flavonols resulting in osteoprotective activity.

    PubMed

    Pandey, Ashutosh; Misra, Prashant; Khan, Mohd P; Swarnkar, Gaurav; Tewari, Mahesh C; Bhambhani, Sweta; Trivedi, Ritu; Chattopadhyay, Naibedya; Trivedi, Prabodh K

    2014-01-01

    Isoflavones, a group of flavonoids, restricted almost exclusively to family Leguminosae are known to exhibit anticancerous and anti-osteoporotic activities in animal systems and have been a target for metabolic engineering in commonly consumed food crops. Earlier efforts based on the expression of legume isoflavone synthase (IFS) genes in nonlegume plant species led to the limited success in terms of isoflavone content in transgenic tissue due to the limitation of substrate for IFS enzyme. In this work to overcome this limitation, the activation of multiple genes of flavonoid pathway using Arabidopsis transcription factor AtMYB12 has been carried out. We developed transgenic tobacco lines constitutively co-expressing AtMYB12 and GmIFS1 (soybean IFS) genes or independently and carried out their phytochemical and molecular analyses. The leaves of co-expressing transgenic lines were found to have elevated flavonol content along with the accumulation of substantial amount of genistein glycoconjugates being at the highest levels that could be engineered in tobacco leaves till date. Oestrogen-deficient (ovariectomized, Ovx) mice fed with leaf extract from transgenic plant co-expressing AtMYB12 and GmIFS1 but not wild-type extract exhibited significant conservation of trabecular microarchitecture, reduced osteoclast number and expression of osteoclastogenic genes, higher total serum antioxidant levels and increased uterine oestrogenicity compared with Ovx mice treated with vehicle (control). The skeletal effect of the transgenic extract was comparable to oestrogen-treated Ovx mice. Together, our results establish an efficient strategy for successful pathway engineering of isoflavones and other flavonoids in crop plants and provide a direct evidence of improved osteoprotective effect of transgenic plant extract.

  1. Construction of a bivalent DNA vaccine co-expressing S genes of transmissible gastroenteritis virus and porcine epidemic diarrhea virus delivered by attenuated Salmonella typhimurium.

    PubMed

    Zhang, Yudi; Zhang, Xiaohui; Liao, Xiaodan; Huang, Xiaobo; Cao, Sanjie; Wen, Xintian; Wen, Yiping; Wu, Rui; Liu, Wumei

    2016-06-01

    Porcine transmissible gastroenteritis virus (TGEV) and porcine epidemic diarrhea virus (PEDV) can cause severe diarrhea in newborn piglets and led to significant economic losses. The S proteins are the main structural proteins of PEDV and TGEV capable of inducing neutralizing antibodies in vivo. In this study, a DNA vaccine SL7207 (pVAXD-PS1-TS) co-expressing S proteins of TGEV and PEDV delivered by attenuated Salmonella typhimurium was constructed and its immunogenicity in piglets was investigated. Twenty-day-old piglets were orally immunized with SL7207 (pVAXD-PS1-TS) at a dosage of 1.6 × 10(11) CFU per piglet and then booster immunized with 2.0 × 10(11) CFU after 2 weeks. Humoral immune responses, as reflected by virus neutralizing antibodies and specific IgG and sIgA, and cellular immune responses, as reflected by IFN-γ, IL-4, and lymphocyte proliferation, were evaluated. SL7207 (pVAXD-PS1-TS) simultaneously elicited immune responses against TGEV and PEDV after oral immunization. The immune levels started to increase at 2 weeks after immunization and increased to levels statistically significantly different than controls at 4 weeks post-immunization, peaking at 6 weeks and declined at 8 weeks. The humoral, mucosal, and cellular immune responses induced by SL7207 (pAXD-PS1-TS) were significantly higher than those of the PBS and SL7207 (pVAXD) (p < 0.01). In particular, the levels of IFN-γ and IL-4 were higher than those induced by the single-gene vaccine SL7207 (pVAXD-PS1) (p < 0.05). These results demonstrated that SL7207 (pVAXD-PS1-TS) possess the immunological functions of the two S proteins of TGEV and PEDV, indicating that SL7207 (pVAXD-PS1-TS) is a candidate oral vaccine for TGE and PED.

  2. Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

    SciTech Connect

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; Overall, Christopher C.; Hill, Eric A.; Beliaev, Alex S.

    2015-03-27

    Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

  3. Integrated in silico analyses of regulatory and metabolic networks of Synechococcus sp. PCC 7002 reveal relationships between gene centrality and essentiality

    DOE PAGES

    Song, Hyun-Seob; McClure, Ryan S.; Bernstein, Hans C.; ...

    2015-03-27

    Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as ‘topologically important.’ Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termedmore » as ‘functionally important’ genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.« less

  4. Unbiased Functional Clustering of Gene Variants with a Phenotypic-Linkage Network

    PubMed Central

    Honti, Frantisek; Meader, Stephen; Webber, Caleb

    2014-01-01

    Groupwise functional analysis of gene variants is becoming standard in next-generation sequencing studies. As the function of many genes is unknown and their classification to pathways is scant, functional associations between genes are often inferred from large-scale omics data. Such data types—including protein–protein interactions and gene co-expression networks—are used to examine the interrelations of the implicated genes. Statistical significance is assessed by comparing the interconnectedness of the mutated genes with that of random gene sets. However, interconnectedness can be affected by confounding bias, potentially resulting in false positive findings. We show that genes implicated through de novo sequence variants are biased in their coding-sequence length and longer genes tend to cluster together, which leads to exaggerated p-values in functional studies; we present here an integrative method that addresses these bias. To discern molecular pathways relevant to complex disease, we have inferred functional associations between human genes from diverse data types and assessed them with a novel phenotype-based method. Examining the functional association between de novo gene variants, we control for the heretofore unexplored confounding bias in coding-sequence length. We test different data types and networks and find that the disease-associated genes cluster more significantly in an integrated phenotypic-linkage network than in other gene networks. We present a tool of superior power to identify functional associations among genes mutated in the same disease even after accounting for significant sequencing study bias and demonstrate the suitability of this method to functionally cluster variant genes underlying polygenic disorders. PMID:25166029

  5. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

    PubMed Central

    Blatti, Charles; Sinha, Saurabh

    2016-01-01

    Motivation: Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or ‘properties’ such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene–gene or gene–property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. Results: We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. Availability and Implementation: DRaWR was implemented as

  6. Analysis of cascading failure in gene networks.

    PubMed

    Sun, Longxiao; Wang, Shudong; Li, Kaikai; Meng, Dazhi

    2012-01-01

    It is an important subject to research the functional mechanism of cancer-related genes make in formation and development of cancers. The modern methodology of data analysis plays a very important role for deducing the relationship between cancers and cancer-related genes and analyzing functional mechanism of genome. In this research, we construct mutual information networks using gene expression profiles of glioblast and renal in normal condition and cancer conditions. We investigate the relationship between structure and robustness in gene networks of the two tissues using a cascading failure model based on betweenness centrality. Define some important parameters such as the percentage of failure nodes of the network, the average size-ratio of cascading failure, and the cumulative probability of size-ratio of cascading failure to measure the robustness of the networks. By comparing control group and experiment groups, we find that the networks of experiment groups are more robust than that of control group. The gene that can cause large scale failure is called structural key gene. Some of them have been confirmed to be closely related to the formation and development of glioma and renal cancer respectively. Most of them are predicted to play important roles during the formation of glioma and renal cancer, maybe the oncogenes, suppressor genes, and other cancer candidate genes in the glioma and renal cancer cells. However, these studies provide little information about the detailed roles of identified cancer genes.

  7. Structure and function of gene regulatory networks associated with worker sterility in honeybees.

    PubMed

    Sobotka, Julia A; Daley, Mark; Chandrasekaran, Sriram; Rubin, Benjamin D; Thompson, Graham J

    2016-03-01

    A characteristic of eusocial bees is a reproductive division of labor in which one or a few queens monopolize reproduction, while her worker daughters take on reproductively altruistic roles within the colony. The evolution of worker reproductive altruism involves indirect selection for the coordinated expression of genes that regulate personal reproduction, but evidence for this type of selection remains elusive. In this study, we tested whether genes coexpressed under queen-induced worker sterility show evidence of adaptive organization within a model brain transcriptional regulatory network (TRN). If so, this structured pattern would imply that indirect selection on nonreproductive workers has influenced the functional organization of genes within the network, specifically to regulate the expression of sterility. We found that literature-curated sets of candidate genes for sterility, ranging in size from 18 to 267, show strong evidence of clustering within the three-dimensional space of the TRN. This finding suggests that our candidate sets of genes for sterility form functional modules within the living bee brain's TRN. Moreover, these same gene sets colocate to a single, albeit large, region of the TRN's topology. This spatially organized and convergent pattern contrasts with a null expectation for functionally unrelated genes to be haphazardly distributed throughout the network. Our meta-genomic analysis therefore provides first evidence for a truly "social transcriptome" that may regulate the conditional expression of honeybee worker sterility.

  8. Network Topology Reveals Key Cardiovascular Disease Genes

    PubMed Central

    Stojković, Neda; Radak, Djordje; Pržulj, Nataša

    2013-01-01

    The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and “driver genes.” We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies “key” genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs. PMID:23977067

  9. Gene Regulation Networks for Modeling Drosophila Development

    NASA Technical Reports Server (NTRS)

    Mjolsness, E.

    1999-01-01

    This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila Melanogaster.

  10. Autonomous Boolean modeling of gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Sun, Mengyang; Cheng, Xianrui

    2014-03-01

    In cases where the dynamical properties of gene regulatory networks are important, a faithful model must include three key features: a network topology; a functional response of each element to its inputs; and timing information about the transmission of signals across network links. Autonomous Boolean network (ABN) models are efficient representations of these elements and are amenable to analysis. We present an ABN model of the gene regulatory network governing cell fate specification in the early sea urchin embryo, which must generate three bands of distinct tissue types after several cell divisions, beginning from an initial condition with only two distinct cell types. Analysis of the spatial patterning problem and the dynamics of a network constructed from available experimental results reveals that a simple mechanism is at work in this case. Supported by NSF Grant DMS-10-68602

  11. Identifying genes of gene regulatory networks using formal concept analysis.

    PubMed

    Gebert, Jutta; Motameny, Susanne; Faigle, Ulrich; Forst, Christian V; Schrader, Rainer

    2008-03-01

    In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g., in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application, our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.

  12. Time-course gene profiling and networks in demethylated retinoblastoma cell line.

    PubMed

    Malusa, Federico; Taranta, Monia; Zaki, Nazar; Cinti, Caterina; Capobianco, Enrico

    2015-09-15

    Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2'-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.

  13. Time-course gene profiling and networks in demethylated retinoblastoma cell line

    PubMed Central

    Malusa, Federico; Taranta, Monia; Zaki, Nazar; Cinti, Caterina; Capobianco, Enrico

    2015-01-01

    Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects. PMID:26143641

  14. Gene Networks Specific for Innate Immunity Define Post-Traumatic Stress Disorder

    PubMed Central

    Breen, Michael S.; Maihofer, Adam X.; Glatt, Stephen J.; Tylee, Daniel S.; Chandler, Sharon D.; Tsuang, Ming T.; Risbrough, Victoria B.; Baker, Dewleen G.; O’Connor, Daniel T.; Nievergelt, Caroline M.; Woelk, Christopher H.

    2015-01-01

    The molecular factors involved in the development of Post-Traumatic Stress Disorder (PTSD) remain poorly understood. Previous transcriptomic studies investigating the mechanisms of PTSD apply targeted approaches to identify individual genes under a cross-sectional framework lack a holistic view of the behaviours and properties of these genes at the system-level. Here we sought to apply an unsupervised gene-network based approach to a prospective experimental design using whole-transcriptome RNA-Seq gene expression from peripheral blood leukocytes of U.S. Marines (N=188), obtained both pre- and post-deployment to conflict zones. We identified discrete groups of co-regulated genes (i.e., co-expression modules) and tested them for association to PTSD. We identified one module at both pre- and post-deployment containing putative causal signatures for PTSD development displaying an over-expression of genes enriched for functions of innate-immune response and interferon signalling (Type-I and Type-II). Importantly, these results were replicated in a second non-overlapping independent dataset of U.S. Marines (N=96), further outlining the role of innate immune and interferon signalling genes within co-expression modules to explain at least part of the causal pathophysiology for PTSD development. A second module, consequential of trauma exposure, contained PTSD resiliency signatures and an over-expression of genes involved in hemostasis and wound responsiveness suggesting that chronic levels of stress impair proper wound healing during/after exposure to the battlefield while highlighting the role of the hemostatic system as a clinical indicator of chronic-based stress. These findings provide novel insights for early preventative measures and advanced PTSD detection, which may lead to interventions that delay or perhaps abrogate the development of PTSD. PMID:25754082

  15. Combinatorial explosion in model gene networks

    NASA Astrophysics Data System (ADS)

    Edwards, R.; Glass, L.

    2000-09-01

    The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

  16. Network analysis reveals crosstalk between autophagy genes and disease genes

    PubMed Central

    Wang, Ji-Ye; Yao, Wei-Xuan; Wang, Yun; Fan, Yi-lei; Wu, Jian-Bing

    2017-01-01

    Autophagy is a protective and life-sustaining process in which cytoplasmic components are packaged into double-membrane vesicles and targeted to lysosomes for degradation. Accumulating evidence supports that autophagy is associated with several pathological conditions. However, research on the functional cross-links between autophagy and disease genes remains in its early stages. In this study, we constructed a disease-autophagy network (DAN) by integrating known disease genes, known autophagy genes and protein-protein interactions (PPI). Dissecting the topological properties of the DAN suggested that nodes that both autophagy and disease genes (inter-genes), are topologically important in the DAN structure. Next, a core network from the DAN was extracted to analyze the functional links between disease and autophagy genes. The genes in the core network were significantly enriched in multiple disease-related pathways, suggesting that autophagy genes may function in various disease processes. Of 17 disease classes, 11 significantly overlapped with autophagy genes, including cancer diseases, metabolic diseases and hematological diseases, a finding that is supported by the literatures. We also found that autophagy genes have a bridging role in the connections between pairs of disease classes. Altogether, our study provides a better understanding of the molecular mechanisms underlying human diseases and the autophagy process. PMID:28295050

  17. Modeling gene regulatory network motifs using statecharts

    PubMed Central

    2012-01-01

    Background Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks. For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. Results We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal. We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. Conclusions We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed. PMID:22536967

  18. Chaperone Hsp47 Drives Malignant Growth and Invasion by Modulating an ECM Gene Network.

    PubMed

    Zhu, Jieqing; Xiong, Gaofeng; Fu, Hanjiang; Evers, B Mark; Zhou, Binhua P; Xu, Ren

    2015-04-15

    The extracellular matrix (ECM) is a determining factor in the tumor microenvironment that restrains or promotes malignant growth. In this report, we show how the molecular chaperone protein Hsp47 functions as a nodal hub in regulating an ECM gene transcription network. A transcription network analysis showed that Hsp47 expression was activated during breast cancer development and progression. Hsp47 silencing reprogrammed human breast cancer cells to form growth-arrested and/or noninvasive structures in 3D cultures, and to limit tumor growth in xenograft assays by reducing deposition of collagen and fibronectin. Coexpression network analysis also showed that levels of microRNA(miR)-29b and -29c were inversely correlated with expression of Hsp47 and ECM network genes in human breast cancer tissues. We found that miR-29 repressed expression of Hsp47 along with multiple ECM network genes. Ectopic expression of miR-29b suppressed malignant phenotypes of breast cancer cells in 3D culture. Clinically, increased expression of Hsp47 and reduced levels of miR-29b and -29c were associated with poor survival outcomes in breast cancer patients. Our results show that Hsp47 is regulated by miR-29 during breast cancer development and progression, and that increased Hsp47 expression promotes cancer progression in part by enhancing deposition of ECM proteins.

  19. Phenotypic switching in gene regulatory networks.

    PubMed

    Thomas, Philipp; Popović, Nikola; Grima, Ramon

    2014-05-13

    Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory.

  20. Modeling gene regulatory networks: A network simplification algorithm

    NASA Astrophysics Data System (ADS)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  1. Mutated Genes in Schizophrenia Map to Brain Networks

    MedlinePlus

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks in the prefrontal ... Vasculitis Therapy as Effective as Standard Care Mutated Genes in Schizophrenia Map to Brain Networks Connect with Us Subscribe to ...

  2. Subspace differential coexpression analysis: problem definition and a general approach.

    PubMed

    Fang, Gang; Kuang, Rui; Pandey, Gaurav; Steinbach, Michael; Myers, Chad L; Kumar, Vipin

    2010-01-01

    In this paper, we study methods to identify differential coexpression patterns in case-control gene expression data. A differential coexpression pattern consists of a set of genes that have substantially different levels of coherence of their expression profiles across the two sample-classes, i.e., highly coherent in one class, but not in the other. Biologically, a differential coexpression patterns may indicate the disruption of a regulatory mechanism possibly caused by disregulation of pathways or mutations of transcription factors. A common feature of all the existing approaches for differential coexpression analysis is that the coexpression of a set of genes is measured on all the samples in each of the two classes, i.e., over the full-space of samples. Hence, these approaches may miss patterns that only cover a subset of samples in each class, i.e., subspace patterns, due to the heterogeneity of the subject population and disease causes. In this paper, we extend differential coexpression analysis by defining a subspace differential coexpression pattern, i.e., a set of genes that are coexpressed in a relatively large percent of samples in one class, but in a much smaller percent of samples in the other class. We propose a general approach based upon association analysis framework that allows exhaustive yet efficient discovery of subspace differential coexpression patterns. This approach can be used to adapt a family of biclustering algorithms to obtain their corresponding differential versions that can directly discover differential coexpression patterns. Using a recently developed biclustering algorithm as illustration, we perform experiments on cancer datasets which demonstrates the existence of subspace differential coexpression patterns. Permutation tests demonstrate the statistical significance for a large number of discovered subspace patterns, many of which can not be discovered if they are measured over all the samples in each of the classes

  3. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data

    PubMed Central

    2013-01-01

    Background High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. Results We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. Conclusions We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments. PMID:24053776

  4. Diversification in the genetic architecture of gene expression and transcriptional networks in organ differentiation of Populus.

    PubMed

    Drost, Derek R; Benedict, Catherine I; Berg, Arthur; Novaes, Evandro; Novaes, Carolina R D B; Yu, Qibin; Dervinis, Christopher; Maia, Jessica M; Yap, John; Miles, Brianna; Kirst, Matias

    2010-05-04

    A fundamental goal of systems biology is to identify genetic elements that contribute to complex phenotypes and to understand how they interact in networks predictive of system response to genetic variation. Few studies in plants have developed such networks, and none have examined their conservation among functionally specialized organs. Here we used genetical genomics in an interspecific hybrid population of the model hardwood plant Populus to uncover transcriptional networks in xylem, leaves, and roots. Pleiotropic eQTL hotspots were detected and used to construct coexpression networks a posteriori, for which regulators were predicted based on cis-acting expression regulation. Networks were shown to be enriched for groups of genes that function in biologically coherent processes and for cis-acting promoter motifs with known roles in regulating common groups of genes. When contrasted among xylem, leaves, and roots, transcriptional networks were frequently conserved in composition, but almost invariably regulated by different loci. Similarly, the genetic architecture of gene expression regulation is highly diversified among plant organs, with less than one-third of genes with eQTL detected in two organs being regulated by the same locus. However, colocalization in eQTL position increases to 50% when they are detected in all three organs, suggesting conservation in the genetic regulation is a function of ubiquitous expression. Genes conserved in their genetic regulation among all organs are primarily cis regulated (approximately 92%), whereas genes with eQTL in only one organ are largely trans regulated. Trans-acting regulation may therefore be the primary driver of differentiation in function between plant organs.

  5. Induction of Cellular Immune Response by DNA Vaccine Coexpressing E. acervulina 3-1E Gene and Mature CHIl-15 Gene

    PubMed Central

    Ma, Dexing; Ma, Chunli; Gao, Mingyang; Li, Guangxing; Niu, Ze; Huang, Xiaodan

    2012-01-01

    We previously reported that the chimeric DNA vaccine pcDNA-3-1E-linker-mChIL-15, fused through linking Eimeria acervulina 3-1E encoding gene and mature chicken IL-15 (mChIL-15) gene with four flexible amino acid SPGS, could significantly offer protection against homologous challenge. In the present study, the induction of cellular immune response induced by the chimeric DNA vaccine pcDNA-3-1E-linker-mChIL-15 was investigated. Spleen lymphocyte subpopulations were characterized by flow cytometric analysis. The spleen lymphocyte proliferation assays were measured by 3-[4,5-dimethylthiazol-2-y1]-2,5-diphenyltetrazolium bromide (MTT) method. The mRNA profiles of ChIL-2 and ChIFN-γ in spleen were characterized by means of real-time PCR. Chickens immunized with pcDNA-3-1E-linker-mChIL-15 exhibited significant upregulated level of ChIL-2 and ChIFN-γ transcripts in spleen following two immunizations compared with chickens in other groups (P < 0.01). In comparison with pcDNA3.1-immunized and control groups, lymphocyte proliferation, percentage of CD8α+ cell, and levels of ChIL-2 and ChIFN-γ transcripts in the group immunized with pcDNA-3-1E-linker-mChIL-15 were significantly increased on day 6 following challenge (P < 0.05, P < 0.01, and P < 0.01, resp.). Our data suggested that the fusion antigen 3-1E-linker-mChIL-15 could be a potential candidate for E. acervulina vaccine development. PMID:22754694

  6. COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH).

    PubMed

    Yang, Sunmo; Kim, Chan Yeong; Hwang, Sohyun; Kim, Eiru; Kim, Hyojin; Shim, Hongseok; Lee, Insuk

    2017-01-04

    The use of high-throughput array and sequencing technologies has produced unprecedented amounts of gene expression data in central public depositories, including the Gene Expression Omnibus (GEO). The immense amount of expression data in GEO provides both vast research opportunities and data analysis challenges. Co-expression analysis of high-dimensional expression data has proven effective for the study of gene functions, and several co-expression databases have been developed. Here, we present a new co-expression database, COEXPEDIA (www.coexpedia.org), which is distinctive from other co-expression databases in three aspects: (i) it contains only co-functional co-expressions that passed a rigorous statistical assessment for functional association, (ii) the co-expressions were inferred from individual studies, each of which was designed to investigate gene functions with respect to a particular biomedical context such as a disease and (iii) the co-expressions are associated with medical subject headings (MeSH) that provide biomedical information for anatomical, disease, and chemical relevance. COEXPEDIA currently contains approximately eight million co-expressions inferred from 384 and 248 GEO series for humans and mice, respectively. We describe how these MeSH-associated co-expressions enable the identification of diseases and drugs previously unknown to be related to a gene or a gene group of interest.

  7. COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH)

    PubMed Central

    Yang, Sunmo; Kim, Chan Yeong; Hwang, Sohyun; Kim, Eiru; Kim, Hyojin; Shim, Hongseok; Lee, Insuk

    2017-01-01

    The use of high-throughput array and sequencing technologies has produced unprecedented amounts of gene expression data in central public depositories, including the Gene Expression Omnibus (GEO). The immense amount of expression data in GEO provides both vast research opportunities and data analysis challenges. Co-expression analysis of high-dimensional expression data has proven effective for the study of gene functions, and several co-expression databases have been developed. Here, we present a new co-expression database, COEXPEDIA (www.coexpedia.org), which is distinctive from other co-expression databases in three aspects: (i) it contains only co-functional co-expressions that passed a rigorous statistical assessment for functional association, (ii) the co-expressions were inferred from individual studies, each of which was designed to investigate gene functions with respect to a particular biomedical context such as a disease and (iii) the co-expressions are associated with medical subject headings (MeSH) that provide biomedical information for anatomical, disease, and chemical relevance. COEXPEDIA currently contains approximately eight million co-expressions inferred from 384 and 248 GEO series for humans and mice, respectively. We describe how these MeSH-associated co-expressions enable the identification of diseases and drugs previously unknown to be related to a gene or a gene group of interest. PMID:27679477

  8. Co-expression changes of lncRNAs and mRNAs in the cervical sympathetic ganglia in diabetic cardiac autonomic neuropathic rats.

    PubMed

    Li, Guilin; Sheng, Xuan; Xu, Yurong; Jiang, Huaide; Zheng, Chaoran; Guo, Jingjing; Sun, Shanshan; Yi, Zhihua; Qin, Shulan; Liu, Shuangmei; Gao, Yun; Zhang, Chunping; Xu, Hong; Wu, Bing; Zou, Lifang; Liang, Shangdong; Zhu, Gaochun

    2016-12-19

    Cardiac autonomic neuropathy in Type 2 diabetes (T2D) is often a devastating complication. Long non-coding RNAs (lncRNAs) have important effects on both normal development and disease pathogenesis. In this study, we explored the expression profiles of some lncRNAs involved in inflammation which may be co-expressed with messenger RNA (mRNA) in superior cervical and stellate ganglia after type 2 diabetic injuries. Total RNA isolated from 10 pairs of superior cervical and stellate ganglia in diabetic and normal male rats was hybridized to lncRNA arrays for detections. Pathway analysis indicated that the most significant gene ontology (GO) processes that were upregulated in diabetes were associated with immune response, cell migration, defense response, taxis, and chemotaxis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed that most of the target genes of the lncRNAs were located in cytokine-cytokine receptor interactions, the chemokine signaling pathway and cell adhesion molecules, which were involved in T2D. Gene co-expression network construction showed that the co-expression network in the experimental rats consisted of 268 regulation edges among 105 lncRNAs and 11 mRNAs. Our studies demonstrated the co-expression profile of lncRNAs and mRNAs in diabetic cardiac autonomic ganglia, suggesting possible roles for multiple lncRNAs as potential targets for the development of therapeutic strategies or biomarkers for diabetic cardiac autonomic neuropathy. © 2016 Wiley Periodicals, Inc.

  9. Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice

    PubMed Central

    Hilchey, Shannon P.; Thakar, Juilee; Liu, Zhi-Ping; Welle, Stephen L.; Henn, Alicia D.; Wu, Hulin; Zand, Martin S.

    2015-01-01

    Current approaches to study transcriptional profiles post influenza infection typically rely on tissue sampling from one or two sites at a few time points, such as spleen and lung in murine models. In this study, we infected female C57/BL6 mice intranasally with mouse-adapted H3N2/Hong Kong/X31 avian influenza A virus, and then analyzed the gene expression profiles in four different compartments (blood, lung, mediastinal lymph nodes, and spleen) over 11 consecutive days post infection. These data were analyzed by an advanced statistical procedure based on ordinary differential equation (ODE) modeling. Vastly different lists of significant genes were identified by the same statistical procedure in each compartment. Only 11 of them are significant in all four compartments. We classified significant genes in each compartment into co-expressed modules based on temporal expression patterns. We then performed functional enrichment analysis on these co-expression modules and identified significant pathway and functional motifs. Finally, we used an ODE based model to reconstruct gene regulatory network (GRN) for each compartment and studied their network properties. PMID:26413862

  10. Snapshot of iron response in Shewanella oneidensis by gene network reconstruction

    SciTech Connect

    Yang, Yunfeng; Harris, Daniel P.; Luo, Feng; Xiong, Wenlu; Joachimiak, Marcin; Wu, Liyou; Dehal, Paramvir; Jacobsen, Janet; Yang, Zamin; Palumbo, Anthony V.; Arkin, Adam P.; Zhou, Jizhong

    2008-10-09

    Background: Iron homeostasis of Shewanella oneidensis, a gamma-proteobacterium possessing high iron content, is regulated by a global transcription factor Fur. However, knowledge is incomplete about other biological pathways that respond to changes in iron concentration, as well as details of the responses. In this work, we integrate physiological, transcriptomics and genetic approaches to delineate the iron response of S. oneidensis. Results: We show that the iron response in S. oneidensis is a rapid process. Temporal gene expression profiles were examined for iron depletion and repletion, and a gene co-expression network was reconstructed. Modules of iron acquisition systems, anaerobic energy metabolism and protein degradation were the most noteworthy in the gene network. Bioinformatics analyses suggested that genes in each of the modules might be regulated by DNA-binding proteins Fur, CRP and RpoH, respectively. Closer inspection of these modules revealed a transcriptional regulator (SO2426) involved in iron acquisition and ten transcriptional factors involved in anaerobic energy metabolism. Selected genes in the network were analyzed by genetic studies. Disruption of genes encoding a putative alcaligin biosynthesis protein (SO3032) and a gene previously implicated in protein degradation (SO2017) led to severe growth deficiency under iron depletion conditions. Disruption of a novel transcriptional factor (SO1415) caused deficiency in both anaerobic iron reduction and growth with thiosulfate or TMAO as an electronic acceptor, suggesting that SO1415 is required for specific branches of anaerobic energy metabolism pathways. Conclusions: Using a reconstructed gene network, we identified major biological pathways that were differentially expressed during iron depletion and repletion. Genetic studies not only demonstrated the importance of iron acquisition and protein degradation for iron depletion, but also characterized a novel transcriptional factor (SO1415) with a

  11. Gene network biological validity based on gene-gene interaction relevance.

    PubMed

    Gómez-Vela, Francisco; Díaz-Díaz, Norberto

    2014-01-01

    In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

  12. Gene Networks in the Wild: Identifying Transcriptional Modules that Mediate Coral Resistance to Experimental Heat Stress.

    PubMed

    Rose, Noah H; Seneca, Francois O; Palumbi, Stephen R

    2015-12-28

    Organisms respond to environmental variation partly through changes in gene expression, which underlie both homeostatic and acclimatory responses to environmental stress. In some cases, so many genes change in expression in response to different influences that understanding expression patterns for all these individual genes becomes difficult. To reduce this problem, we use a systems genetics approach to show that variation in the expression of thousands of genes of reef-building corals can be explained as variation in the expression of a small number of coexpressed "modules." Modules were often enriched for specific cellular functions and varied predictably among individuals, experimental treatments, and physiological state. We describe two transcriptional modules for which expression levels immediately after heat stress predict bleaching a day later. One of these early "bleaching modules" is enriched for sequence-specific DNA-binding proteins, particularly E26 transformation-specific (ETS)-family transcription factors. The other module is enriched for extracellular matrix proteins. These classes of bleaching response genes are clear in the modular gene expression analysis we conduct but are much more difficult to discern in single gene analyses. Furthermore, the ETS-family module shows repeated differences in expression among coral colonies grown in the same common garden environment, suggesting a heritable genetic or epigenetic basis for these expression polymorphisms. This finding suggests that these corals harbor high levels of gene-network variation, which could facilitate rapid evolution in the face of environmental change.

  13. Gene Networks in the Wild: Identifying Transcriptional Modules that Mediate Coral Resistance to Experimental Heat Stress

    PubMed Central

    Rose, Noah H.; Seneca, Francois O.; Palumbi, Stephen R.

    2016-01-01

    Organisms respond to environmental variation partly through changes in gene expression, which underlie both homeostatic and acclimatory responses to environmental stress. In some cases, so many genes change in expression in response to different influences that understanding expression patterns for all these individual genes becomes difficult. To reduce this problem, we use a systems genetics approach to show that variation in the expression of thousands of genes of reef-building corals can be explained as variation in the expression of a small number of coexpressed “modules.” Modules were often enriched for specific cellular functions and varied predictably among individuals, experimental treatments, and physiological state. We describe two transcriptional modules for which expression levels immediately after heat stress predict bleaching a day later. One of these early “bleaching modules” is enriched for sequence-specific DNA-binding proteins, particularly E26 transformation-specific (ETS)-family transcription factors. The other module is enriched for extracellular matrix proteins. These classes of bleaching response genes are clear in the modular gene expression analysis we conduct but are much more difficult to discern in single gene analyses. Furthermore, the ETS-family module shows repeated differences in expression among coral colonies grown in the same common garden environment, suggesting a heritable genetic or epigenetic basis for these expression polymorphisms. This finding suggests that these corals harbor high levels of gene-network variation, which could facilitate rapid evolution in the face of environmental change. PMID:26710855

  14. Co-expression of Skp and FkpA chaperones improves cell viability and alters the global expression of stress response genes during scFvD1.3 production

    PubMed Central

    2010-01-01

    Background The overexpression of scFv antibody fragments in the periplasmic space of Escherichia coli frequently results in extensive protein misfolding and loss of cell viability. Although protein folding factors such as Skp and FkpA are often exploited to restore the solubility and functionality of recombinant protein products, their exact impact on cellular metabolism during periplasmic antibody fragment expression is not clearly understood. In this study, we expressed the scFvD1.3 antibody fragment in E. coli BL21 and evaluated the overall physiological and global gene expression changes upon Skp or FkpA co-expression. Results The periplasmic expression of scFvD1.3 led to a rapid accumulation of insoluble scFvD1.3 proteins and a decrease in cell viability. The co-expression of Skp and FkpA improved scFvD1.3 solubility and cell viability in a dosage-dependent manner. Through mutagenesis experiments, it was found that only the chaperone activity of FkpA, not the peptidyl-prolyl isomerase (PPIase) activity, is required for the improvement in cell viability. Global gene expression analysis of the scFvD1.3 cells over the chaperone-expressing cells showed a clear up-regulation of genes involved in heat-shock and misfolded protein stress responses. These included genes of the major HSP70 DnaK chaperone family and key proteases belonging to the Clp and Lon protease systems. Other metabolic gene expression trends include: (1) the differential regulation of several energy metabolic genes, (2) down-regulation of the central metabolic TCA cycle and transport genes, and (3) up-regulation of ribosomal genes. Conclusions The simultaneous activation of multiple stress related and other metabolic genes may constitute the stress response to protein misfolding in the scFvD1.3 cells. These gene expression information could prove to be valuable for the selection and construction of reporter contructs to monitor the misfolded protein stress response during antibody fragment

  15. Summing up the noise in gene networks.

    PubMed

    Paulsson, Johan

    2004-01-29

    Random fluctuations in genetic networks are inevitable as chemical reactions are probabilistic and many genes, RNAs and proteins are present in low numbers per cell. Such 'noise' affects all life processes and has recently been measured using green fluorescent protein (GFP). Two studies show that negative feedback suppresses noise, and three others identify the sources of noise in gene expression. Here I critically analyse these studies and present a simple equation that unifies and extends both the mathematical and biological perspectives.

  16. GenePANDA—a novel network-based gene prioritizing tool for complex diseases

    PubMed Central

    Yin, Tianshu; Chen, Shu; Wu, Xiaohui; Tian, Weidong

    2017-01-01

    Here we describe GenePANDA, a novel network-based tool for prioritizing candidate disease genes. GenePANDA assesses whether a gene is likely a candidate disease gene based on its relative distance to known disease genes in a functional association network. A unique feature of GenePANDA is the introduction of adjusted network distance derived by normalizing the raw network distance between two genes with their respective mean raw network distance to all other genes in the network. The use of adjusted network distance significantly improves GenePANDA’s performance on prioritizing complex disease genes. GenePANDA achieves superior performance over five previously published algorithms for prioritizing disease genes. Finally, GenePANDA can assist in prioritizing functionally important SNPs identified by GWAS. PMID:28252032

  17. Generation of oscillating gene regulatory network motifs

    NASA Astrophysics Data System (ADS)

    van Dorp, M.; Lannoo, B.; Carlon, E.

    2013-07-01

    Using an improved version of an evolutionary algorithm originally proposed by François and Hakim [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0304532101 101, 580 (2004)], we generated small gene regulatory networks in which the concentration of a target protein oscillates in time. These networks may serve as candidates for oscillatory modules to be found in larger regulatory networks and protein interaction networks. The algorithm was run for 105 times to produce a large set of oscillating modules, which were systematically classified and analyzed. The robustness of the oscillations against variations of the kinetic rates was also determined, to filter out the least robust cases. Furthermore, we show that the set of evolved networks can serve as a database of models whose behavior can be compared to experimentally observed oscillations. The algorithm found three smallest (core) oscillators in which nonlinearities and number of components are minimal. Two of those are two-gene modules: the mixed feedback loop, already discussed in the literature, and an autorepressed gene coupled with a heterodimer. The third one is a single gene module which is competitively regulated by a monomer and a dimer. The evolutionary algorithm also generated larger oscillating networks, which are in part extensions of the three core modules and in part genuinely new modules. The latter includes oscillators which do not rely on feedback induced by transcription factors, but are purely of post-transcriptional type. Analysis of post-transcriptional mechanisms of oscillation may provide useful information for circadian clock research, as recent experiments showed that circadian rhythms are maintained even in the absence of transcription.

  18. Positioning the expanded akirin gene family of Atlantic salmon within the transcriptional networks of myogenesis

    SciTech Connect

    Macqueen, Daniel J.; Bower, Neil I.; Johnston, Ian A.

    2010-10-01

    Research highlights: {yields} The expanded akirin gene family of Atlantic salmon was characterised. {yields} akirin paralogues are regulated between mono- and multi-nucleated muscle cells. {yields} akirin paralogues positioned within known genetic networks controlling myogenesis. {yields} Co-expression of akirin paralogues is evident across cell types/during myogenesis. {yields} Selection has likely maintained common regulatory elements among akirin paralogues. -- Abstract: Vertebrate akirin genes usually form a family with one-to-three members that regulate gene expression during the innate immune response, carcinogenesis and myogenesis. We recently established that an expanded family of eight akirin genes is conserved across salmonid fish. Here, we measured mRNA levels of the akirin family of Atlantic salmon (Salmo salar L.) during the differentiation of primary myoblasts cultured from fast-skeletal muscle. Using hierarchical clustering and correlation, the data was positioned into a network of expression profiles including twenty further genes that regulate myogenesis. akirin1(2b) was not significantly regulated during the maturation of the cell culture. akirin2(1a) and 2(1b), along with IGF-II and several igfbps, were most highly expressed in mononuclear cells, then significantly and constitutively downregulated as differentiation proceeded and myotubes formed/matured. Conversely, akirin1(1a), 1(1b), 1(2a), 2(2a) and 2(2b) were expressed at lowest levels when mononuclear cells dominated the culture and highest levels when confluent layers of myotubes were evident. However, akirin1(2a) and 2(2a) were first upregulated earlier than akirin1(1a), 1(1b) and 2(2b), when rates of myoblast proliferation were highest. Interestingly, akirin1(1b), 1(2a), 2(2a) and 2(2b) formed part of a module of co-expressed genes involved in muscle differentiation, including myod1a, myog, mef2a, 14-3-3{beta} and 14-3-3{gamma}. All akirin paralogues were expressed ubiquitously across ten

  19. Identification of genes and signaling pathways associated with diabetic neuropathy using a weighted correlation network analysis

    PubMed Central

    Li, Ya; Ma, Weiguo; Xie, Chuanqing; Zhang, Min; Yin, Xiaohong; Wang, Fenfen; Xu, Jie; Shi, Bingyin

    2016-01-01

    Abstract Background: The molecular mechanisms behind diabetic neuropathy remains to be investigated. Methods: This is a secondary study on microarray dataset (GSE24290) downloaded from Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI), which included 18 nerve tissue samples of progressing diabetic neuropathy (fibers loss ≥500 fibers/mm2) and 17 nerve tissue samples of nonprogressing diabetic neuropathy (fibers loss ≤100 fibers/mm2). Differentially expressed genes (DEGs) were screened between progressing and nonprogressing diabetic neuropathy. With the DEGs obtained, a weighted gene coexpression network analysis was conducted to identify gene clusters associated with diabetic neuropathy. Diabetes-related microRNAs (miRNAs) and their target genes were predicted and mapped to the genes in the gene clusters identified. Consequently, a miRNA–gene network was constructed, for which gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed. Potential drugs for treatment of diabetic neuropathy were also predicted. Results: Total 370 upregulated and 379 downregulated DEGs were screened between nonprogressing and progressing diabetic neuropathy. Has-miR-377, has-miR-216a, and has-miR-217 were associated with diabetes. Inflammation was the most significant GO term. The peroxisome proliferator-activated receptor (PPAR) pathway and the adenosine monophosphate (AMP)-activated protein kinase (AMPK) signaling pathway were significantly KEGG pathways significantly enriched with PPAR gamma (PPARG), stearoyl-CoA desaturase (SCD), cluster of differentiation 36 (CD36), and phosphoenolpyruvate carboxykinase 1 (PCK1). Conclusion: The study suggests that PPARG, SCD, CD36, PCK1, AMPK pathway, and PPAR pathway may be involved in progression of diabetic neuropathy. PMID:27893688

  20. Gene regulatory networks and the underlying biology of developmental toxicity

    EPA Science Inventory

    Embryonic cells are specified by large-scale networks of functionally linked regulatory genes. Knowledge of the relevant gene regulatory networks is essential for understanding phenotypic heterogeneity that emerges from disruption of molecular functions, cellular processes or sig...

  1. Hybrid stochastic simplifications for multiscale gene networks

    PubMed Central

    Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

    2009-01-01

    Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach. PMID:19735554

  2. Discovering Study-Specific Gene Regulatory Networks

    PubMed Central

    Bo, Valeria; Curtis, Tanya; Lysenko, Artem; Saqi, Mansoor; Swift, Stephen; Tucker, Allan

    2014-01-01

    Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets. PMID:25191999

  3. Engineering stability in gene networks by autoregulation

    NASA Astrophysics Data System (ADS)

    Becskei, Attila; Serrano, Luis

    2000-06-01

    The genetic and biochemical networks which underlie such things as homeostasis in metabolism and the developmental programs of living cells, must withstand considerable variations and random perturbations of biochemical parameters. These occur as transient changes in, for example, transcription, translation, and RNA and protein degradation. The intensity and duration of these perturbations differ between cells in a population. The unique state of cells, and thus the diversity in a population, is owing to the different environmental stimuli the individual cells experience and the inherent stochastic nature of biochemical processes (for example, refs 5 and 6). It has been proposed, but not demonstrated, that autoregulatory, negative feedback loops in gene circuits provide stability, thereby limiting the range over which the concentrations of network components fluctuate. Here we have designed and constructed simple gene circuits consisting of a regulator and transcriptional repressor modules in Escherichia coli and we show the gain of stability produced by negative feedback.

  4. Pathway network inference from gene expression data

    PubMed Central

    2014-01-01

    Background The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data. PMID:25032889

  5. Co-expression analysis and identification of fecundity-related long non-coding RNAs in sheep ovaries

    PubMed Central

    Miao, Xiangyang; Luo, Qingmiao; Zhao, Huijing; Qin, Xiaoyu

    2016-01-01

    Small Tail Han sheep, including the FecBBFecBB (Han BB) and FecB+ FecB+ (Han++) genotypes, and Dorset sheep exhibit different fecundities. To identify novel long non-coding RNAs (lncRNAs) associated with sheep fecundity to better understand their molecular mechanisms, a genome-wide analysis of mRNAs and lncRNAs from Han BB, Han++ and Dorset sheep was performed. After the identification of differentially expressed mRNAs and lncRNAs, 16 significant modules were explored by using weighted gene coexpression network analysis (WGCNA) followed by functional enrichment analysis of the genes and lncRNAs in significant modules. Among these selected modules, the yellow and brown modules were significantly related to sheep fecundity. lncRNAs (e.g., NR0B1, XLOC_041882, and MYH15) in the yellow module were mainly involved in the TGF-β signalling pathway, and NYAP1 and BCORL1 were significantly associated with the oxytocin signalling pathway, which regulates several genes in the coexpression network of the brown module. Overall, we identified several gene modules associated with sheep fecundity, as well as networks consisting of hub genes and lncRNAs that may contribute to sheep prolificacy by regulating the target mRNAs related to the TGF-β and oxytocin signalling pathways. This study provides an alternative strategy for the identification of potential candidate regulatory lncRNAs. PMID:27982099

  6. Regulatory gene networks that shape the development of adaptive phenotypic plasticity in a cichlid fish.

    PubMed

    Schneider, Ralf F; Li, Yuanhao; Meyer, Axel; Gunter, Helen M

    2014-09-01

    Phenotypic plasticity is the ability of organisms with a given genotype to develop different phenotypes according to environmental stimuli, resulting in individuals that are better adapted to local conditions. In spite of their ecological importance, the developmental regulatory networks underlying plastic phenotypes often remain uncharacterized. We examined the regulatory basis of diet-induced plasticity in the lower pharyngeal jaw (LPJ) of the cichlid fish Astatoreochromis alluaudi, a model species in the study of adaptive plasticity. Through raising juvenile A. alluaudi on either a hard or soft diet (hard-shelled or pulverized snails) for between 1 and 8 months, we gained insight into the temporal regulation of 19 previously identified candidate genes during the early stages of plasticity development. Plasticity in LPJ morphology was first detected between 3 and 5 months of diet treatment. The candidate genes, belonging to various functional categories, displayed dynamic expression patterns that consistently preceded the onset of morphological divergence and putatively contribute to the initiation of the plastic phenotypes. Within functional categories, we observed striking co-expression, and transcription factor binding site analysis was used to examine the prospective basis of their coregulation. We propose a regulatory network of LPJ plasticity in cichlids, presenting evidence for regulatory crosstalk between bone and muscle tissues, which putatively facilitates the development of this highly integrated trait. Through incorporating a developmental time-course into a phenotypic plasticity study, we have identified an interconnected, environmentally responsive regulatory network that shapes the development of plasticity in a key innovation of East African cichlids.

  7. Improving gene regulatory network inference using network topology information.

    PubMed

    Nair, Ajay; Chetty, Madhu; Wangikar, Pramod P

    2015-09-01

    Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

  8. Sequencing-based gene network analysis provides a core set of gene resource for understanding thermal adaptation in Zhikong scallop Chlamys farreri.

    PubMed

    Fu, X; Sun, Y; Wang, J; Xing, Q; Zou, J; Li, R; Wang, Z; Wang, S; Hu, X; Zhang, L; Bao, Z

    2014-01-01

    Marine organisms are commonly exposed to variable environmental conditions, and many of them are under threat from increased sea temperatures caused by global climate change. Generating transcriptomic resources under different stress conditions are crucial for understanding molecular mechanisms underlying thermal adaptation. In this study, we conducted transcriptome-wide gene expression profiling of the scallop Chlamys farreri challenged by acute and chronic heat stress. Of the 13 953 unique tags, more than 850 were significantly differentially expressed at each time point after acute heat stress, which was more than the number of tags differentially expressed (320-350) under chronic heat stress. To obtain a systemic view of gene expression alterations during thermal stress, a weighted gene coexpression network was constructed. Six modules were identified as acute heat stress-responsive modules. Among them, four modules involved in apoptosis regulation, mRNA binding, mitochondrial envelope formation and oxidation reduction were downregulated. The remaining two modules were upregulated. One was enriched with chaperone and the other with microsatellite sequences, whose coexpression may originate from a transcription factor binding site. These results indicated that C. farreri triggered several cellular processes to acclimate to elevated temperature. No modules responded to chronic heat stress, suggesting that the scallops might have acclimated to elevated temperature within 3 days. This study represents the first sequencing-based gene network analysis in a nonmodel aquatic species and provides valuable gene resources for the study of thermal adaptation, which should assist in the development of heat-tolerant scallop lines for aquaculture.

  9. Evolutionary conservation of zinc finger transcription factor binding sites in promoters of genes co-expressed with WT1 in prostate cancer

    PubMed Central

    Eisermann, Kurtis; Tandon, Sunpreet; Bazarov, Anton; Brett, Adina; Fraizer, Gail; Piontkivska, Helen

    2008-01-01

    Background Gene expression analyses have led to a better understanding of growth control of prostate cancer cells. We and others have identified the presence of several zinc finger transcription factors in the neoplastic prostate, suggesting a potential role for these genes in the regulation of the prostate cancer transcriptome. One of the transcription factors (TFs) identified in the prostate cancer epithelial cells was the Wilms tumor gene (WT1). To rapidly identify coordinately expressed prostate cancer growth control genes that may be regulated by WT1, we used an in silico approach. Results Evolutionary conserved transcription factor binding sites (TFBS) recognized by WT1, EGR1, SP1, SP2, AP2 and GATA1 were identified in the promoters of 24 differentially expressed prostate cancer genes from eight mammalian species. To test the relationship between sequence conservation and function, chromatin of LNCaP prostate cancer and kidney 293 cells were tested for TF binding using chromatin immunoprecipitation (ChIP). Multiple putative TFBS in gene promoters of placental mammals were found to be shared with those in human gene promoters and some were conserved between genomes that diverged about 170 million years ago (i.e., primates and marsupials), therefore implicating these sites as candidate binding sites. Among those genes coordinately expressed with WT1 was the kallikrein-related peptidase 3 (KLK3) gene commonly known as the prostate specific antigen (PSA) gene. This analysis located several potential WT1 TFBS in the PSA gene promoter and led to the rapid identification of a novel putative binding site confirmed in vivo by ChIP. Conversely for two prostate growth control genes, androgen receptor (AR) and vascular endothelial growth factor (VEGF), known to be transcriptionally regulated by WT1, regulatory sequence conservation was observed and TF binding in vivo was confirmed by ChIP. Conclusion Overall, this targeted approach rapidly identified important candidate

  10. Gene-Transformation-Induced Changes in Chemical Functional Group Features and Molecular Structure Conformation in Alfalfa Plants Co-Expressing Lc-bHLH and C1-MYB Transcriptive Flavanoid Regulatory Genes: Effects of Single-Gene and Two-Gene Insertion

    PubMed Central

    Heendeniya, Ravindra G.; Yu, Peiqiang

    2017-01-01

    Alfalfa (Medicago sativa L.) genotypes transformed with Lc-bHLH and Lc transcription genes were developed with the intention of stimulating proanthocyanidin synthesis in the aerial parts of the plant. To our knowledge, there are no studies on the effect of single-gene and two-gene transformation on chemical functional groups and molecular structure changes in these plants. The objective of this study was to use advanced molecular spectroscopy with multivariate chemometrics to determine chemical functional group intensity and molecular structure changes in alfalfa plants when co-expressing Lc-bHLH and C1-MYB transcriptive flavanoid regulatory genes in comparison with non-transgenic (NT) and AC Grazeland (ACGL) genotypes. The results showed that compared to NT genotype, the presence of double genes (Lc and C1) increased ratios of both the area and peak height of protein structural Amide I/II and the height ratio of α-helix to β-sheet. In carbohydrate-related spectral analysis, the double gene-transformed alfalfa genotypes exhibited lower peak heights at 1370, 1240, 1153, and 1020 cm−1 compared to the NT genotype. Furthermore, the effect of double gene transformation on carbohydrate molecular structure was clearly revealed in the principal component analysis of the spectra. In conclusion, single or double transformation of Lc and C1 genes resulted in changing functional groups and molecular structure related to proteins and carbohydrates compared to the NT alfalfa genotype. The current study provided molecular structural information on the transgenic alfalfa plants and provided an insight into the impact of transgenes on protein and carbohydrate properties and their molecular structure’s changes. PMID:28335521

  11. Gene-Transformation-Induced Changes in Chemical Functional Group Features and Molecular Structure Conformation in Alfalfa Plants Co-Expressing Lc-bHLH and C1-MYB Transcriptive Flavanoid Regulatory Genes: Effects of Single-Gene and Two-Gene Insertion.

    PubMed

    Heendeniya, Ravindra G; Yu, Peiqiang

    2017-03-20

    Alfalfa (Medicago sativa L.) genotypes transformed with Lc-bHLH and Lc transcription genes were developed with the intention of stimulating proanthocyanidin synthesis in the aerial parts of the plant. To our knowledge, there are no studies on the effect of single-gene and two-gene transformation on chemical functional groups and molecular structure changes in these plants. The objective of this study was to use advanced molecular spectroscopy with multivariate chemometrics to determine chemical functional group intensity and molecular structure changes in alfalfa plants when co-expressing Lc-bHLH and C1-MYB transcriptive flavanoid regulatory genes in comparison with non-transgenic (NT) and AC Grazeland (ACGL) genotypes. The results showed that compared to NT genotype, the presence of double genes (Lc and C1) increased ratios of both the area and peak height of protein structural Amide I/II and the height ratio of α-helix to β-sheet. In carbohydrate-related spectral analysis, the double gene-transformed alfalfa genotypes exhibited lower peak heights at 1370, 1240, 1153, and 1020 cm(-1) compared to the NT genotype. Furthermore, the effect of double gene transformation on carbohydrate molecular structure was clearly revealed in the principal component analysis of the spectra. In conclusion, single or double transformation of Lc and C1 genes resulted in changing functional groups and molecular structure related to proteins and carbohydrates compared to the NT alfalfa genotype. The current study provided molecular structural information on the transgenic alfalfa plants and provided an insight into the impact of transgenes on protein and carbohydrate properties and their molecular structure's changes.

  12. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

    PubMed Central

    Lähdesmäki, Harri; Hautaniemi, Sampsa; Shmulevich, Ilya; Yli-Harja, Olli

    2006-01-01

    A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes. PMID:17415411

  13. Differential network analysis from cross-platform gene expression data

    PubMed Central

    Zhang, Xiao-Fei; Ou-Yang, Le; Zhao, Xing-Ming; Yan, Hong

    2016-01-01

    Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different groups. In addition, with the development of high-throughput technologies, we can collect gene expression profiles of same patients from multiple platforms. Therefore, inferring differential networks by considering cross-platform gene expression profiles will improve the reliability of network inference. We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks. TDJGL can borrow strength across different patient groups and data platforms to improve the accuracy of estimated networks. Simulation studies demonstrate that TDJGL provides more accurate estimates of gene networks and differential networks than previous competing approaches. We apply TDJGL to the PI3K/AKT/mTOR pathway in ovarian tumors to build differential networks associated with platinum resistance. The hub genes of our inferred differential networks are significantly enriched with known platinum resistance-related genes and include potential platinum resistance-related genes. PMID:27677586

  14. Paper-based Synthetic Gene Networks

    PubMed Central

    Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

    2014-01-01

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

  15. Paper-based synthetic gene networks.

    PubMed

    Pardee, Keith; Green, Alexander A; Ferrante, Tom; Cameron, D Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J

    2014-11-06

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

  16. Estimating Gene Regulatory Networks with pandaR.

    PubMed

    Schlauch, Daniel; Paulson, Joseph N; Young, Albert; Glass, Kimberly; Quackenbush, John

    2017-03-11

    PANDA (Passing Attributes betweenNetworks forData Assimilation) is a gene regulatory network inference method that begins with amodel of transcription factor-target gene interactions and usesmessage passing to update the network model given available transcriptomic and protein-protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks.

  17. Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization

    PubMed Central

    Shim, Jung Eun; Hwang, Sohyun; Lee, Insuk

    2015-01-01

    A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity. PMID:26091506

  18. Improved lysis efficiency and immunogenicity of Salmonella ghosts mediated by co-expression of λ phage holin-endolysin and ɸX174 gene E

    PubMed Central

    Won, Gayeon; Hajam, Irshad Ahmed; Lee, John Hwa

    2017-01-01

    Bacterial ghosts (BGs) are empty cell envelopes derived from Gram-negative bacteria by bacteriophage ɸX174 gene E mediated lysis. They represent a novel inactivated vaccine platform; however, the practical application of BGs for human vaccines seems to be limited due to the safety concerns on the presence of viable cells in BGs. Therefore, to improve the lysis efficiency of the gene E, we exploited the peptidoglycan hydrolyzing ability of the λ phage holin-endolysins to expedite the process of current BG production system. In this report, we constructed a novel ghost plasmid encoding protein E and holin-endolysins in tandem. We observed that sequential expressions of the gene E and the holin-endolysins elicited rapid and highly efficient Salmonella lysis compared to the lysis mediated by gene E only. These lysed BGs displayed improved immunogenicity in mice compared to the gene E mediated BGs. Consequently, seventy percent of the mice immunized with these novel ghosts survived against a lethal challenge while all the mice vaccinated with gene E mediated ghosts died by day 9 post-infection. We conclude that this novel strategy has the potential to generate highly efficient inactivated candidate vaccines that could replace the currently available bacterial vaccines. PMID:28332591

  19. Modular composition of gene transcription networks.

    PubMed

    Gyorgy, Andras; Del Vecchio, Domitilla

    2014-03-01

    Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.

  20. Discovering Implicit Entity Relation with the Gene-Citation-Gene Network

    PubMed Central

    Song, Min; Han, Nam-Gi; Kim, Yong-Hwan; Ding, Ying; Chambers, Tamy

    2013-01-01

    In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner. PMID:24358368

  1. Biomarker Gene Signature Discovery Integrating Network Knowledge

    PubMed Central

    Cun, Yupeng; Fröhlich, Holger

    2012-01-01

    Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches. PMID:24832044

  2. 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

  3. Co-expression analysis of fetal weight-related genes in ovine skeletal muscle during mid and late fetal development stages

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Muscle development and lipid metabolism play important roles during fetal development stages. The commercial Texel sheep are more muscular than the indigenous Ujumqin sheep which are fatter. We performed serial transcriptomics assays and systems biology analyses to investigate the dynamics of gene e...

  4. A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks.

    PubMed

    Petrovskaya, Olga V; Petrovskiy, Evgeny D; Lavrik, Inna N; Ivanisenko, Vladimir A

    2016-12-22

    Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

  5. Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers.

    PubMed

    Tamada, Yoshinori; Imoto, Seiya; Araki, Hiromitsu; Nagasaki, Masao; Print, Cristin; Charnock-Jones, D Stephen; Miyano, Satoru

    2011-01-01

    We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

  6. Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network

    PubMed Central

    Sun, Chaoyu; Jiang, Hao; Sun, Zhiguo; Gui, Yifang; Xia, Hongyuan

    2016-01-01

    Long non-coding RNAs (lncRNAs) have recently been shown as novel promising diagnostic or prognostic biomarkers for various cancers. However, lncRNA expression patterns and their predictive value in early diagnosis of myocardial infarction (MI) have not been systematically investigated. In our study, we performed a comprehensive analysis of lncRNA expression profiles in MI and found altered lncRNA expression pattern in MI compared to healthy samples. We then constructed a lncRNA-mRNA dysregulation network (DLMCEN) by integrating aberrant lncRNAs, mRNAs and their co-dysregulation relationships, and found that some of mRNAs were previously reported to be involved in cardiovascular disease, suggesting the functional roles of dysregulated lncRNAs in the pathogenesis of MI. Therefore, using support vector machine (SVM) and leave one out cross-validation (LOOCV), we developed a 9-lncRNA signature (termed 9LncSigAMI) from the discovery cohort which could distinguish MI patients from healthy samples with accuracy of 95.96%, sensitivity of 93.88% and specificity of 98%, and validated its predictive power in early diagnosis of MI in another completely independent cohort. Functional analysis demonstrated that these nine lncRNA biomarkers in the 9LncSigAMI may be involved in myocardial innate immune and inflammatory response, and their deregulation may lead to the dysfunction of the inflammatory and immune system contributing to MI recurrence. With prospective validation, the 9LncSigAMI identified by our work will provide additional diagnostic information beyond other known clinical parameters, and increase the understanding of the molecular mechanism underlying the pathogenesis of MI. PMID:27634901

  7. Integrating large-scale functional genomics data to dissect metabolic networks for hydrogen production

    SciTech Connect

    Harwood, Caroline S

    2012-12-17

    The goal of this project is to identify gene networks that are critical for efficient biohydrogen production by leveraging variation in gene content and gene expression in independently isolated Rhodopseudomonas palustris strains. Coexpression methods were applied to large data sets that we have collected to define probabilistic causal gene networks. To our knowledge this a first systems level approach that takes advantage of strain-to strain variability to computationally define networks critical for a particular bacterial phenotypic trait.

  8. Network rewiring is an important mechanism of gene essentiality change

    PubMed Central

    Kim, Jinho; Kim, Inhae; Han, Seong Kyu; Bowie, James U.; Kim, Sanguk

    2012-01-01

    Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality. PMID:23198090

  9. A Systems Genetics Approach Identifies Gene Regulatory Networks Associated with Fatty Acid Composition in Brassica rapa Seed1

    PubMed Central

    Xiao, Dong; Bucher, Johan; Jin, Mina; Boyle, Kerry; Fobert, Pierre; Maliepaard, Chris

    2016-01-01

    Fatty acids in seeds affect seed germination and seedling vigor, and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation of seed fatty acid composition in a Brassica rapa doubled haploid population from a cross between a yellow sarson oil type and a black-seeded pak choi. The distribution of major QTLs for fatty acids showed a relationship with the fatty acid types: linkage group A03 for monounsaturated fatty acids, A04 for saturated fatty acids, and A05 for polyunsaturated fatty acids. Using a genetical genomics approach, expression quantitative trait locus (eQTL) hotspots were found at major fatty acid QTLs on linkage groups A03, A04, A05, and A09. An eQTL-guided gene coexpression network of lipid metabolism-related genes showed major hubs at the genes BrPLA2-ALPHA, BrWD-40, a number of seed storage protein genes, and the transcription factor BrMD-2, suggesting essential roles for these genes in lipid metabolism. Three subnetworks were extracted for the economically important and most abundant fatty acids erucic, oleic, linoleic, and linolenic acids. Network analysis, combined with comparison of the genome positions of cis- or trans-eQTLs with fatty acid QTLs, allowed the identification of candidate genes for genetic regulation of these fatty acids. The generated insights in the genetic architecture of fatty acid composition and the underlying complex gene regulatory networks in B. rapa seeds are discussed. PMID:26518343

  10. Assessing translational efficiency by a reporter protein co-expressed in a cell-free synthesis system.

    PubMed

    Park, Yu Jin; Lee, Kyung-Ho; Kim, Dong-Myung

    2017-02-01

    We demonstrate the use of a cell-free protein synthesis system as a convenient tool for assessing the relative translational efficiencies of genes. When sfGFP was used as a common reporter gene and co-expressed with a series of target genes, the intensities of sfGFP fluorescence from the co-expression reactions were highly correlated with the individual expression levels of the co-expressed genes. The relative translational efficiencies of genes estimated by this method were reproducible when the same genes were expressed in transformed Escherichia coli, suggesting that this method could be used as a universal tool for prognostic assessment of translational efficiency.

  11. Identification of Gene Networks for Residual Feed Intake in Angus Cattle Using Genomic Prediction and RNA-seq

    PubMed Central

    Weber, Kristina L.; Welly, Bryan T.; Van Eenennaam, Alison L.; Young, Amy E.; Porto-Neto, Laercio R.; Reverter, Antonio; Rincon, Gonzalo

    2016-01-01

    Improvement in feed conversion efficiency can improve the sustainability of beef cattle production, but genomic selection for feed efficiency affects many underlying molecular networks and physiological traits. This study describes the differences between steer progeny of two influential Angus bulls with divergent genomic predictions for residual feed intake (RFI). Eight steer progeny of each sire were phenotyped for growth and feed intake from 8 mo. of age (average BW 254 kg, with a mean difference between sire groups of 4.8 kg) until slaughter at 14–16 mo. of age (average BW 534 kg, sire group difference of 28.8 kg). Terminal samples from pituitary gland, skeletal muscle, liver, adipose, and duodenum were collected from each steer for transcriptome sequencing. Gene expression networks were derived using partial correlation and information theory (PCIT), including differentially expressed (DE) genes, tissue specific (TS) genes, transcription factors (TF), and genes associated with RFI from a genome-wide association study (GWAS). Relative to progeny of the high RFI sire, progeny of the low RFI sire had -0.56 kg/d finishing period RFI (P = 0.05), -1.08 finishing period feed conversion ratio (P = 0.01), +3.3 kg^0.75 finishing period metabolic mid-weight (MMW; P = 0.04), +28.8 kg final body weight (P = 0.01), -12.9 feed bunk visits per day (P = 0.02) with +0.60 min/visit duration (P = 0.01), and +0.0045 carcass specific gravity (weight in air/weight in air—weight in water, a predictor of carcass fat content; P = 0.03). RNA-seq identified 633 DE genes between sire groups among 17,016 expressed genes. PCIT analysis identified >115,000 significant co-expression correlations between genes and 25 TF hubs, i.e. controllers of clusters of DE, TS, and GWAS SNP genes. Pathway analysis suggests low RFI bull progeny possess heightened gut inflammation and reduced fat deposition. This multi-omics analysis shows how differences in RFI genomic breeding values can impact other

  12. SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples.

    PubMed

    Yalamanchili, Hari Krishna; Li, Zhaoyuan; Wang, Panwen; Wong, Maria P; Yao, Jianfeng; Wang, Junwen

    2014-09-01

    Conventionally, overall gene expressions from microarrays are used to infer gene networks, but it is challenging to account splicing isoforms. High-throughput RNA Sequencing has made splice variant profiling practical. However, its true merit in quantifying splicing isoforms and isoform-specific exon expressions is not well explored in inferring gene networks. This study demonstrates SpliceNet, a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace. It goes beyond differentially expressed genes and infers splicing isoform network changes between normal and diseased samples. It eases the sample size bottleneck; evaluations on simulated data and lung cancer-specific ERBB2 and MAPK signaling pathways, with varying number of samples, evince the merit in handling high exon to sample size ratio datasets. Inferred network rewiring of well established Bcl-x and EGFR centered networks from lung adenocarcinoma expression data is in good agreement with literature. Gene level evaluations demonstrate a substantial performance of SpliceNet over canonical correlation analysis, a method that is currently applied to exon level RNA-Seq data. SpliceNet can also be applied to exon array data. SpliceNet is distributed as an R package available at http://www.jjwanglab.org/SpliceNet.

  13. GENIES: gene network inference engine based on supervised analysis.

    PubMed

    Kotera, Masaaki; Yamanishi, Yoshihiro; Moriya, Yuki; Kanehisa, Minoru; Goto, Susumu

    2012-07-01

    Gene network inference engine based on supervised analysis (GENIES) is a web server to predict unknown part of gene network from various types of genome-wide data in the framework of supervised network inference. The originality of GENIES lies in the construction of a predictive model using partially known network information and in the integration of heterogeneous data with kernel methods. The GENIES server accepts any 'profiles' of genes or proteins (e.g. gene expression profiles, protein subcellular localization profiles and phylogenetic profiles) or pre-calculated gene-gene similarity matrices (or 'kernels') in the tab-delimited file format. As a training data set to learn a predictive model, the users can choose either known molecular network information in the KEGG PATHWAY database or their own gene network data. The user can also select an algorithm of supervised network inference, choose various parameters in the method, and control the weights of heterogeneous data integration. The server provides the list of newly predicted gene pairs, maps the predicted gene pairs onto the associated pathway diagrams in KEGG PATHWAY and indicates candidate genes for missing enzymes in organism-specific metabolic pathways. GENIES (http://www.genome.jp/tools/genies/) is publicly available as one of the genome analysis tools in GenomeNet.

  14. Analysis of global gene expression in Brachypodium distachyon reveals extensive network plasticity in response to abiotic stress.

    PubMed

    Priest, Henry D; Fox, Samuel E; Rowley, Erik R; Murray, Jessica R; Michael, Todd P; Mockler, Todd C

    2014-01-01

    Brachypodium distachyon is a close relative of many important cereal crops. Abiotic stress tolerance has a significant impact on productivity of agriculturally important food and feedstock crops. Analysis of the transcriptome of Brachypodium after chilling, high-salinity, drought, and heat stresses revealed diverse differential expression of many transcripts. Weighted Gene Co-Expression Network Analysis revealed 22 distinct gene modules with specific profiles of expression under each stress. Promoter analysis implicated short DNA sequences directly upstream of module members in the regulation of 21 of 22 modules. Functional analysis of module members revealed enrichment in functional terms for 10 of 22 network modules. Analysis of condition-specific correlations between differentially expressed gene pairs revealed extensive plasticity in the expression relationships of gene pairs. Photosynthesis, cell cycle, and cell wall expression modules were down-regulated by all abiotic stresses. Modules which were up-regulated by each abiotic stress fell into diverse and unique gene ontology GO categories. This study provides genomics resources and improves our understanding of abiotic stress responses of Brachypodium.

  15. System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

    PubMed

    Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

    2014-01-01

    Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome

  16. Gene Network Inference and Biochemical Assessment Delineates GPCR Pathways and CREB Targets in Small Intestinal Neuroendocrine Neoplasia

    PubMed Central

    Drozdov, Ignat; Svejda, Bernhard; Gustafsson, Bjorn I.; Mane, Shrikant; Pfragner, Roswitha; Kidd, Mark; Modlin, Irvin M.

    2011-01-01

    Small intestinal (SI) neuroendocrine tumors (NET) are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including ‘Nervous system development’, ‘Immune response’, and ‘Cell-cycle’. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE) transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations). All were up-regulated (p<0.035) with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10−5 M) significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10−5 M) stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D2 and Serotonin [5-HT2] receptor agonist, 10−6 M) stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8–2-fold for isoproterenol and forskolin). Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional

  17. A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization

    PubMed Central

    Li, Jianhua; Lin, Xiaoyan; Teng, Yueyang; Qi, Shouliang; Xiao, Dayu; Zhang, Jianying; Kang, Yan

    2016-01-01

    Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein–protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary. PMID:27415759

  18. Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation

    NASA Technical Reports Server (NTRS)

    Mjolsness, Eric

    2000-01-01

    This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.

  19. Coexpression of the type I signal peptidase gene sipM increases recombinant protein production and export in Bacillus megaterium MS941.

    PubMed

    Malten, Marco; Nahrstedt, Hannes; Meinhardt, Friedhelm; Jahn, Dieter

    2005-09-05

    The removal of the signal peptide from a precursor protein is a crucial step of protein secretion. In order to improve Bacillus megaterium as protein production and secretion host, the influence of homologous type I signal peptidase SipM overproduction on recombinant Leuconostoc mesenteroides dextransucrase DsrS synthesis and export was investigated. The dsrS gene was integrated as a single copy into the chromosomal bgaM locus encoding beta-galactosidase. Desired clones were identified by blue-white selection. In this strain, the expression of sipM from a multicopy plasmid using its own promoter increased the amount of secreted DsrS 3.7-fold. This increase in protein secretion by SipM overproduction was next transferred to a high level DsrS production strain using a multicopy plasmid encoding sipM with its natural promoter and dsrS under control of a strong xylose-inducible promoter. No further increase in DsrS export were observed when this vector was carrying two sipM copies. Similarly, bicistronic sipM and dsrS high level expression did not enhance DsrS secretion, indicating the natural limitation of the approach. Interestingly, SipM-enhanced DsrS secretion also resulted in an overall increase of DsrS production.

  20. Co-expression of bacterial aspartate kinase and adenylylsulfate reductase genes substantially increases sulfur amino acid levels in transgenic alfalfa (Medicago sativa L.).

    PubMed

    Tong, Zongyong; Xie, Can; Ma, Lei; Liu, Liping; Jin, Yongsheng; Dong, Jiangli; Wang, Tao

    2014-01-01

    Alfalfa (Medicago sativa L.) is one of the most important forage crops used to feed livestock, such as cattle and sheep, and the sulfur amino acid (SAA) content of alfalfa is used as an index of its nutritional value. Aspartate kinase (AK) catalyzes the phosphorylation of aspartate to Asp-phosphate, the first step in the aspartate family biosynthesis pathway, and adenylylsulfate reductase (APR) catalyzes the conversion of activated sulfate to sulfite, providing reduced sulfur for the synthesis of cysteine, methionine, and other essential metabolites and secondary compounds. To reduce the feedback inhibition of other metabolites, we cloned bacterial AK and APR genes, modified AK, and introduced them into alfalfa. Compared to the wild-type alfalfa, the content of cysteine increased by 30% and that of methionine increased substantially by 60%. In addition, a substantial increase in the abundance of essential amino acids (EAAs), such as aspartate and lysine, was found. The results also indicated a close connection between amino acid metabolism and the tricarboxylic acid (TCA) cycle. The total amino acid content and the forage biomass tested showed no significant changes in the transgenic plants. This approach provides a new method for increasing SAAs and allows for the development of new genetically modified crops with enhanced nutritional value.

  1. Gene Networks Underlying Chronic Sleep Deprivation in Drosophila

    DTIC Science & Technology

    2014-06-15

    SECURITY CLASSIFICATION OF: Studies of the gene network affected by sleep deprivation and stress in the fruit fly Drosophila have revealed the...transduction pathways are affected. Subseuqent tests of mutants in these pathways demonstrated a strong effect on sleep maintenance. Further...15-Apr-2009 14-Apr-2013 Approved for Public Release; Distribution Unlimited Gene Networks Underlying Chronic Sleep Deprivation in Drosophila The

  2. Preparation, characterization, and in ovo vaccination of dextran-spermine nanoparticle DNA vaccine coexpressing the fusion and hemagglutinin genes against Newcastle disease.

    PubMed

    Firouzamandi, Masoumeh; Moeini, Hassan; Hosseini, Seyed Davood; Bejo, Mohd Hair; Omar, Abdul Rahman; Mehrbod, Parvaneh; El Zowalaty, Mohamed E; Webster, Thomas J; Ideris, Aini

    2016-01-01

    Plasmid DNA (pDNA)-based vaccines have emerged as effective subunit vaccines against viral and bacterial pathogens. In this study, a DNA vaccine, namely plasmid internal ribosome entry site-HN/F, was applied in ovo against Newcastle disease (ND). Vaccination was carried out using the DNA vaccine alone or as a mixture of the pDNA and dextran-spermine (D-SPM), a nanoparticle used for pDNA delivery. The results showed that in ovo vaccination with 40 μg pDNA/egg alone induced high levels of antibody titer (P<0.05) in specific pathogen-free (SPF) chickens at 3 and 4 weeks postvaccination compared to 2 weeks postvaccination. Hemagglutination inhibition (HI) titer was not significantly different between groups injected with 40 μg pDNA + 64 μg D-SPM and 40 μg pDNA at 4 weeks postvaccination (P>0.05). Higher antibody titer was observed in the group immunized with 40 μg pDNA/egg at 4 weeks postvaccination. The findings also showed that vaccination with 40 μg pDNA/egg alone was able to confer protection against Newcastle disease virus strain NDIBS002 in two out of seven SPF chickens. Although the chickens produced antibody titers 3 weeks after in ovo vaccination, it was not sufficient to provide complete protection to the chickens from lethal viral challenge. In addition, vaccination with pDNA/D-SPM complex did not induce high antibody titer when compared with naked pDNA. Therefore, it was concluded that DNA vaccination with plasmid internal ribosome entry site-HN/F can be suitable for in ovo application against ND, whereas D-SPM is not recommended for in ovo gene delivery.

  3. Exhaustive Search for Fuzzy Gene Networks from Microarray Data

    SciTech Connect

    Sokhansanj, B A; Fitch, J P; Quong, J N; Quong, A A

    2003-07-07

    Recent technological advances in high-throughput data collection allow for the study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are required to interpret large and complex data sets. Rationally designed system perturbations (e.g. gene knock-outs, metabolite removal, etc) can be used to iteratively refine hypothetical models, leading to a modeling-experiment cycle for high-throughput biological system analysis. We use fuzzy logic gene network models because they have greater resolution than Boolean logic models and do not require the precise parameter measurement needed for chemical kinetics-based modeling. The fuzzy gene network approach is tested by exhaustive search for network models describing cyclin gene interactions in yeast cell cycle microarray data, with preliminary success in recovering interactions predicted by previous biological knowledge and other analysis techniques. Our goal is to further develop this method in combination with experiments we are performing on bacterial regulatory networks.

  4. Caenorhabditis elegans metabolic gene regulatory networks govern the cellular economy.

    PubMed

    Watson, Emma; Walhout, Albertha J M

    2014-10-01

    Diet greatly impacts metabolism in health and disease. In response to the presence or absence of specific nutrients, metabolic gene regulatory networks sense the metabolic state of the cell and regulate metabolic flux accordingly, for instance by the transcriptional control of metabolic enzymes. Here, we discuss recent insights regarding metazoan metabolic regulatory networks using the nematode Caenorhabditis elegans as a model, including the modular organization of metabolic gene regulatory networks, the prominent impact of diet on the transcriptome and metabolome, specialized roles of nuclear hormone receptors (NHRs) in responding to dietary conditions, regulation of metabolic genes and metabolic regulators by miRNAs, and feedback between metabolic genes and their regulators.

  5. Efficient reverse-engineering of a developmental gene regulatory network.

    PubMed

    Crombach, Anton; Wotton, Karl R; Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  6. Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

    PubMed Central

    Cicin-Sain, Damjan; Ashyraliyev, Maksat; Jaeger, Johannes

    2012-01-01

    Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to

  7. On the robustness of complex heterogeneous gene expression networks.

    PubMed

    Gómez-Gardeñes, Jesús; Moreno, Yamir; Floría, Luis M

    2005-04-01

    We analyze a continuous gene expression model on the underlying topology of a complex heterogeneous network. Numerical simulations aimed at studying the chaotic and periodic dynamics of the model are performed. The results clearly indicate that there is a region in which the dynamical and structural complexity of the system avoid chaotic attractors. However, contrary to what has been reported for Random Boolean Networks, the chaotic phase cannot be completely suppressed, which has important bearings on network robustness and gene expression modeling.

  8. DCGL v2.0: An R Package for Unveiling Differential Regulation from Differential Co-expression

    PubMed Central

    Liu, Bao-Hong; Zhao, Zhongming; Liu, Lei; Ma, Liang-Xiao; Li, Yi-Xue; Li, Yuan-Yuan

    2013-01-01

    Motivation Differential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner. Results To advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named “Differential Regulation Analysis” (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject. Conclusions With ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical. Availability DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp. PMID

  9. Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

    PubMed Central

    Emmert-Streib, Frank; Dehmer, Matthias; Haibe-Kains, Benjamin

    2014-01-01

    In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine. PMID:25364745

  10. Computational inference of gene regulatory networks: Approaches, limitations and opportunities.

    PubMed

    Banf, Michael; Rhee, Seung Y

    2017-01-01

    Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

  11. Ethanol production from acid- and alkali-pretreated corncob by endoglucanase and β-glucosidase co-expressing Saccharomyces cerevisiae subject to the expression of heterologous genes and nutrition added.

    PubMed

    Feng, Chunying; Zou, Shaolan; Liu, Cheng; Yang, Huajun; Zhang, Kun; Ma, Yuanyuan; Hong, Jiefang; Zhang, Minhua

    2016-05-01

    Low-cost technologies to overcome the recalcitrance of cellulose are the key to widespread utilization of lignocellulosic biomass for ethanol production. Efficient enzymatic hydrolysis of cellulose requires the synergism of various cellulases, and the ratios of each cellulase are required to be regulated to achieve the maximum hydrolysis. On the other hand, engineering of cellulolytic Saccharomyces cerevisiae strains is a promising strategy for lignocellulosic ethanol production. The expression of cellulase-encoding genes in yeast would affect the synergism of cellulases and thus the fermentation ability of strains with exogenous enzyme addition. However, such researches are rarely reported. In this study, ten endoglucanase and β-glucosidase co-expressing S. cerevisiae strains were constructed and evaluated by enzyme assay and fermentation performance measurement. The results showed that: (1) maximum ethanol titers of recombinant strains exhibited high variability in YPSC medium (20 g/l peptone, 10 g/l yeast extract, 100 g/l acid- and alkali-pretreated corncob) within 10 days. However, they had relatively little difference in USC medium (100 g/l acid- and alkali-pretreated corncob, 0.33 g/l urea, pH 5.0). (2) Strains 17# and 19#, with ratio (CMCase to β-glucosidase) of 7.04 ± 0.61 and 7.40 ± 0.71 respectively, had the highest fermentation performance in YPSC. However, strains 11# and 3# with the highest titers in USC medium had a higher ratio of CMCase to β-glucosidase, and CMCase activities. These results indicated that nutrition, enzyme activities and the ratio of heterologous enzymes had notable influence on the fermentation ability of cellulase-expressing yeast.

  12. Approaches for recognizing disease genes based on network.

    PubMed

    Zou, Quan; Li, Jinjin; Wang, Chunyu; Zeng, Xiangxiang

    2014-01-01

    Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.

  13. A novel computational approach for the prediction of networked transcription factors of aryl hydrocarbon-receptor-regulated genes.

    PubMed

    Kel, Alexander; Reymann, Susanne; Matys, Volker; Nettesheim, Paul; Wingender, Edgar; Borlak, Jürgen

    2004-12-01

    A novel computational method based on a genetic algorithm was developed to study composite structure of promoters of coexpressed genes. Our method enabled an identification of combinations of multiple transcription factor binding sites regulating the concerted expression of genes. In this article, we study genes whose expression is regulated by a ligand-activated transcription factor, aryl hydrocarbon receptor (AhR), that mediates responses to a variety of toxins. AhR-mediated change in expression of AhR target genes was measured by oligonucleotide microarrays and by reverse transcription-polymerase chain reaction in human and rat hepatocytes. Promoters and long-distance regulatory regions (>10 kb) of AhR-responsive genes were analyzed by the genetic algorithm and a variety of other computational methods. Rules were established on the local oligonucleotide context in the flanks of the AhR binding sites, on the occurrence of clusters of AhR recognition elements, and on the presence in the promoters of specific combinations of multiple binding sites for the transcription factors cooperating in the AhR regulatory network. Our rules were applied to search for yet unknown Ah-receptor target genes. Experimental evidence is presented to demonstrate high fidelity of this novel in silico approach.

  14. Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis

    PubMed Central

    Bonthala, Venkata Suresh; Mayes, Katie; Moreton, Joanna; Blythe, Martin; Wright, Victoria; May, Sean Tobias; Massawe, Festo; Mayes, Sean; Twycross, Jamie

    2016-01-01

    Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties. PMID:26859686

  15. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    PubMed

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  16. Evolvability and hierarchy in rewired bacterial gene networks

    PubMed Central

    Isalan, Mark; Lemerle, Caroline; Michalodimitrakis, Konstantinos; Beltrao, Pedro; Horn, Carsten; Raineri, Emanuele; Garriga-Canut, Mireia; Serrano, Luis

    2009-01-01

    Sequencing DNA from several organisms has revealed that duplication and drift of existing genes have primarily molded the contents of a given genome. Though the effect of knocking out or over-expressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network evolvability, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or σ-factor genes in Escherichia coli, added over a wild-type genetic background. Here we show that ~95% of new networks are tolerated by the bacteria, that very few alter growth, and that expression level correlates with factor position in the wild-type network hierarchy. Most importantly, we find that certain networks consistently survive over the wild-type under various selection pressures. Therefore new links in the network are rarely a barrier for evolution and can even confer a fitness advantage. PMID:18421347

  17. Inferring slowly-changing dynamic gene-regulatory networks

    PubMed Central

    2015-01-01

    Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experiments are designed in order to tease out temporal changes in the underlying network. It is typically reasonable to assume that changes in genomic networks are few, because biological systems tend to be stable. We introduce a new model for estimating slow changes in dynamic gene-regulatory networks, which is suitable for high-dimensional data, e.g. time-course microarray data. Our aim is to estimate a dynamically changing genomic network based on temporal activity measurements of the genes in the network. Our method is based on the penalized likelihood with ℓ1-norm, that penalizes conditional dependencies between genes as well as differences between conditional independence elements across time points. We also present a heuristic search strategy to find optimal tuning parameters. We re-write the penalized maximum likelihood problem into a standard convex optimization problem subject to linear equality constraints. We show that our method performs well in simulation studies. Finally, we apply the proposed model to a time-course T-cell dataset. PMID:25917062

  18. Functional Gene Networks: R/Bioc package to generate and analyse gene networks derived from functional enrichment and clustering

    PubMed Central

    Aibar, Sara; Fontanillo, Celia; Droste, Conrad; De Las Rivas, Javier

    2015-01-01

    Summary: Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use. Availability and implementation: FGNet is available in Bioconductor, including a tutorial. URL: http://bioconductor.org/packages/release/bioc/html/FGNet.html Contact: jrivas@usal.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25600944

  19. GINI: From ISH Images to Gene Interaction Networks

    PubMed Central

    Puniyani, Kriti; Xing, Eric P.

    2013-01-01

    Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and

  20. Reverse engineering of gene regulatory networks: a comparative study.

    PubMed

    Hache, Hendrik; Lehrach, Hans; Herwig, Ralf

    2009-01-01

    Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

  1. Time-Delayed Models of Gene Regulatory Networks

    PubMed Central

    Parmar, K.; Blyuss, K. B.; Kyrychko, Y. N.; Hogan, S. J.

    2015-01-01

    We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems. PMID:26576197

  2. Variable neighborhood search for reverse engineering of gene regulatory networks.

    PubMed

    Nicholson, Charles; Goodwin, Leslie; Clark, Corey

    2017-01-01

    A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.

  3. Regulatory gene networks and the properties of the developmental process

    NASA Technical Reports Server (NTRS)

    Davidson, Eric H.; McClay, David R.; Hood, Leroy

    2003-01-01

    Genomic instructions for development are encoded in arrays of regulatory DNA. These specify large networks of interactions among genes producing transcription factors and signaling components. The architecture of such networks both explains and predicts developmental phenomenology. Although network analysis is yet in its early stages, some fundamental commonalities are already emerging. Two such are the use of multigenic feedback loops to ensure the progressivity of developmental regulatory states and the prevalence of repressive regulatory interactions in spatial control processes. Gene regulatory networks make it possible to explain the process of development in causal terms and eventually will enable the redesign of developmental regulatory circuitry to achieve different outcomes.

  4. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks.

    PubMed

    Szedlak, Anthony; Smith, Nicholas; Liu, Li; Paternostro, Giovanni; Piermarocchi, Carlo

    2016-06-01

    The diverse, specialized genes present in today's lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins' binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes' evolutionary properties. Slowly evolving ("cold"), old genes tend to interact with each other, as do rapidly evolving ("hot"), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN's community structures and its genes' evolutionary properties provide new perspectives for understanding evolutionary genetics.

  5. A Maize Gene Regulatory Network for Phenolic Metabolism.

    PubMed

    Yang, Fan; Li, Wei; Jiang, Nan; Yu, Haidong; Morohashi, Kengo; Ouma, Wilberforce Zachary; Morales-Mantilla, Daniel E; Gomez-Cano, Fabio Andres; Mukundi, Eric; Prada-Salcedo, Luis Daniel; Velazquez, Roberto Alers; Valentin, Jasmin; Mejía-Guerra, Maria Katherine; Gray, John; Doseff, Andrea I; Grotewold, Erich

    2017-03-06

    The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.

  6. Analysis of Gene Sets Based on the Underlying Regulatory Network

    PubMed Central

    Michailidis, George

    2009-01-01

    Abstract Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast. PMID:19254181

  7. Phenotype accessibility and noise in random threshold gene regulatory networks.

    PubMed

    Pinho, Ricardo; Garcia, Victor; Feldman, Marcus W

    2014-01-01

    Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes.

  8. Identifying gene regulatory network rewiring using latent differential graphical models

    PubMed Central

    Tian, Dechao; Gu, Quanquan; Ma, Jian

    2016-01-01

    Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions. PMID:27378774

  9. Gene duplication models for directed networks with limits on growth

    NASA Astrophysics Data System (ADS)

    Enemark, Jakob; Sneppen, Kim

    2007-11-01

    Background: Duplication of genes is important for evolution of molecular networks. Many authors have therefore considered gene duplication as a driving force in shaping the topology of molecular networks. In particular it has been noted that growth via duplication would act as an implicit means of preferential attachment, and thereby provide the observed broad degree distributions of molecular networks. Results: We extend current models of gene duplication and rewiring by including directions and the fact that molecular networks are not a result of unidirectional growth. We introduce upstream sites and downstream shapes to quantify potential links during duplication and rewiring. We find that this in itself generates the observed scaling of transcription factors for genome sites in prokaryotes. The dynamical model can generate a scale-free degree distribution, p(k)\\propto 1/k^{\\gamma } , with exponent γ = 1 in the non-growing case, and with γ>1 when the network is growing. Conclusions: We find that duplication of genes followed by substantial recombination of upstream regions could generate features of genetic regulatory networks. Our steady state degree distribution is however too broad to be consistent with data, thereby suggesting that selective pruning acts as a main additional constraint on duplicated genes. Our analysis shows that gene duplication can only be a main cause for the observed broad degree distributions if there are also substantial recombinations between upstream regions of genes.

  10. microRNA and gene networks in human laryngeal cancer

    PubMed Central

    ZHANG, FENGYU; XU, ZHIWEN; WANG, KUNHAO; SUN, LINLIN; LIU, GENGHE; HAN, BAIXU

    2015-01-01

    Genes and microRNAs (miRNAs) are considered to be key biological factors in human carcinogenesis. To date, considerable data have been obtained regarding genes and miRNAs in cancer; however, the regulatory mechanisms associated with the genes and miRNAs in cancer have yet to be fully elucidated. The aim of the present study was to use the key genes and miRNAs associated with laryngeal cancer (LC) to construct three regulatory networks (differentially expressed, LC-related and global). A network topology of the development of LC, involving 10 differentially expressed miRNAs and 55 differentially expressed genes, was obtained. These genes exhibited multiple identities, including target genes of miRNA, transcription factors (TFs) and host genes. The key regulatory interactions were determined by comparing the similarities and differences among the three networks. The nodes and pathways in LC, as well as the association between each pair of factors within the networks, such as TFs and miRNA, miRNA and target genes and miRNA and its host gene, were discussed. The mechanisms of LC involved certain key pathways featuring self-adaptation regulation and nodes without direct predecessors or successors. The findings of the present study have further elucidated the pathogenesis of LC and are likely to be beneficial for future research into LC. PMID:26668624

  11. Gene regulatory network inference using out of equilibrium statistical mechanics

    PubMed Central

    Benecke, Arndt

    2008-01-01

    Spatiotemporal control of gene expression is fundamental to multicellular life. Despite prodigious efforts, the encoding of gene expression regulation in eukaryotes is not understood. Gene expression analyses nourish the hope to reverse engineer effector-target gene networks using inference techniques. Inference from noisy and circumstantial data relies on using robust models with few parameters for the underlying mechanisms. However, a systematic path to gene regulatory network reverse engineering from functional genomics data is still impeded by fundamental problems. Recently, Johannes Berg from the Theoretical Physics Institute of Cologne University has made two remarkable contributions that significantly advance the gene regulatory network inference problem. Berg, who uses gene expression data from yeast, has demonstrated a nonequilibrium regime for mRNA concentration dynamics and was able to map the gene regulatory process upon simple stochastic systems driven out of equilibrium. The impact of his demonstration is twofold, affecting both the understanding of the operational constraints under which transcription occurs and the capacity to extract relevant information from highly time-resolved expression data. Berg has used his observation to predict target genes of selected transcription factors, and thereby, in principle, demonstrated applicability of his out of equilibrium statistical mechanics approach to the gene network inference problem. PMID:19404429

  12. Genomewide Expression and Functional Interactions of Genes under Drought Stress in Maize

    PubMed Central

    Sharma, Rinku; Singh, Nidhi; Mohan, Sweta; Mittal, Swati; Mittal, Shikha; Mallikarjuna, Mallana Gowdra; Rao, Atmakuri Ramakrishna; Dash, Prasanta Kumar; Hossain, Firoz; Gupta, Hari Shanker

    2017-01-01

    A genomewide transcriptome assay of two subtropical genotypes of maize was used to observe the expression of genes at seedling stage of drought stress. The number of genes expressed differentially was greater in HKI1532 (a drought tolerant genotype) than in PC3 (a drought sensitive genotype), indicating primary differences at the transcriptional level in stress tolerance. The global coexpression networks of the two genotypes differed significantly with respect to the number of modules and the coexpression pattern within the modules. A total of 174 drought-responsive genes were selected from HKI1532, and their coexpression network revealed key correlations between different adaptive pathways, each cluster of the network representing a specific biological function. Transcription factors related to ABA-dependent stomatal closure, signalling, and phosphoprotein cascades work in concert to compensate for reduced photosynthesis. Under stress, water balance was maintained by coexpression of the genes involved in osmotic adjustments and transporter proteins. Metabolism was maintained by the coexpression of genes involved in cell wall modification and protein and lipid metabolism. The interaction of genes involved in crucial biological functions during stress was identified and the results will be useful in targeting important gene interactions to understand drought tolerance in greater detail. PMID:28326315

  13. Systems Approaches to Identifying Gene Regulatory Networks in Plants

    PubMed Central

    Long, Terri A.; Brady, Siobhan M.; Benfey, Philip N.

    2009-01-01

    Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed. PMID:18616425

  14. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks

    PubMed Central

    Szedlak, Anthony; Smith, Nicholas; Liu, Li; Paternostro, Giovanni; Piermarocchi, Carlo

    2016-01-01

    The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes’ evolutionary properties. Slowly evolving (“cold”), old genes tend to interact with each other, as do rapidly evolving (“hot”), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN’s community structures and its genes’ evolutionary properties provide new perspectives for understanding evolutionary genetics. PMID:27359334

  15. Listening to the noise: random fluctuations reveal gene network parameters.

    PubMed

    Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

    2009-01-01

    The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations show cell-to-cell variability that can manifest significant phenotypic differences. Noise-induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We show that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.

  16. Gene Networks and Functional Features of Gravitropic response in Rice Shoot Bases

    NASA Astrophysics Data System (ADS)

    Hu, Liwei; Zang, Aiping; Ai, Qianru; Chen, Haiying; Li, Lin; Li, Rui; Su, Feng; Chen, Xijiang; Rong, Hui; Dou, Xianying; Reinhold-Hurek, Barbara; Li, Qi; Cai, Weiming

    To delineate key genes and the corresponding physiological functions as well as the coordina-tion of genes involved in the gravitropism of rice shoot bases, we used whole-genome microarray analysis of upper and lower parts of rice shoot bases at 0.5 h and 6 h after gravistimulation. And bio-information analysis was applied including GO-analysis, expression tendency and net-work analysis. In the lower shoot bases, auxin-mediated signaling pathway and glutathione transferase activity with the biggest enrichment were activated at 0.5 h, while cytokinin stimu-lus and photosynthesis were activated at 6 h. Meanwhile, several processes were suppressed in the lower shoot bases, including: xyloglucan:xyloglucosyl transferase activity, glucan metabolic processes, and ATPase activity at 0.5 h; and tRNA isopentenyltransferase activity, and chiti-nase activity, etc. at 6 h. Gene expression profile responding to gravistimulation suggested that the asymmetrically activation of several phytohormone signaling pathways including auxin, gib-berellin and cytokinin brassinolide ethylene and cytokinin-related genes were involved in the differentially growth between the upper and lower parts of rice shoot bases, and so do cell wall-related genes. Topological analysis of the coexpression networks revealed the core statue of AY177699.1(apetala3-like protein) and AK105103.1 at 0.5 h; AK062612.1 (ethylene response factor) and AK099932.1 (lectin-like receptor kinase 72) at 6 h. All the core factors have the function "response to endogenous stimulus". Additionally, AK108057.1(similar to germin-like protein precursor) was discovered as the most important core gene in the upper shoot bases in 6h after gravistimualtion while AK067424.1(cellulose synthase-like protein), AK120101.1 (Zinc finger, B-box domain containing protein) and CR278698 (ATPase associated with various cel-lular activities cellulose synthase-like protein) contribute equally to gravitropic response in the lower shoot bases.

  17. Dynamics of gene regulatory networks with cell division cycle

    NASA Astrophysics Data System (ADS)

    Chen, Luonan; Wang, Ruiqi; Kobayashi, Tetsuya J.; Aihara, Kazuyuki

    2004-07-01

    This paper focuses on modeling and analyzing the nonlinear dynamics of gene regulatory networks with the consideration of a cell division cycle with duplication process of DNA , in particular for switches and oscillators of synthetic networks. We derive two models that may correspond to the eukaryotic and prokaryotic cells, respectively. A biologically plausible three-gene model ( lac,tetR , and cI ) and a repressilator as switch and oscillator examples are used to illustrate our theoretical results. We show that the cell cycle may play a significant role in gene regulation due to the nonlinear dynamics of a gene regulatory network although gene expressions are usually tightly controlled by transcriptional factors.

  18. Fused Regression for Multi-source Gene Regulatory Network Inference

    PubMed Central

    Lam, Kari Y.; Westrick, Zachary M.; Müller, Christian L.; Christiaen, Lionel; Bonneau, Richard

    2016-01-01

    Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms. PMID:27923054

  19. Network of Cancer Genes (NCG 3.0): integration and analysis of genetic and network properties of cancer genes.

    PubMed

    D'Antonio, Matteo; Pendino, Vera; Sinha, Shruti; Ciccarelli, Francesca D

    2012-01-01

    The identification of a constantly increasing number of genes whose mutations are causally implicated in tumor initiation and progression (cancer genes) requires the development of tools to store and analyze them. The Network of Cancer Genes (NCG 3.0) collects information on 1494 cancer genes that have been found mutated in 16 different cancer types. These genes were collected from the Cancer Gene Census as well as from 18 whole exome and 11 whole-genome screenings of cancer samples. For each cancer gene, NCG 3.0 provides a summary of the gene features and the cross-reference to other databases. In addition, it describes duplicability, evolutionary origin, orthology, network properties, interaction partners, microRNA regulation and functional roles of cancer genes and of all genes that are related to them. This integrated network of information can be used to better characterize cancer genes in the context of the system in which they act. The data can also be used to identify novel candidates that share the same properties of known cancer genes and may therefore play a similar role in cancer. NCG 3.0 is freely available at http://bio.ifom-ieo-campus.it/ncg.

  20. SLC9A9 Co-expression modules in autism-associated brain regions.

    PubMed

    Patak, Jameson; Hess, Jonathan L; Zhang-James, Yanli; Glatt, Stephen J; Faraone, Stephen V

    2016-07-21

    SLC9A9 is a sodium hydrogen exchanger present in the recycling endosome and highly expressed in the brain. It is implicated in neuropsychiatric disorders, including autism spectrum disorders (ASDs). Little research concerning its gene expression patterns and biological pathways has been conducted. We sought to investigate its possible biological roles in autism-associated brain regions throughout development. We conducted a weighted gene co-expression network analysis on RNA-seq data downloaded from Brainspan. We compared prenatal and postnatal gene expression networks for three ASD-associated brain regions known to have high SLC9A9 gene expression. We also performed an ASD-associated single nucleotide polymorphism enrichment analysis and a cell signature enrichment analysis. The modules showed differences in gene constituents (membership), gene number, and connectivity throughout time. SLC9A9 was highly associated with immune system functions, metabolism, apoptosis, endocytosis, and signaling cascades. Gene list comparison with co-immunoprecipitation data was significant for multiple modules. We found a disproportionately high autism risk signal among genes constituting the prenatal hippocampal module. The modules were enriched with astrocyte and oligodendrocyte markers. SLC9A9 is potentially involved in the pathophysiology of ASDs. Our investigation confirmed proposed functions for SLC9A9, such as endocytosis and immune regulation, while also revealing potential roles in mTOR signaling and cell survival.. By providing a concise molecular map and interactions, evidence of cell type and implicated brain regions we hope this will guide future research on SLC9A9. Autism Res 2016. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

  1. Topological origin of global attractors in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Zhang, YunJun; Ouyang, Qi; Geng, Zhi

    2015-02-01

    Fixed-point attractors with global stability manifest themselves in a number of gene regulatory networks. This property indicates the stability of regulatory networks against small state perturbations and is closely related to other complex dynamics. In this paper, we aim to reveal the core modules in regulatory networks that determine their global attractors and the relationship between these core modules and other motifs. This work has been done via three steps. Firstly, inspired by the signal transmission in the regulation process, we extract the model of chain-like network from regulation networks. We propose a module of "ideal transmission chain (ITC)", which is proved sufficient and necessary (under certain condition) to form a global fixed-point in the context of chain-like network. Secondly, by examining two well-studied regulatory networks (i.e., the cell-cycle regulatory networks of Budding yeast and Fission yeast), we identify the ideal modules in true regulation networks and demonstrate that the modules have a superior contribution to network stability (quantified by the relative size of the biggest attraction basin). Thirdly, in these two regulation networks, we find that the double negative feedback loops, which are the key motifs of forming bistability in regulation, are connected to these core modules with high network stability. These results have shed new light on the connection between the topological feature and the dynamic property of regulatory networks.

  2. GENIUS: web server to predict local gene networks and key genes for biological functions.

    PubMed

    Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

    2016-12-19

    GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods.

  3. Gene regulation: hacking the network on a sugar high.

    PubMed

    Ellis, Tom; Wang, Xiao; Collins, James J

    2008-04-11

    In a recent issue of Molecular Cell, Kaplan et al. (2008) determine the input functions for 19 E. coli sugar-utilization genes by using a two-dimensional high-throughput approach. The resulting input-function map reveals that gene network regulation follows non-Boolean, and often nonmonotonic, logic.

  4. Gene-based and semantic structure of the Gene Ontology as a complex network

    NASA Astrophysics Data System (ADS)

    Coronnello, Claudia; Tumminello, Michele; Miccichè, Salvatore

    2016-09-01

    The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The Gene Ontology (GO) is constantly evolving over time. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. Here we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium. Moreover, the GO is a natural example of bipartite network of terms and genes. Here we are interested in studying the properties of the projected network of terms, i.e. a gene-based weighted network of GO terms, in which a link between any two terms is set if at least one gene is annotated in both terms. One aim of the present paper is to compare the structural properties of the semantic and the gene-based network. The relative importance of terms is very similar in the two networks, but the community structure changes. We show that in some cases GO terms that appear to be distinct from a semantic point of view are instead connected, and appear in the same community when considering their gene content. The identification of such gene-based communities of terms might therefore be the basis of a simple protocol aiming at improving the semantic structure of GO. Information about terms that share large gene content might also be important from a biomedical point of view, as it might reveal how genes over-expressed in a certain term also affect other biological processes, molecular functions and cellular components not directly linked according to GO semantics.

  5. Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

    PubMed Central

    CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

    2014-01-01

    Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

  6. Gene regulatory network clustering for graph layout based on microarray gene expression data.

    PubMed

    Kojima, Kaname; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

    2010-01-01

    We propose a statistical model realizing simultaneous estimation of gene regulatory network and gene module identification from time series gene expression data from microarray experiments. Under the assumption that genes in the same module are densely connected, the proposed method detects gene modules based on the variational Bayesian technique. The model can also incorporate existing biological prior knowledge such as protein subcellular localization. We apply the proposed model to the time series data from a synthetically generated network and verified the effectiveness of the proposed model. The proposed model is also applied the time series microarray data from HeLa cell. Detected gene module information gives the great help on drawing the estimated gene network.

  7. Optimal finite horizon control in gene regulatory networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiuli

    2013-06-01

    As a paradigm for modeling gene regulatory networks, probabilistic Boolean networks (PBNs) form a subclass of Markov genetic regulatory networks. To date, many different stochastic optimal control approaches have been developed to find therapeutic intervention strategies for PBNs. A PBN is essentially a collection of constituent Boolean networks via a probability structure. Most of the existing works assume that the probability structure for Boolean networks selection is known. Such an assumption cannot be satisfied in practice since the presence of noise prevents the probability structure from being accurately determined. In this paper, we treat a case in which we lack the governing probability structure for Boolean network selection. Specifically, in the framework of PBNs, the theory of finite horizon Markov decision process is employed to find optimal constituent Boolean networks with respect to the defined objective functions. In order to illustrate the validity of our proposed approach, an example is also displayed.

  8. Identification of cancer-related genes and motifs in the human gene regulatory network.

    PubMed

    Carson, Matthew B; Gu, Jianlei; Yu, Guangjun; Lu, Hui

    2015-08-01

    The authors investigated the regulatory network motifs and corresponding motif positions of cancer-related genes. First, they mapped disease-related genes to a transcription factor regulatory network. Next, they calculated statistically significant motifs and subsequently identified positions within these motifs that were enriched in cancer-related genes. Potential mechanisms of these motifs and positions are discussed. These results could be used to identify other disease- and cancer-related genes and could also suggest mechanisms for how these genes relate to co-occurring diseases.

  9. An extensive network of coupling among gene expression machines.

    PubMed

    Maniatis, Tom; Reed, Robin

    2002-04-04

    Gene expression in eukaryotes requires several multi-component cellular machines. Each machine carries out a separate step in the gene expression pathway, which includes transcription, several pre-messenger RNA processing steps and the export of mature mRNA to the cytoplasm. Recent studies lead to the view that, in contrast to a simple linear assembly line, a complex and extensively coupled network has evolved to coordinate the activities of the gene expression machines. The extensive coupling is consistent with a model in which the machines are tethered to each other to form 'gene expression factories' that maximize the efficiency and specificity of each step in gene expression.

  10. Replaying the evolutionary tape: biomimetic reverse engineering of gene networks.

    PubMed

    Marbach, Daniel; Mattiussi, Claudio; Floreano, Dario

    2009-03-01

    In this paper, we suggest a new approach for reverse engineering gene regulatory networks, which consists of using a reconstruction process that is similar to the evolutionary process that created these networks. The aim is to integrate prior knowledge into the reverse-engineering procedure, thus biasing the search toward biologically plausible solutions. To this end, we propose an evolutionary method that abstracts and mimics the natural evolution of gene regulatory networks. Our method can be used with a wide range of nonlinear dynamical models. This allows us to explore novel model types such as the log-sigmoid model introduced here. We apply the biomimetic method to a gold-standard dataset from an in vivo gene network. The obtained results won a reverse engineering competition of the second DREAM conference (Dialogue on Reverse Engineering Assessments and Methods 2007, New York, NY).

  11. Linear control theory for gene network modeling.

    PubMed

    Shin, Yong-Jun; Bleris, Leonidas

    2010-09-16

    Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

  12. A complex network analysis of hypertension-related genes

    NASA Astrophysics Data System (ADS)

    Wang, Huan; Xu, Chuan-Yun; Hu, Jing-Bo; Cao, Ke-Fei

    2014-01-01

    In this paper, a network of hypertension-related genes is constructed by analyzing the correlations of gene expression data among the Dahl salt-sensitive rat and two consomic rat strains. The numerical calculations show that this sparse and assortative network has small-world and scale-free properties. Further, 16 key hub genes (Col4a1, Lcn2, Cdk4, etc.) are determined by introducing an integrated centrality and have been confirmed by biological/medical research to play important roles in hypertension.

  13. Gene network and pathway generation and analysis: Editorial

    SciTech Connect

    Zhao, Zhongming; Sanfilippo, Antonio P.; Huang, Kun

    2011-02-18

    The past decade has witnessed an exponential growth of biological data including genomic sequences, gene annotations, expression and regulation, and protein-protein interactions. A key aim in the post-genome era is to systematically catalogue gene networks and pathways in a dynamic living cell and apply them to study diseases and phenotypes. To promote the research in systems biology and its application to disease studies, we organized a workshop focusing on the reconstruction and analysis of gene networks and pathways in any organisms from high-throughput data collected through techniques such as microarray analysis and RNA-Seq.

  14. Identification of Gene Networks: An Approach Based on Mathematical Modeling

    DTIC Science & Technology

    2014-08-21

    applied the sensitivity method to a five-gene subnetwork of Escherichia coli and obtained promising preliminary experimental results. (a) Papers... Escherichia coli : ompR, flhC, flhD, flgA, and flgC. The regulatory interactions among these genes have been previously discovered and are part of the...Network Identification. (Submitted). 3 Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR

  15. Gene regulatory networks modelling using a dynamic evolutionary hybrid

    PubMed Central

    2010-01-01

    Background Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. Results The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes. Conclusions The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks. The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/. PMID:20298548

  16. Coexpression of neuronatin splice forms promotes medulloblastoma growth.

    PubMed

    Siu, I-Mei; Bai, Renyuan; Gallia, Gary L; Edwards, Jennifer B; Tyler, Betty M; Eberhart, Charles G; Riggins, Gregory J

    2008-10-01

    Medulloblastoma (MB) is the most common pediatric brain cancer. Several important developmental pathways have been implicated in MB formation, but fewer therapeutic targets have been identified. To locate frequently overexpressed genes, we performed a comprehensive gene expression survey of MB. Our comparison of 20 primary tumors to normal cerebellum identified neuronatin (NNAT) as the most frequently overexpressed gene in our analysis. NNAT is a neural-specific developmental gene with alpha and beta splice forms. Functional evaluation revealed that RNA interference knockdown of NNAT causes a significant decrease in proliferation. Conversely, coexpression of both splice forms in NNAT-negative MB cell lines increased proliferation, caused a significant shift from G(1) to G(2)/M, and increased soft agar colony formation and size. When expressed individually, each NNAT splice form had much less effect on these in vitro oncogenic predictors. In an in vivo model, the coexpression of both splice forms conferred the ability of xenograft formation to human MB cells that do not normally form xenografts, whereas a control gene had no effect. Our findings suggest that the frequently observed overexpression of both NNAT splice forms in MB enhances growth in this cancer.

  17. The incorporation of epigenetics in artificial gene regulatory networks.

    PubMed

    Turner, Alexander P; Lones, Michael A; Fuente, Luis A; Stepney, Susan; Caves, Leo S D; Tyrrell, Andy M

    2013-05-01

    Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

  18. Development of a synthetic gene network to modulate gene expression by mechanical forces

    PubMed Central

    Kis, Zoltán; Rodin, Tania; Zafar, Asma; Lai, Zhangxing; Freke, Grace; Fleck, Oliver; Del Rio Hernandez, Armando; Towhidi, Leila; Pedrigi, Ryan M.; Homma, Takayuki; Krams, Rob

    2016-01-01

    The majority of (mammalian) cells in our body are sensitive to mechanical forces, but little work has been done to develop assays to monitor mechanosensor activity. Furthermore, it is currently impossible to use mechanosensor activity to drive gene expression. To address these needs, we developed the first mammalian mechanosensitive synthetic gene network to monitor endothelial cell shear stress levels and directly modulate expression of an atheroprotective transcription factor by shear stress. The technique is highly modular, easily scalable and allows graded control of gene expression by mechanical stimuli in hard-to-transfect mammalian cells. We call this new approach mechanosyngenetics. To insert the gene network into a high proportion of cells, a hybrid transfection procedure was developed that involves electroporation, plasmids replication in mammalian cells, mammalian antibiotic selection, a second electroporation and gene network activation. This procedure takes 1 week and yielded over 60% of cells with a functional gene network. To test gene network functionality, we developed a flow setup that exposes cells to linearly increasing shear stress along the length of the flow channel floor. Activation of the gene network varied logarithmically as a function of shear stress magnitude. PMID:27404994

  19. A Synthesis Method of Gene Networks Having Cyclic Expression Pattern Sequences by Network Learning

    NASA Astrophysics Data System (ADS)

    Mori, Yoshihiro; Kuroe, Yasuaki

    Recently, synthesis of gene networks having desired functions has become of interest to many researchers because it is a complementary approach to understanding gene networks, and it could be the first step in controlling living cells. There exist several periodic phenomena in cells, e.g. circadian rhythm. These phenomena are considered to be generated by gene networks. We have already proposed synthesis method of gene networks based on gene expression. The method is applicable to synthesizing gene networks possessing the desired cyclic expression pattern sequences. It ensures that realized expression pattern sequences are periodic, however, it does not ensure that their corresponding solution trajectories are periodic, which might bring that their oscillations are not persistent. In this paper, in order to resolve the problem we propose a synthesis method of gene networks possessing the desired cyclic expression pattern sequences together with their corresponding solution trajectories being periodic. In the proposed method the persistent oscillations of the solution trajectories are realized by specifying passing points of them.

  20. microRNA and gene networks in human pancreatic cancer

    PubMed Central

    ZHU, MINGHUI; XU, ZHIWEN; WANG, KUNHAO; WANG, NING; LI, YANG

    2013-01-01

    To date, scientists have obtained a substantial amount of knowledge with regard to genes and microRNAs (miRNAs) in pancreatic cancer (PC). However, deciphering the regulatory mechanism of these genes and miRNAs remains difficult. In the present study, three regulatory networks consisting of a differentially-expressed network, a related network and a global network, were constructed in order to identify the mechanisms and certain key miRNA and gene pathways in PC. The interactions between transcription factors (TFs) and miRNAs, miRNAs and target genes and an miRNA and its host gene were investigated. The present study compared and analyzed the similarities and differences between the three networks in order to distinguish the key pathways. Certain pathways involving the differentially-expressed genes and miRNAs demonstrated specific features. TP53 and hsa-miR-125b were observed to form a self-adaptation association. A further 16 significant differentially-expressed miRNAs were obtained and it was observed that an miRNA and its host gene exhibit specific features in PC, for example, hsa-miR-196a-1 and its host gene, HOXB7, form a self-adaptation association. The differentially-expressed network partially illuminated the mechanism of PC. The present study provides comprehensive data that is associated with PC and may aid future studies in obtaining pertinent data results with regards to PC. In the future, an improved understanding of PC may be obtained through an increased knowledge of the occurrence, mechanism, improvement, metastasis and treatment of the disease. PMID:24137477

  1. Hidden hysteresis – population dynamics can obscure gene network dynamics

    PubMed Central

    2013-01-01

    Background Positive feedback is a common motif in gene regulatory networks. It can be used in synthetic networks as an amplifier to increase the level of gene expression, as well as a nonlinear module to create bistable gene networks that display hysteresis in response to a given stimulus. Using a synthetic positive feedback-based tetracycline sensor in E. coli, we show that the population dynamics of a cell culture has a profound effect on the observed hysteretic response of a population of cells with this synthetic gene circuit. Results The amount of observable hysteresis in a cell culture harboring the gene circuit depended on the initial concentration of cells within the culture. The magnitude of the hysteresis observed was inversely related to the dilution procedure used to inoculate the subcultures; the higher the dilution of the cell culture, lower was the observed hysteresis of that culture at steady state. Although the behavior of the gene circuit in individual cells did not change significantly in the different subcultures, the proportion of cells exhibiting high levels of steady-state gene expression did change. Although the interrelated kinetics of gene expression and cell growth are unpredictable at first sight, we were able to resolve the surprising dilution-dependent hysteresis as a result of two interrelated phenomena - the stochastic switching between the ON and OFF phenotypes that led to the cumulative failure of the gene circuit over time, and the nonlinear, logistic growth of the cell in the batch culture. Conclusions These findings reinforce the fact that population dynamics cannot be ignored in analyzing the dynamics of gene networks. Indeed population dynamics may play a significant role in the manifestation of bistability and hysteresis, and is an important consideration when designing synthetic gene circuits intended for long-term application. PMID:23800122

  2. Solution of the quasispecies model for an arbitrary gene network

    NASA Astrophysics Data System (ADS)

    Tannenbaum, Emmanuel; Shakhnovich, Eugene I.

    2004-08-01

    In this paper, we study the equilibrium behavior of Eigen’s quasispecies equations for an arbitrary gene network. We consider a genome consisting of N genes, so that the full genome sequence σ may be written as σ=σ1σ2⋯σN , where σi are sequences of individual genes. We assume a single fitness peak model for each gene, so that gene i has some “master” sequence σi,0 for which it is functioning. The fitness landscape is then determined by which genes in the genome are functioning and which are not. The equilibrium behavior of this model may be solved in the limit of infinite sequence length. The central result is that, instead of a single error catastrophe, the model exhibits a series of localization to delocalization transitions, which we term an “error cascade.” As the mutation rate is increased, the selective advantage for maintaining functional copies of certain genes in the network disappears, and the population distribution delocalizes over the corresponding sequence spaces. The network goes through a series of such transitions, as more and more genes become inactivated, until eventually delocalization occurs over the entire genome space, resulting in a final error catastrophe. This model provides a criterion for determining the conditions under which certain genes in a genome will lose functionality due to genetic drift. It also provides insight into the response of gene networks to mutagens. In particular, it suggests an approach for determining the relative importance of various genes to the fitness of an organism, in a more accurate manner than the standard “deletion set” method. The results in this paper also have implications for mutational robustness and what C.O. Wilke termed “survival of the flattest.”

  3. Modularity and evolutionary constraints in a baculovirus gene regulatory network

    PubMed Central

    2013-01-01

    Background The structure of regulatory networks remains an open question in our understanding of complex biological systems. Interactions during complete viral life cycles present unique opportunities to understand how host-parasite network take shape and behave. The Anticarsia gemmatalis multiple nucleopolyhedrovirus (AgMNPV) is a large double-stranded DNA virus, whose genome may encode for 152 open reading frames (ORFs). Here we present the analysis of the ordered cascade of the AgMNPV gene expression. Re