Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

Statistics, University of California, Los Angeles, CA 90095, USA.
BMC Genomics (Impact Factor: 3.99). 08/2009; 10(1):327. DOI: 10.1186/1471-2164-10-327
Source: PubMed


Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes.
We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation.
Our systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology.

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    • "In gene differential coexpression analysis, a pair of gene expression datasets under disease and normal conditions is transformed to a pair of coexpression matrix in which links represent transcriptionally correlated gene pairs [5]. Until now, methods for differential coexpression analysis of gene expression data have been extensively researched, and multiple algorithms have been developed and tested [9] [10] [11] [12]. "
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    ABSTRACT: 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.
    09/2015; 2015:836929. DOI:10.1155/2015/836929
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    • "The first step is to calculate the Pearson correlation of TCs between all pairs of ICNs, r ij . To distinguish between positive and negative correlations, we do not use the absolute value of the correlation, but compute a signed similarity measure defined as (Mason et al., 2009; Mumford et al., 2010; Yu et al., 2011a) "
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    ABSTRACT: Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. Copyright © 2014 Elsevier Inc. All rights reserved.
    NeuroImage 12/2014; 107. DOI:10.1016/j.neuroimage.2014.12.020 · 6.36 Impact Factor
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    • "While traditional methods compare pair wise measurements, WGCNA can describe the correlation patterns among a series of transcript measurements across multiple microarrays. WGCNA was developed to find modules/clusters of highly correlated genes and link modules to quantitative traits, which has been successfully applied in a variety of different studies recently 33, 34, 37-41. "
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    ABSTRACT: Background: Muscle development and lipid metabolism play important roles during fetal development stages. The commercial Texel sheep are more muscular than the indigenous Ujumqin sheep. Results: We performed serial transcriptomics assays and systems biology analyses to investigate the dynamics of gene expression changes associated with fetal longissimus muscles during different fetal stages in two sheep breeds. Totally, we identified 1472 differentially expressed genes during various fetal stages using time-series expression analysis. A systems biology approach, weighted gene co-expression network analysis (WGCNA), was used to detect modules of correlated genes among these 1472 genes. Dramatically different gene modules were identified in four merged datasets, corresponding to the mid fetal stage in Texel and Ujumqin sheep, the late fetal stage in Texel and Ujumqin sheep, respectively. We further detected gene modules significantly correlated with fetal weight, and constructed networks and pathways using genes with high significances. In these gene modules, we identified genes like TADA3, LMNB1, TGF-β3, EEF1A2, FGFR1, MYOZ1, and FBP2 correlated with fetal weight. Conclusion: Our study revealed the complex network characteristics involved in muscle development and lipid metabolism during fetal development stages. Diverse patterns of the network connections observed between breeds and fetal stages could involve some hub genes, which play central roles in fetal development, correlating with fetal weight. Our findings could provide potential valuable biomarkers for selection of body weight-related traits in sheep and other livestock.
    International journal of biological sciences 09/2014; 10(9):1039-50. DOI:10.7150/ijbs.9737 · 4.51 Impact Factor
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