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Review
. 2014 Jan;13(1):13-24.
doi: 10.1111/gbb.12106. Epub 2013 Dec 10.

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

C Gaiteri  1 , Y Ding, B French, G C Tseng, E Sibille
Affiliations
Review

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

C Gaiteri et al. Genes Brain Behav. 2014 Jan.

Abstract

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene-gene expression correlations are a common source of molecular networks because they can be extracted from high-dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming 'hub genes' direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi-omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease-related prioritization of genes and molecular systems and network meta-analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality-lethality relationship, integration with machine learning approaches and network pharmacology.

Keywords: Coexpression; complex diseases; coregulation; depression; gene; hub; module; network; post-mortem; regulatory network; small-world; transcriptome.

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Figures

Figure 1
Figure 1. Summary of molecular, cellular, tissue and technical regulatory sources of observed gene-gene correlations/coexpression links
Various biological activities (depicted in outer shapes) can influence the expression of two or more genes and yield correlated expression patterns, denoted as “coexpression links”. Hence coexpression links reflect the converging influences of these genetic, biochemical and environmental factors, and are thus informative of the biological state of an individual. The relative proportion of links from these various sources (depicted by small arrows) has not been surveyed in a consistent experimental system, and may vary for each gene. Furthermore, technical and cell-type variability can easily generate correlated expression patterns which are indistinguishable from “biological” sources of coexpression, such as epigenetic regulation. Therefore, when interpreting coexpression networks, it is helpful to separate gene-gene correlations with likely biological origins versus those which are related to overarching technical factors such as batch effects.
Figure 2
Figure 2. Gene expression patterns translate regulatory changes into networks links
Gene expression patterns can change in several ways between control and disease samples, beyond standard differential expression (purple line). The variance of a gene’s expression may be altered in disease with or without differential expression (red gene expression profile) (Ho et al., 2008). Similarly altered gene-gene correlations in disease can occur with or without changes in expression (Hudson et al., 2009). A potential mechanism mediating the loss of gene-gene correlations in the disease state, through disrupted transcription factor (TF) binding, is shown on the right.
Figure 3
Figure 3. Multi-scale mapping of gene expression traits and coexpression networks
Disease-related gene expression traits can be aggregated at various scales in the context of coexpression networks. A) Coexpression links stem from multiple sources, but aggregate into an approximately scale-free network. B) Global coexpression networks may be decomposed into groups of coexpressed gene through many different clustering methods. These clusters are overlapping and may be generated by multiple regulatory systems. C) Because coexpressed gene sets tend to have similar functions, they may be useful bins in which to assess the most disease-impacted systems. D) Final selection of disease or potential therapeutic targets can integrate information from all scales to identify genes at the center of complex regulatory changes. E) Changes in any of the regulatory systems that create coexpression may be reflected in differentially coexpressed links, genes or modules that are enriched in coexpressed links. Finding the source of differential coexpression requires additional data drawn from scientific literature or ideally assessed experimentally in the same model system (represented by color-coded arrows for disease-specific coexpression links).
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