Abstract
Differential coexpression analysis is emerging as a complement to conventional differential gene expression analysis. The identified differential coexpression links can be assembled into a differential coexpression network (DCEN) in response to environmental stresses or genetic changes. Differential coexpression analyses have been successfully used to identify condition-specific modules; however, the structural properties and biological significance of general DCENs have not been well investigated. Here, we analyzed two independent Saccharomyces cerevisiae DCENs constructed from large-scale time-course gene expression profiles in response to different situations. Topological analyses show that DCENs are tree-like networks possessing scale-free characteristics, but not small-world. Functional analyses indicate that differentially coexpressed gene pairs in DCEN tend to link different biological processes, achieving complementary or synergistic effects. Furthermore, the gene pairs lacking common transcription factors are sensitive to perturbation and hence lead to differential coexpression. Based on these observations, we integrated transcriptional regulatory information into DCEN and identified transcription factors that might cause differential coexpression by gain or loss of activation in response to different situations. Collectively, our results not only uncover the unique structural characteristics of DCEN but also provide new insights into interpretation of DCEN to reveal its biological significance and infer the underlying gene regulatory dynamics.
Highlights
Molecular interactions can change dramatically in response to different conditions, such as environmental stresses and genetic changes
We investigated the structural characteristics and biological significance of differential coexpression network (DCEN) and used time-course gene expression data to construct coexpression networks and obtain DCEN by comparing the networks between two biological conditions
For the second data set (Dataset 2, Gene Expression Omnibus (GEO) accession GSE3635 and GSE5283), the gene expression profiling of wild-type and strains with deleted YOX1 and YHP1 was performed to understand the regulation of transcription factor YOX1 and YHP1 during the cell cycle of Saccharomyces cerevisiae[23]
Summary
Molecular interactions can change dramatically in response to different conditions, such as environmental stresses and genetic changes. Understanding of network dynamics has been achieved to some extent by integrating static networks with gene expression profiles[5]. These approaches are typically unable to identify new interactions that are condition-specific. Coexpression networks are typically constructed from gene expression data using correlation-based inference methods. These networks have been commonly used to reveal gene functions and investigate gene regulatory systems[9,10]. To understand the dynamics of cellular regulation, differential coexpression analysis incorporating regulatory changes between different conditions is emerging. We offered a computational method to identify differential activation of transcription factors inferred from DCENs
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