Functional Analysis and Characterization of Differential Coexpression Networks.
Functional Analysis and Characterization of Differential Coexpression Networks.
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DOI：
10.1038/srep13295
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发表时间：
2015-08-18
影响因子：
4.6
通讯作者：
Huang HC
中科院分区：
文献类型：
--
作者：
Hsu CL;Juan HF;Huang HC
差异共表达分析是对传统差异基因表达分析的补充。识别的差异共表达链可以组装成差异共表达网络(DCEN)，以响应环境胁迫或遗传变化。差异共表达分析已经成功地用于识别特定条件的模块；然而，一般DCEN的结构特性和生物学意义还没有得到很好的研究。在这里，我们分析了两个独立的酿酒酵母DCEN，这些DCEN是从大规模的时间进程基因表达谱构建的，以响应不同的情况。拓扑分析表明，DCEN是树状网络，具有无标度特性，但不具有小世界特性。功能分析表明，DCEN中差异共表达的基因对往往将不同的生物学过程联系在一起，实现互补或协同效应。此外，缺乏共同转录因子的基因对对扰动敏感，从而导致差异共表达。基于这些观察，我们将转录调控信息整合到DCEN中，并确定了可能通过激活的获得或丢失来对不同情况做出反应而导致差异共表达的转录因子。总之，我们的结果不仅揭示了DCEN独特的结构特征，而且为解释DCEN提供了新的见解，以揭示其生物学意义并推断潜在的基因调控动态。
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.