Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.
Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.
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超越模块和集线器：基因共表达网络研究复杂脑疾病的分子机制的潜力。
DOI：
10.1111/gbb.12106
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发表时间：
2014-01
期刊：
影响因子：
--
通讯作者：
Sibille E
中科院分区：
文献类型：
--
作者：
Gaiteri C;Ding Y;French B;Tseng GC;Sibille E
在一个以简化论方法研究大脑疾病机制的研究环境中，基因网络分析提供了一个补充框架，在其中处理神经精神和其他神经疾病中发生的复杂的失调。基因-基因表达相关性是分子网络的常见来源，因为它们可以从高维疾病数据中提取，并封装了多个调控系统的活动。然而，基因共表达模式的分析通常被视为一个机械的黑匣子，在这个黑匣子里，隐约可见的“中枢基因”直接领导着细胞网络，而其他特征则被掩盖了。通过研究共表达的生物物理基础和疾病中发生的基因调控变化，最近的研究表明，将共表达网络作为一种多组学筛选程序来产生疾病机制的新假说是可能的。由于技术处理步骤会影响共表达网络的结果和解释，我们检查了共表达分析的常见模式的假设和替代方案，并讨论了其他主题，如用于共表达分析的可接受数据集、模块的稳健识别、与疾病相关的基因和分子系统的优先顺序以及网络荟萃分析。为了加速超越模块和中心的共表达研究，我们强调了一些与复杂脑部疾病特别相关的共表达网络研究的新兴方向，包括中心性-致命性关系、与机器学习方法的集成和网络药理学。
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.
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