Sexually-dimorphic targeting of functionally-related genes in COPD.
Sexually-dimorphic targeting of functionally-related genes in COPD.
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DOI：
10.1186/s12918-014-0118-y
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发表时间：
2014-11-28
影响因子：
--
通讯作者：
DeMeo DL
中科院分区：
文献类型：
--
作者：
Glass K;Quackenbush J;Silverman EK;Celli B;Rennard SI;Yuan GC;DeMeo DL
越来越多的证据表明，许多疾病在男性和女性中的发展，进展和对治疗的反应不同。这种变异性可能表现为基因调控网络中性别特异性结构的结果，这些结构影响这些网络的运作方式。然而，很少有方法来识别和表征网络结构的差异，减缓了理解驱动两性异形机制的进展。在这里，我们应用一个综合的网络推理方法，PANDA（传递属性之间的网络数据同化），从慢性阻塞性肺疾病（COPD）受试者的血液和痰液样本中的性别特异性网络建模。我们使用了一种折刀式的方法来为每种性别建立一个可能的网络集合。通过采用统计方法来比较这些网络集合，我们能够识别与功能相关的基因集相关的强差异靶向模式，包括那些参与线粒体功能和能量代谢的基因。网络分析还确定了这些途径的几个潜在的性别和疾病特异性转录调节因子。网络分析深入了解了COPD中驱动性二态性的潜在机制，这些机制仅从基因表达分析中并不明显。我们相信，我们的网络分析集成方法提供了一种原则性的方法来捕获性别特异性的调控关系，并可应用于识别各种疾病和背景下的基因调控模式的差异。本文的在线版本（doi：10.1186/s12918-014-0118-y）包含补充材料，可供授权用户使用。
There is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods to identify and characterize differences in network structure, slowing progress in understanding mechanisms driving sexual dimorphism. Here we apply an integrative network inference method, PANDA (Passing Attributes between Networks for Data Assimilation), to model sex-specific networks in blood and sputum samples from subjects with Chronic Obstructive Pulmonary Disease (COPD). We used a jack-knifing approach to build an ensemble of likely networks for each sex. By adapting statistical methods to compare these network ensembles, we were able to identify strong differential-targeting patterns associated with functionally-related sets of genes, including those involved in mitochondrial function and energy metabolism. Network analysis also identified several potential sex- and disease-specific transcriptional regulators of these pathways. Network analysis yielded insight into potential mechanisms driving sexual dimorphism in COPD that were not evident from gene expression analysis alone. We believe our ensemble approach to network analysis provides a principled way to capture sex-specific regulatory relationships and could be applied to identify differences in gene regulatory patterns in a wide variety of diseases and contexts. The online version of this article (doi:10.1186/s12918-014-0118-y) contains supplementary material, which is available to authorized users.
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影响因子：
3.8
作者：
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通讯作者：
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作者：
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