Computational identification of key biological modules and transcription factors in acute lung injury
Computational identification of key biological modules and transcription factors in acute lung injury
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DOI：
10.1164/rccm.200509-1473oc
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发表时间：
2006-03-15
影响因子：
24.7
通讯作者：
Altemeier, WA
中科院分区：
文献类型：
--
作者：
Gharib, SA;Liles, WC;Altemeier, WA
原理：机械通气会加重细菌制品引起的急性肺损伤(ALI)。这种协同作用的分子机制尚不完全清楚。目的：我们试图开发一个计算框架来系统地识别ALI中激活的基因调控网络。方法：我们建立了一种小鼠模型，在该模型中，机械通气和气管内毒素联合使用比单独使用任何一种方法对肺的损伤都要大得多。结果：通过整合表达谱、基因本体论和启动子分析，我们构建了ALI中重要激活过程的大规模调控模块图谱。这张图谱将差异表达的基因分配给高度过度表达的生物模块，包括“防御反应”、“免疫反应”和“氧化还原酶活性”。然后将这些模块系统地整合到一个基因调控网络中，该网络由可能的转录因子组成，如干扰素刺激反应元件、IRF7和Sp1，这些转录因子可能调节参与ALI发病的关键过程。结论：我们提出了一种新的、公正的、强大的计算方法来研究机械通气和内毒素在促进ALI中的协同作用。我们的方法适用于任何涉及真核生物的表达谱实验。
Rationale: Mechanical ventilation augments the acute lung injury (ALI) caused by bacterial products. The molecular pathogenesis of this synergistic interaction remains incompletely understood.Objective: We sought to develop a computational framework to systematically identify gene regulatory networks activated in ALI.Methods: We have developed a mouse model in which the combination of mechanical ventilation and intratracheal LPS produces significantly more injury to the lung than either insult alone. We used global gene ontology analysis to determine overrepresented biological modules and computational transcription factor analysis to identify putative regulatory factors involved in this model of ALI.Results: By integrating expression profiling with gene ontology and promoter analysis, we constructed a large-scale regulatory modular map of the important processes activated in ALI. This map assigned differentially expressed genes to highly overrepresented biological modules, including "defense response," "immune response," and "oxidoreductase activity." These modules were then systematically incorporated into a gene regulatory network that consisted of putative transcription factors, such as IFN-stimulated response element, IRF7, and Sp1, that may regulate critical processes involved in the pathogenesis of ALI.Conclusions: We present a novel, unbiased, and powerful computational approach to investigate the synergistic effects of mechanical ventilation and LPS in promoting ALI. Our methodology is applicable to any expression profiling experiment involving eukaryotic organisms.