Nilsson Roland, Bajic Vladimir B, Suzuki Harukazu, di Bernardo Diego, Björkegren Johan, Katayama Shintaro, Reid James F, Sweet Matthew J, Gariboldi Manuela, Carninci Piero, Hayashizaki Yosihide, Hume David A, Tegner Jesper, Ravasi Timothy
Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden.
Genomics. 2006 Aug;88(2):133-42. doi: 10.1016/j.ygeno.2006.03.022. Epub 2006 May 12.
Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.
转录调控网络控制细胞分化以及细胞对外界刺激的反应。然而，哺乳动物模型系统尚未可用于网络分析。在此，我们展示了对小鼠巨噬细胞对细菌脂多糖（LPS）反应背后转录调控的全基因组网络分析。揭示网络结构的关键在于我们将基因表达的时间序列帽分析与转录因子结合位点的计算机预测相结合。通过将微阵列和qPCR时间序列表达数据与启动子分析相结合，我们发现了动态子网络，这些子网络描述了巨噬细胞LPS反应过程中信号通路如何动态变化，从而定义了炎症反应的特征性调控模块。特别是，我们的综合分析使我们能够提出转录因子ATF - 3和NRF - 2在炎症反应中的新作用。我们相信，我们在此提出的系统方法适用于理解高等真核生物中的细胞分化。
