使用系统生物学方法来预测先天免疫系统中的新参与者

Bin Li
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引用次数: 8

摘要

toll样受体(TLRs)是先天免疫应答病原体的关键参与者。然而,对TLR激活途径中的转录调控机制的描述仍然相对较差。为了解决这个问题,本章的作者采用了一种系统的方法来预测在不同TLR刺激下暂时调节差异表达基因的转录因子。时间过程微阵列数据取自6种TLR激动剂刺激的小鼠骨髓源性巨噬细胞。差异调控基因根据其动态行为聚类。然后,作者开发了一种计算方法来识别每个簇中的位置重叠转录因子(TF)结合位点,从而预测可能调控这些基因的TF。第二个微阵列数据集,关于野生型,Myd88-/和Trif-/巨噬细胞,通过脂多糖(LPS)刺激,为这种联合方法提供支持证据。总的来说,作者能够识别已知的TLR tf,并预测可能参与TLR信号传导的新tf。DOI: 10.4018 / 978 - 1 - 4666 - 3604 - 0. - ch037
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Systems Biology Approaches to Predict New Players in the Innate Immune System
Toll-like receptors (TLRs) are critical players in the innate immune response to pathogens. However, transcriptional regulatory mechanisms in the TLR activation pathways are still relatively poorly characterized. To address this question, the author of this chapter applied a systematic approach to predict transcription factors that temporally regulate differentially expressed genes under diverse TLR stimuli. Time-course microarray data were selected from mouse bone marrow-derived macrophages stimulated by six TLR agonists. Differentially regulated genes were clustered on the basis of their dynamic behavior. The author then developed a computational method to identify positional overlapping transcription factor (TF) binding sites in each cluster, so as to predict possible TFs that may regulate these genes. A second microarray dataset, on wild-type, Myd88-/and Trif-/macrophages stimulated by lipopolysaccharide (LPS), was used to provide supporting evidence on this combined approach. Overall, the author was able to identify known TLR TFs, as well as to predict new TFs that may be involved in TLR signaling. DOI: 10.4018/978-1-4666-3604-0.ch037
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