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通过整合基序扫描和表达动态的证据来揭示巨噬细胞转录程序。

Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics.

作者信息

Ramsey Stephen A, Klemm Sandy L, Zak Daniel E, Kennedy Kathleen A, Thorsson Vesteinn, Li Bin, Gilchrist Mark, Gold Elizabeth S, Johnson Carrie D, Litvak Vladimir, Navarro Garnet, Roach Jared C, Rosenberger Carrie M, Rust Alistair G, Yudkovsky Natalya, Aderem Alan, Shmulevich Ilya

机构信息

Institute for Systems Biology, Seattle, Washington, United States of America.

出版信息

PLoS Comput Biol. 2008 Mar 21;4(3):e1000021. doi: 10.1371/journal.pcbi.1000021.

Abstract

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation.

摘要

巨噬细胞是多功能免疫细胞,可通过其Toll样受体(TLR)检测多种病原体相关分子模式。响应微生物挑战时,经TLR刺激的巨噬细胞会经历一个由动态诱导的转录调控网络控制的激活程序。绘制复杂的哺乳动物转录网络面临重大挑战,需要整合多种实验数据类型。在这项工作中,我们推断了TLR刺激的小鼠巨噬细胞激活背后的转录网络。基于微阵列的表达谱分析和转录因子结合位点基序扫描被用于推断转录因子基因与共表达靶基因簇之间的关联网络。时间滞后相关性用于分析时间表达数据,以识别网络中的潜在因果影响。开发了一种新颖的统计测试来评估时间滞后相关性的显著性。使用靶向芯片上染色质免疫沉淀实验验证了所得推断网络中的几种关联。该网络纳入了已知的调节因子,并深入了解了巨噬细胞激活的转录控制。我们的分析确定了一种可能在巨噬细胞激活中起作用的新型调节因子(TGIF1)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f6b/2265556/8db445160970/pcbi.1000021.g001.jpg

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