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.
Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics.
通过整合基序扫描和表达动态的证据,揭示巨噬细胞转录程序
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| 期刊: | PLoS Computational Biology | 影响因子: | 3.600 |
| 时间: | 2008 | 起止号: | 2008 Mar 21; 4(3):e1000021 |
| doi: | 10.1371/journal.pcbi.1000021 | 研究方向: | 细胞生物学 |
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