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

Authors :
Stephen A Ramsey
Sandy L Klemm
Daniel E Zak
Kathleen A Kennedy
Vesteinn Thorsson
Bin Li
Mark Gilchrist
Elizabeth S Gold
Carrie D Johnson
Vladimir Litvak
Garnet Navarro
Jared C Roach
Carrie M Rosenberger
Alistair G Rust
Natalya Yudkovsky
Alan Aderem
Ilya Shmulevich
Source :
PLoS Computational Biology, Vol 4, Iss 3, p e1000021 (2008)
Publication Year :
2008
Publisher :
Public Library of Science (PLoS), 2008.

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.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X, 15537358, and 23037415
Volume :
4
Issue :
3
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.bac8d3e23037415b81b9fb156935223c
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1000021