Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics Export

@article{Ramsey2008, abstract = {Author SummaryMacrophages play a vital role in host defense against infection by recognizing pathogens through pattern recognition receptors, such as the Toll-like receptors (TLRs), and mounting an immune response. Stimulation of TLRs initiates a complex transcriptional program in which induced transcription factor genes dynamically regulate downstream genes. Microarray-based transcriptional profiling has proved useful for mapping such transcriptional programs in simpler model organisms; however, mammalian systems present difficulties such as post-translational regulation of transcription factors, combinatorial gene regulation, and a paucity of available gene-knockout expression data. Additional evidence sources, such as DNA sequence-based identification of transcription factor binding sites, are needed. In this work, we computationally inferred a transcriptional network for TLR-stimulated murine macrophages. Our approach combined sequence scanning with time-course expression data in a probabilistic framework. Expression data were analyzed using the time-lagged correlation. A novel, unbiased method was developed to assess the significance of the time-lagged correlation. The inferred network of associations between transcription factor genes and co-expressed gene clusters was validated with targeted ChIP-on-chip experiments, and yielded insights into the macrophage activation program, including a potential novel regulator. Our general approach could be used to analyze other complex mammalian systems for which time-course expression data are available.}, author = {Ramsey, Stephen A. and Klemm, Sandy L. and Zak, Daniel E. and Kennedy, Kathleen A. and Thorsson, Vesteinn and Li, Bin and Gilchrist, Mark and Gold, Elizabeth S. and Johnson, Carrie D. and Litvak, Vladimir and Navarro, Garnet and Roach, Jared C. and Rosenberger, Carrie M. and Rust, Alistair G. and Yudkovsky, Natalya and Aderem, Alan and Shmulevich, Ilya}, citeulike-article-id = {4785792}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000021}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18369420}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18369420}, day = {21}, doi = {10.1371/journal.pcbi.1000021}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {biology, network, system, time-course}, month = {March}, number = {3}, pages = {e1000021+}, posted-at = {2009-07-27 20:42:45}, priority = {2}, publisher = {Public Library of Science}, title = {Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000021}, volume = {4}, year = {2008} }

TY - JOUR ID - Ramsey2008 L3 - citeulike-article-id:4785792 N2 - Author SummaryMacrophages play a vital role in host defense against infection by recognizing pathogens through pattern recognition receptors, such as the Toll-like receptors (TLRs), and mounting an immune response. Stimulation of TLRs initiates a complex transcriptional program in which induced transcription factor genes dynamically regulate downstream genes. Microarray-based transcriptional profiling has proved useful for mapping such transcriptional programs in simpler model organisms; however, mammalian systems present difficulties such as post-translational regulation of transcription factors, combinatorial gene regulation, and a paucity of available gene-knockout expression data. Additional evidence sources, such as DNA sequence-based identification of transcription factor binding sites, are needed. In this work, we computationally inferred a transcriptional network for TLR-stimulated murine macrophages. Our approach combined sequence scanning with time-course expression data in a probabilistic framework. Expression data were analyzed using the time-lagged correlation. A novel, unbiased method was developed to assess the significance of the time-lagged correlation. The inferred network of associations between transcription factor genes and co-expressed gene clusters was validated with targeted ChIP-on-chip experiments, and yielded insights into the macrophage activation program, including a potential novel regulator. Our general approach could be used to analyze other complex mammalian systems for which time-course expression data are available. IS - 3 JF - PLoS Comput Biol SN - 1553-7358 TI - Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics PB - Public Library of Science VL - 4 SP - e1000021 KW - biology KW - network KW - system KW - time-course AU - Ramsey, Stephen AU - Klemm, Sandy AU - Zak, Daniel AU - Kennedy, Kathleen AU - Thorsson, Vesteinn AU - Li, Bin AU - Gilchrist, Mark AU - Gold, Elizabeth AU - Johnson, Carrie AU - Litvak, Vladimir AU - Navarro, Garnet AU - Roach, Jared AU - Rosenberger, Carrie AU - Rust, Alistair AU - Yudkovsky, Natalya AU - Aderem, Alan AU - Shmulevich, Ilya PY - 2008/03/21/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000021 ER -