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Transcriptional Network Dynamics in Macrophage Activation
Roland Nilsson1,2, Vladimir B. Bajic3, Harukazu Suzuki4, Diego di Bernardo5, Johan Björkegren1,2, Shintaro Katayama4, James F. Reid7, Matthew J. Sweet8, Manuela Gariboldi7, Piero Carninci4,5, Yosihide Hayashizaki4,6, David A. Hume6,8,*, Jesper Tegner1,2,* and Timothy Ravasi4,6,9,*.
1Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden, 2Computational Biology, IFM, Linkoping University, Linkoping, Sweden. 3Knowledge Extraction Lab, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. 4Genome Exploration Research Group (Genome Network Project Core Group), RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan, 5Telethon Institute for Genetics and Medicine, Naples, Italy, 6Genome Science Laboratory, Discovery Research Institute, RIKEN Wako Institute, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan, 7Department of Experimental Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milan, Italy, 8ARC Special Research Centre for Functional and Applied Genomics, Institute for Molecular Bioscience, University of Queensland, Brisbane QLD 4072, Australia, and 9Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
* Corresponding authors
Status: Submitted
Abstract:
Transcriptional regulation is central to cell differentiation and response to external stimuli. In mouse, the complete genome is known and a large part of the transcriptome has recently been sequenced, facilitating genome-wide studies of mechanisms of transcriptional regulation. Herein, we present such a study of the mouse macrophage response to bacterial lipopolysaccharide (LPS).
We combine time-series cap analysis of gene expression (CAGE) with in-silico prediction of transcription factor binding sites (TFBSs) to infer regulatory networks specific to the macrophage LPS response, and we study the dynamic properties of these networks using combined microarray and qPCR time-series expression data. We demonstrate that genes with similar promoter structures tend to be co-expressed and functionally related. By a novel clustering approach based on promoter structure, we discover sub-networks that describe how signalling pathways change dynamically during the progress of the macrophage LPS response, defining regulatory modules characteristic of the inflammatory response.
Expression clustering alone is not sufficient to decode the complex, dynamical networks of transcriptional regulation. Rather, integration of many different data types will be instrumental in understanding these mechanisms. For the first time we have demonstrated in a mammalian model system, the mouse macrophage, that the new approach of combining different techniques and data provides convincing and consistent biological answers in reconstructed parts of dynamic transcriptional regulatory networks. Finally, our integrative analysis enabled us to suggest novel roles for the transcription factor ATF-3 and NRF-2 during inflammatory response.
The system approach presented here will have general applications in understanding cellular differentiation in higher eukaryotes.
Keywords:
System Biology; Regulatory Networks; Network Dynamics; Complex Systems; Transcriptional Regulation; Genome; Macrophages; Innate Immunity; Inflammation. |
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