An integrative ChIP-chip and gene expression profiling to model SMAD regulatory modules
- Equal contributors
1 Human Cancer Genetics Program, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA
2 Division of Medical Technology, School of Allied Medical Professions, The Ohio State University, Columbus, OH 43210, USA
3 Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
4 Medical Sciences, Indiana University School of Medicine, Bloomington, IN 47405, USA
5 Department of Life Science and Institute of Molecular Biology, National Chung Cheng University, Min-Hsiung, Chia-Yi 621, Taiwan, Republic of China
6 Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, PR China
7 Center for Systems and Computational Biology, Molecular and Cellular Oncogenesis Program, The Wistar Institute, Philadelphia, PA, USA
BMC Systems Biology 2009, 3:73 doi:10.1186/1752-0509-3-73Published: 17 July 2009
The TGF-β/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-β/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context.
To address this question and develop a computational model for SMAD modules, we simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-β/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-β activation. We developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1).
Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.