Hu and Gallo BMC Genomics 2010, 11:49 http://www.biomedcentral.com/1471-2164/11/49 R E S E A R C H A R T I C L E Zihua Hu1*, Steven M Gallo2 Abstract Open Access Identification of interacting transcription factors regulating tissue gene expression in human Background: Tissue gene expression is generally regulated by multiple transcription factors (TFs). A major first step toward understanding how tissues achieve their specificity is to identify, at the genome scale, interacting TFs regulating gene expression in different tissues. Despite previous discoveries, the mechanisms that control tissue gene expression are not fully understood. Results: We have integrated a function conservation approach, which is based on evolutionary conservation of biological function, and genes with highest expression level in human tissues to predict TF pairs controlling tissue gene expression. To this end, we have identified 2549 TF pairs associated with a certain tissue. To find interacting TFs controlling tissue gene expression in a broad spatial and temporal manner, we looked for TF pairs common to the same type of tissues and identified 379 such TF pairs, based on which TF-TF interaction networks were further built. We also found that tissue-specific TFs may play an important role in recruiting non-tissue-specific TFs to the TF-TF interaction network, offering the potential for coordinating and controlling tissue gene expression across a variety of conditions. Conclusion: The findings from this study indicate that tissue gene expression is regulated by large sets of interacting TFs either on the same promoter of a gene or through TF-TF interaction networks. Background Transcriptional regulation in eukaryotic organisms is a fundamental process to determine a gene’s spatial and temporal expression. One of the main events involved in this process is the binding of TFs to short DNA motifs, called transcription factor binding sites (TFBSs), on the promoter regions of genes, activating or repressing the transcription machinery. In mammalian tissues most TFs do not act alone, but work through combinatorial regulation [1,2], in which two or more TFs work syner- gistically to control individual gene expression. This combinatorial regulation is able to increase the specifi- city and flexibility of genes in controlling tissue develop- ment and differentiation. Therefore, one of the major first steps toward understanding how tissues achieve their specificity is to identify interacting TFs regulating gene expression in different tissues. * Correspondence: zihuahu@ccr.buffalo.edu 1Center for Computational Research, New York State Center of Excellence in Bioinformatics & Life Sciences, Department of Biostatistics, Department of Medicine, State University of New York (SUNY), Buffalo, NY 14260, USA Early attempts to identify interacting TFs controlling tissue gene expression came from the use of experimen- tal approaches such as gel retardation assay [3], site- directed mutagenesis [4], chromatin immunoprecipita- tion [5,6], and genomic microarrays [5,6] in tissues such as liver [3,5-8], pancreas [6], immune systems [9,10], muscle [11-13], and neural stem cells [14]. In these stu- dies, interactions between TFs were discovered on a lim- ited scale. To overcome this limitation, some researchers built models to predict tissue-specific cis-regulatory modules in liver [15,16] and muscle [17] tissues. Taking advantage of the unprecedented amount of sequence and gene expression information from the most recent technical and experimental advances, a few researchers have developed computational approaches to predict tis- sue-specific TFs and cis-regulatory modules based on recognizable sequence features from either highly expressed genes [18] or genes expressed only in a parti- cular tissue [19-21] derived from genome-wide gene- expression profiling. Some of these researchers have defined tissue-specific enhancers by combining gene- expression profiling, genome comparison, and TFBS © 2010 Hu and Gallo; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.