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Open Access Research article

Identification of interacting transcription factors regulating tissue gene expression in human

Zihua Hu1* and Steven M Gallo2

Author Affiliations

1 Center 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

2 Center for Computational Research, New York State Center of Excellence in Bioinformatics & Life Sciences, State University of New York (SUNY), Buffalo, NY 14260, USA

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BMC Genomics 2010, 11:49  doi:10.1186/1471-2164-11-49

Published: 19 January 2010

Abstract

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.