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Nucleic Acids Research Advance Access originally published online on September 18, 2006
Nucleic Acids Research 2006 34(17):4925-4936; doi:10.1093/nar/gkl595
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Nucleic Acids Research, 2006, Vol. 34, No. 17 4925-4936
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues

Xueping Yu1, Jimmy Lin1, Donald J. Zack1,2,3,4 and Jiang Qian1,*

1 Wilmer Institute, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA 2 Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA 3 Department of Neuroscience, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA 4 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA

*To whom correspondence should be addressed. Tel: +1 443 287 3882; Fax: 1+ 410 502 5382; Email: jiang.qian{at}jhmi.edu

Received April 25, 2006. Revised July 13, 2006. Accepted August 1, 2006.

Tissue-specific gene expression is generally regulated by more than a single transcription factor (TF). Multiple TFs work in concert to achieve tissue specificity. In order to explore these complex TF interaction networks, we performed a large-scale analysis of TF interactions for 30 human tissues. We first identified tissue-specific genes for 30 tissues based on gene expression databases. We then evaluated the relationships between TFs using the relative position and co-occurrence of their binding sites in the promoters of tissue-specific genes. The predicted TF–TF interactions were validated by both known protein–protein interactions and co-expression of their target genes. We found that our predictions are enriched in known protein–protein interactions (>80 times that of random expectation). In addition, we found that the target genes show the highest co-expression in the tissue of interest. Our findings demonstrate that non-tissue specific TFs play a large role in regulation of tissue-specific genes. Furthermore, they show that individual TFs can contribute to tissue specificity in different tissues by interacting with distinct TF partners. Lastly, we identified several tissue-specific TF clusters that may play important roles in tissue-specific gene regulation.


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