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D.J. Zack, X. Yu, J. Qian; A Bioinformatic Approach to Identify Retina– and Other Tissue–Specific Transcription Factor Interactions . Invest. Ophthalmol. Vis. Sci. 2006;47(13):4891.
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© ARVO (1962-2015); The Authors (2016-present)
Multiple transcription factors (TFs) are often needed to work in concert to achieve tissue specificity. In an attempt to understand TF interactions, with particular emphasis on those that are relevant to retina–specific gene expression, we are using a bioinformatics approach to identify biologically relevant interactions.
We first identified tissue–specific genes for 30 tissues based on the gene expression databases and known annotated gene functions. We then searched the evolutionarily conserved regions in the human genome using 306 DNA binding matrices available from TRANSFAC and the literature. The interaction between each pair of TFs was evaluated according to the distances between their respective binding sites on the promoters and the specificity of the pair to the tissue–specific gene groups.
TF interactions in 30 tissues were derived. Known protein–protein interactions are highly enriched among the predicted interactions (>60 times that of random expectation). For genes preferentially expressed in the eye (mostly retina datasets), approximately 100 interacting TF pairs were predicted, including CRX:CRX, CRX:NRL, CRX:CHX10, and NR2E3:MEF2. Since CHX10 has been suggested to act as a repressor of CRX–mediated activation of rhodopsin in non–photoreceptor retinal cells, the finding of a CRX/CHX10 interaction is interesting because it suggests that the algorithm identifies negatively interacting as well as positively interacting TF pairs. We also examined the gene expression of the target genes of the interacting TF pairs, which may reflect the activity of the TF pairs. We found that the eye specific TF interactions are, in general, only active in the eye, but not in the other tissues. In addition, based on the gene expression analysis we also found that these interacting TFs are likely to regulate preferentially eye–specific genes.
A bioinformatics approach can be used successfully to predict interactions between TFs. Such information can complement experimental studies of gene regulation, and we are in the process of experimentally testing some of the bioinformatic predictions related to retinal gene expression.
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