Diversification of TF Paralogs Figure 1. Evolutionary fates of TF paralogs. (a) Model of a TF showing the interaction domain (ID) and binding domain (BD). Duplication events produce two identical paralogous TFs. (b) As a result of evolutionary divergence of the BDs, the two paralogs bind to distinct DNA regulatory sites and thus, target distinct genes. (c) As a result of evolutionary divergence of the IDs, the two paralogs interact with distinct partners and thus, target distinct genes (or the same genes under different contexts) (d) As a result of evolutionary divergence of the expression patterns of the paralogous TF genes, the two TFs are active in different contexts, for instance, different tissues. doi:10.1371/journal.pone.0002345.g001 functional consequences is thus likely to reveal relationships among these pathways. For instance, mutations in the DNA binding domain of a TF gene are likely to alter the TF’s DNA binding site motif. Availability of DNA binding site motifs of a vast number of human TFs[18,19], as well as genome wide expression data[20] provides a novel opportunity to investigate the relation- ship between divergence in the DNA binding site motif and divergence in expression among paralogous TFs. By analyzing a large collection of human paralogous TF pairs, we show for the first time that the paralogous pairs whose DNA binding site motifs are similar tend to have diverged expression patterns so that in any particular tissue at most one of the paralogs is expressed at a high level. Conversely, the paralogs that are highly expressed in a tissue tend to have dissimilar DNA binding site motifs. Our work represents a first attempt to quantify the biological expectation that paralogous TFs must diversify in one or more aspects of their function in order to survive extinction. We have extended our pair-wise analysis to TF families, demonstrating that in any given tissue there is a large separation in expression level between the family members with similar DNA binding site motifs. Furthermore, our finding is independent of the age of paralogs. TF paralogs with diverged binding site motifs tend to be diverged in their functions, as measured by GO terms. We also found that a decrease in tissue-specific gene expression is more frequent than what is expected by chance. Results Identifying human paralogous transcription factors Using a stringent sequence similarity criteria as in [21], we identified 95 pairs of transcription factor (TF) gene paralogs for which there is a DNA binding site motif (Positional Weight Matrix or PWM) derived from human binding sites in the TRANSFAC database[18]. Our investigation of paralogous TFs is based on these 95 paralogous pairs corresponding to 98 unique genes; some genes pair with multiple distinct genes. Certain genes correspond to multiple transcripts, and in terms of transcripts, our data consists of 98 pairs with 123 unique transcripts. Certain transcripts correspond to multiple probe sets on the Affymetrix array. In terms of probe sets, our data consists of 390 probe set pairs with 201 unique probe sets. Human transcription factor paralogs that are expressed highly in a tissue tend to have distinct DNA binding site motifs and conversely, paralogs with similar DNA binding site motifs tend to be diverged in their tissue-specific expression Consider two paralogous TF genes that are highly expressed in a particular tissue. If these two TFs recognize similar DNA binding site motifs then it is possible that these genes may interfere with each other’s activity or they may potentially serve compensatory roles[22]. Another possibility is that the two TFs have dissimilar DNA binding site motifs and target different genes. The extent to which paralogous TFs that are highly expressed in the same tissues have diverged in their DNA binding site motifs is not known. Thus, for our set of paralogous TF pairs, we first tested whether there is correlation between the similarity of their DNA binding site motifs and their gene expression divergence. Expression divergence for a pair of genes (X, Y) is commonly measured using the Pearson Correlation Coefficient (PCC) between EˆX and EˆY, where EˆX and EˆY represent the vectors of expressions for genes X and Y, respectively, in multiple tissues. Employing PCC as a measure of expression divergence yielded no PLoS ONE | www.plosone.org 2 June 2008 | Volume 3 | Issue 6 | e2345