Hu and Gallo BMC Genomics 2010, 11:49 http://www.biomedcentral.com/1471-2164/11/49 Page 10 of 15 Tissue type Adrenal gland* Brain Cancer Immune/ cancer Immune Liver Pancreas Smooth muscle* Skeletal muscle* Testis Thyroid Table 2 Number of tissue-type TF pairs in the selected 11 tissue groups. # Tissues 2 Cluster ID C6 # TF pairs 31 # TF pairs with annotated function 16 16 4 3/4 4 2 2 2 3 5 2 C4 C3 C2 C10 C8 C7 C11 C1 C5 C9 45 32 17 33 30 23 29 39 74 27 26 25 14 15 22 6 14 26 40 3 * One tissue from the same group is used as the tissue type name. Cluster ID: clustering ID for the 11 selected tissue groups in Figure 4. Figure 6 Tissue-type TF-TF interaction networks for liver tissues. (a) Multi-input network motif for TFs of FOXJ2, HNF1, and TTF1 and their target genes which are regulated by either two or three TFs. (b) Topology of seven tissue-type TF-TF interaction networks for liver tissues. Each node represents a TF and each edge (link) represents a significant synergy between two TFs from tissue TF pairs. Previously known liver-specific TFs are labeled with an asterisk. multiple interacting TFs between cancer and immune systems, including cell cycle, cell division, DNA replica- tion, mitosis, phosphoinositide-mediated signaling, and immune response. These findings therefore provide new insight into the molecular interplay between cancer and immune systems. Discussion Tissue gene expression is generally regulated by multiple transcription factors. A major first step toward under- standing how tissues achieve their specificity is to iden- tify interacting TFs regulating gene expression in different tissues. Previous computational approaches to predict interacting TFs were mainly based on recogniz- able sequence features of tissue-specific [19-21] genes derived from genome-wide gene-expression profiling. Despite these studies, the mechanisms controlling tissue gene expression are still not fully understood. In this study, we utilized our previously developed function conservation approach, which, based on this and a prior study [22], was shown to successfully predict interacting TFs from tissue-expressed genes. Based on the predictions, tissue TF pairs were identified. The advantage of our approach lies in the fact that it does not depend solely on sequence features of genes but rather function conservation of interacting TFs from both their binding sites and putative target genes between closely related species. Other than function conservation, the use of tissue-expressed genes would allow one to avoid the elimination of common genes contributing to tissue development and differentiation between tissues, especially for these closely related tis- sues (e.g. skeletal muscle and heart) when compared to the use of tissue-specific genes [19-21] which are expressed in a particular tissue. Therefore, the utiliza- tion of our function conservation approach and tissue- expressed genes provides an alternative way for tissue interacting TF discovery. One of the findings of our study indicates that tissue gene expression is controlled by large sets of tissue TF pairs, which is in agreement with previously reported findings from an approach using sequence features of tissue-specific genes by Yu et al. [19]. We were curious to know the differences of interacting TFs identified by the two different approaches, and selected the liver tis- sue for comparison. For the 8 known liver-specific inter- acting TFs that were successfully predicted by our approach in the 162 liver tissue TF pairs, we found that HNF3:HNF4ALPHA was in the liver-specific TF pairs predicted by Yu et al. However, we did not find the other 7 known liver-specific interacting TFs predicted in our 162 tissue TF pairs from Yu et al. On the other hand, 6 of the 27 known liver-specific interacting TFs were correctly predicted by Yu et al but were not in our