Hu and Gallo BMC Genomics 2010, 11:49 http://www.biomedcentral.com/1471-2164/11/49 analyses [18] or have predicted TF synergy using the relative position and co-occurrence of TFBSs in the pro- moters of genes expressed only in a particular tissue [19]. Others have looked for tissue-specific cis-regulatory modules by enrichment analysis for motifs discovered de novo in tissue-specific promoters relative to other pro- moters from the same species [21]. Despite all these efforts, the mechanism that determines tissue develop- ment and differentiation is still not fully understood, as the regulation of tissue gene expression involves com- plex combinatorial interactions between TFs. In this study, rather than using sequence features of promoters from genes that are expressed only in a par- ticular tissue [19-21], we used our function conserva- tion approach [22] to predict interacting TFs from the most highly expressed genes in each of 79 human tis- sues [23]. Our approach predicts interacting TFs by integrating the function conservation of interacting TFs from both their binding sites and target genes between closely related species, which are based on the following two assumptions. The first is based on the strong possibility that functional TFBS pairs have more distance constraint than random co-occurrence of TFBSs. The second relies on the biological assumption that while a TF pair plays the same role in regulating gene expression between closely related species, the occurrence of its binding sites is expected to be more highly enriched in promoter sequences of orthologous genes than in promoter sequences of non-orthologous genes. Other than function conservation, the use of highly expressed genes in a tissue allows one to avoid the elimination of common genes contributing to tis- sue development and differentiation between tissues, especially for closely related tissues (e.g. skeletal mus- cle and heart), when compared to the use of tissue- specific genes [19-21] which are expressed only in a particular tissue. To our knowledge, this is the first use of a function conservation approach and highly expressed genes in tissues for interacting TF predic- tion. Therefore, the findings provide novel insight into how tissue gene expression is controlled. The application of the function conservation approach to the most highly expressed genes has led to the pre- diction of hundreds of interacting TFs from each of the 79 human tissues. Based on these predictions, TF pairs associated with a certain tissue were identified. The validity of these discovered TF pairs has been evaluated by both known interacting and liver-specific TFs. We further extended our study to find interacting TFs con- trolling gene expression in a broad spatial and temporal manner by looking for TF pairs common to the same type of tissues, from which TF-TF interaction networks were further built. As a first step to elucidating cis- Page 2 of 15 regulatory modules involved in tissue gene regulation, we also performed analysis to identify interactions of 3 TFs. Results Overall analysis procedures The overall analysis procedures are shown in Additional File 1 and Figure 1. We employed our previously devel- oped function conservation approach [22] to first search for TF pairs using the top 300 expressed genes (referred to tissue-expressed genes) [18] in each of the 79 human tissues from the GNF Atlas2 gene expression database (gnfAtlas2) [23] and their corresponding mouse ortholo- gous genes (Additional File 1). We also utilized promo- ter sequences from 1018 human housekeeping genes [24] and their mouse orthologous genes to predict inter- acting TFs playing ubiquitous roles in different tissues (see Methods). We next filtered out the TF pairs in a particular tissue common to those from housekeeping genes (Figure 1a). The remaining TF pairs (referred to tissue TF pairs) in each tissue were more tissue-specific. The rationale for this filtering is that in each tissue some of the interact- ing TFs play general roles, since all tissues possess com- mon mechanisms to control the fundamental biological processes. To find interacting TFs controlling tissue gene expression in a broad spatial and temporal manner, we extended the analysis to identify tissue TF pairs common to the same type of tissues (referred to tissue- type TF pairs) as well as interactions of 3 TFs. For the former, we looked for common tissue TF pairs in at least 50% tissues of the same type (Figure 1a). We also built TF-TF interaction networks by joining 2 or more tissue-type TF pairs with one shared TF between TF pairs in the same tissue. TF-TF interaction networks with the same topology in at least 2 tissues from the same tissue type were defined as “tissue-type TF-TF interaction networks” (Figure 1c). Finally, a two-step analysis of TFBS conservation and enrichment of over- lapping TF target orthologous genes was performed to predict interactions of 3 TFs (Figure 1b). Identification of tissue TF pairs Using the function conservation approach [22] and tis- sue-expressed genes in each of the 79 human tissues, we were able to identify a few hundred TF pairs for each tissue, for which BM-CD71+early erythroid has the lar- gest number of 383 TF pairs, and the ovary tissue has the smallest number of at 230. We also identified 647 TF pairs from housekeeping genes. Filtering TF pairs of housekeeping genes from those in each tissue has greatly reduced the number of TF pairs in each tissue, ranging from 39% TF pairs for lymph node to 59% TF pairs for BM-CD105+endothelial, indicating that a large