Abstract
While combinatorial models of transcriptional regulation can be inferred for metazoan systems from a priori biological knowledge, validation requires extensive and time-consuming experimental work. Thus, there is a need for computational methods that can evaluate hypothesized cis regulatory codes before the difficult task of experimental verification is undertaken. We have developed a novel computational framework (termed “CodeFinder”) that integrates transcription factor binding site and gene expression information to evaluate whether a hypothesized transcriptional regulatory model (TRM; i.e., a set of co-regulating transcription factors) is likely to target a given set of co-expressed genes. Our basic approach is to simultaneously predict cis regulatory modules (CRMs) associated with a given gene set and quantify the enrichment for combinatorial subsets of transcription factor binding site motifs comprising the hypothesized TRM within these predicted CRMs. As a model system, we have examined a TRM experimentally demonstrated to drive the expression of two genes in a sub-population of cells in the developing Drosophila mesoderm, the somatic muscle founder cells. This TRM was previously hypothesized to be a general mode of regulation for genes expressed in this cell population. In contrast, the present analyses suggest that a modified form of this cis regulatory code applies to only a subset of founder cell genes, those whose gene expression responds to specific genetic perturbations in a similar manner to the gene on which the original model was based. We have confirmed this hypothesis by experimentally discovering six (out of 12 tested) new CRMs driving expression in the embryonic mesoderm, four of which drive expression in founder cells.
Highlights
A central challenge to determining the structure of genetic regulatory networks is the development of systematic methods for assessing whether a set of transcription factors (TFs) co-regulates a given set of co-expressed genes
Evaluation of Cis Regulatory Codes. To address this question in metazoan systems, we have developed an initial statistical framework for evaluating hypothesized transcriptional regulatory models (TRMs; i.e., sets of TFs that together co-regulate a target gene set through their combinatorial interactions at cis regulatory modules (CRMs))
We found that three TFs—Pnt, Twi, and Tin—are likely to regulate a specific subset of founder cell (FC) genes that share characteristic changes in their gene expression profiles in response to the genetic perturbations used by Estrada et al [21]
Summary
A central challenge to determining the structure of genetic regulatory networks is the development of systematic methods for assessing whether a set of transcription factors (TFs) co-regulates a given set of co-expressed genes. Classical genetics approaches allow the identification of key regulating TFs and the determination of their approximate ordering within the genetic hierarchy, demonstrating that a collection of TFs forms a combinatorial code acting to directly drive gene expression has required laborious experimental identification and perturbation of numerous individual cis regulatory modules (CRMs; [1]) To speed this process, several groups have recently demonstrated that computational approaches can rapidly identify CRMs with considerable accuracy [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], especially when performing computational searches with a collection of TFs known a priori to co-regulate. In order for these in silico approaches to effectively identify the cis component of regulation in novel biological systems (i.e., discover CRMs), additional computational methods are needed that can identify the trans component of regulation (i.e., the set of co-regulating TFs)
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