Expression-Guided In Silico Evaluation of Candidate Cis Regulatory Codes for Drosophila Muscle Founder Cells

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

Drosophila Araucan and Caupolican Integrate Intrinsic and Signalling Inputs for the Acquisition by Muscle Progenitors of the Lateral Transverse Fate

A central issue of myogenesis is the acquisition of identity by individual muscles. In Drosophila, at the time muscle progenitors are singled out, they already express unique combinations of muscle identity genes. This muscle code results from the integration of positional and temporal signalling inputs. Here we identify, by means of loss-of-function and ectopic expression approaches, the Iroquois Complex homeobox genes araucan and caupolican as novel muscle identity genes that confer lateral transverse muscle identity. The acquisition of this fate requires that Araucan/Caupolican repress other muscle identity genes such as slouch and vestigial. In addition, we show that Caupolican-dependent slouch expression depends on the activation state of the Ras/Mitogen Activated Protein Kinase cascade. This provides a comprehensive insight into the way Iroquois genes integrate in muscle progenitors, signalling inputs that modulate gene expression and protein activity

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns

Highly parallel assays of tissue-specific enhancers in whole Drosophila embryos

Transcriptional enhancers are a primary mechanism by which tissue-specific gene expression is achieved. Despite the importance of these regulatory elements in development, responses to environmental stresses, and disease, testing enhancer activity in animals remains tedious, with a minority of enhancers having been characterized. Here, we have developed ‘enhancer-FACS-Seq’ (eFS) technology for highly parallel identification of active, tissue-specific enhancers in Drosophila embryos. Analysis of enhancers identified by eFS to be active in mesodermal tissues revealed enriched DNA binding site motifs of known and putative, novel mesodermal transcription factors (TFs). Naïve Bayes classifiers using TF binding site motifs accurately predicted mesodermal enhancer activity. Application of eFS to other cell types and organisms should accelerate the cataloging of enhancers and understanding how transcriptional regulation is encoded within them

Erroneous attribution of relevant transcription factor binding sites despite successful prediction of cis-regulatory modules

<p>Abstract</p> <p>Background</p> <p><it>Cis</it>-regulatory modules are bound by transcription factors to regulate gene expression. Characterizing these DNA sequences is central to understanding gene regulatory networks and gaining insight into mechanisms of transcriptional regulation, but genome-scale regulatory module discovery remains a challenge. One popular approach is to scan the genome for clusters of transcription factor binding sites, especially those conserved in related species. When such approaches are successful, it is typically assumed that the activity of the modules is mediated by the identified binding sites and their cognate transcription factors. However, the validity of this assumption is often not assessed.</p> <p>Results</p> <p>We successfully predicted five new <it>cis</it>-regulatory modules by combining binding site identification with sequence conservation and compared these to unsuccessful predictions from a related approach not utilizing sequence conservation. Despite greatly improved predictive success, the positive set had similar degrees of sequence and binding site conservation as the negative set. We explored the reasons for this by mutagenizing putative binding sites in three <it>cis</it>-regulatory modules. A large proportion of the tested sites had little or no demonstrable role in mediating regulatory element activity. Examination of loss-of-function mutants also showed that some transcription factors supposedly binding to the modules are not required for their function.</p> <p>Conclusions</p> <p>Our results raise important questions about interpreting regulatory module predictions obtained by finding clusters of conserved binding sites. Attribution of function to these sites and their cognate transcription factors may be incorrect even when modules are successfully identified. Our study underscores the importance of empirical validation of computational results even when these results are in line with expectation.</p

The Complex Spatio-Temporal Regulation of the Drosophila Myoblast Attractant Gene duf/kirre

A key early player in the regulation of myoblast fusion is the gene dumbfounded (duf, also known as kirre). Duf must be expressed, and function, in founder cells (FCs). A fixed number of FCs are chosen from a pool of equivalent myoblasts and serve to attract fusion-competent myoblasts (FCMs) to fuse with them to form a multinucleate muscle-fibre. The spatial and temporal regulation of duf expression and function are important and play a deciding role in choice of fibre number, location and perhaps size. We have used a combination of bioinformatics and functional enhancer deletion approaches to understand the regulation of duf. By transgenic enhancer-reporter deletion analysis of the duf regulatory region, we found that several distinct enhancer modules regulate duf expression in specific muscle founders of the embryo and the adult. In addition to existing bioinformatics tools, we used a new program for analysis of regulatory sequence, PhyloGibbs-MP, whose development was largely motivated by the requirements of this work. The results complement our deletion analysis by identifying transcription factors whose predicted binding regions match with our deletion constructs. Experimental evidence for the relevance of some of these TF binding sites comes from available ChIP-on-chip from the literature, and from our analysis of localization of myogenic transcription factors with duf enhancer reporter gene expression. Our results demonstrate the complex regulation in each founder cell of a gene that is expressed in all founder cells. They provide evidence for transcriptional control—both activation and repression—as an important player in the regulation of myoblast fusion. The set of enhancer constructs generated will be valuable in identifying novel trans-acting factor-binding sites and chromatin regulation during myoblast fusion in Drosophila. Our results and the bioinformatics tools developed provide a basis for the study of the transcriptional regulation of other complex genes

Robust Target Gene Discovery through Transcriptome Perturbations and Genome-Wide Enhancer Predictions in Drosophila Uncovers a Regulatory Basis for Sensory Specification

CisTarget X is a novel computational method that accurately predicts Atonal governed regulatory networks in the retina of the fruit fly

Contribution of Distinct Homeodomain DNA Binding Specificities to Drosophila Embryonic Mesodermal Cell-Specific Gene Expression Programs

Homeodomain (HD) proteins are a large family of evolutionarily conserved transcription factors (TFs) having diverse developmental functions, often acting within the same cell types, yet many members of this family paradoxically recognize similar DNA sequences. Thus, with multiple family members having the potential to recognize the same DNA sequences in cis-regulatory elements, it is difficult to ascertain the role of an individual HD or a subclass of HDs in mediating a particular developmental function. To investigate this problem, we focused our studies on the Drosophila embryonic mesoderm where HD TFs are required to establish not only segmental identities (such as the Hox TFs), but also tissue and cell fate specification and differentiation (such as the NK-2 HDs, Six HDs and identity HDs (I-HDs)). Here we utilized the complete spectrum of DNA binding specificities determined by protein binding microarrays (PBMs) for a diverse collection of HDs to modify the nucleotide sequences of numerous mesodermal enhancers to be recognized by either no or a single subclass of HDs, and subsequently assayed the consequences of these changes on enhancer function in transgenic reporter assays. These studies show that individual mesodermal enhancers receive separate transcriptional input from both I–HD and Hox subclasses of HDs. In addition, we demonstrate that enhancers regulating upstream components of the mesodermal regulatory network are targeted by the Six class of HDs. Finally, we establish the necessity of NK-2 HD binding sequences to activate gene expression in multiple mesodermal tissues, supporting a potential role for the NK-2 HD TF Tinman (Tin) as a pioneer factor that cooperates with other factors to regulate cell-specific gene expression programs. Collectively, these results underscore the critical role played by HDs of multiple subclasses in inducing the unique genetic programs of individual mesodermal cells, and in coordinating the gene regulatory networks directing mesoderm development.National Institutes of Health (U.S.) (Grant R01 HG005287

Fine-Tuning Enhancer Models to Predict Transcriptional Targets across Multiple Genomes

Networks of regulatory relations between transcription factors (TF) and their target genes (TG)- implemented through TF binding sites (TFBS)- are key features of biology. An idealized approach to solving such networks consists of starting from a consensus TFBS or a position weight matrix (PWM) to generate a high accuracy list of candidate TGs for biological validation. Developing and evaluating such approaches remains a formidable challenge in regulatory bioinformatics. We perform a benchmark study on 34 Drosophila TFs to assess existing TFBS and cis-regulatory module (CRM) detection methods, with a strong focus on the use of multiple genomes. Particularly, for CRM-modelling we investigate the addition of orthologous sites to a known PWM to construct phyloPWMs and we assess the added value of phylogenentic footprinting to predict contextual motifs around known TFBSs. For CRM-prediction, we compare motif conservation with network-level conservation approaches across multiple genomes. Choosing the optimal training and scoring strategies strongly enhances the performance of TG prediction for more than half of the tested TFs. Finally, we analyse a 35th TF, namely Eyeless, and find a significant overlap between predicted TGs and candidate TGs identified by microarray expression studies. In summary we identify several ways to optimize TF-specific TG predictions, some of which can be applied to all TFs, and others that can be applied only to particular TFs. The ability to model known TF-TG relations, together with the use of multiple genomes, results in a significant step forward in solving the architecture of gene regulatory networks