Drosophila myogenesis as a model for studying cis-regulatory networks : identifying novel players and dissecting the role of transcriptional repression

Recent studies have identified in vivo binding profiles of key mesodermal regulators across the Drosophila melanogaster genome. Many of the occupied sites lie in the vicinity of loci encoding yet other transcription factors. The analyzed cis- regulatory modules drive expression in a variety of complex spatio-temporal patterns that cannot be explained by the binding of the core regulators alone. Thus there clearly are additional, unknown transcription factors in the regulatory network that governs the process of embryonic mesoderm specification and muscle differentiation. In order to identify novel myogenic regulators in a systematic way, and thereby enrich the underlying network, I initiated a molecular screen to uncover new players. Candidate putative transcription factors were prioritized based on their expression in mesoderm and on available ChIP-on-chip and expression profiling data. Their role in myogenesis was subsequently assayed using Drosophila deficiency lines whose phenotypes were analyzed with a muscle-specific marker. Altogether, 67 different deficiency and loss-of-function lines were used individually or in combination to delete 46 transcription factors with mesodermal expression. In 21 of the 46 cases, the mutant embryos displayed specific defects in the development of one or more muscle types. One pair of partially overlapping deficiencies placed in trans showed a failure in myoblast fusion, a process that gives rise to muscle syncytia from mononucleated myoblasts. The corresponding deleted candidate gene was MED24, a subunit of the Mediator complex, which is a general co-activator of transcription. Muscle-specific knockdown of MED24 or MED14, another subunit of the complex whose deletion by deficiency lines phenocopies that of MED24, leads to lethality. To establish whether MED24 and MED14 are indeed involved in muscle development, I generated smaller deletion lines using FRT-mediated recombination. While deletion of MED14 does not affect myogenesis, embryos deficient for MED24 display supernumerary mononucleated myoblasts. Both small deletion lines were then combined together to detect possible redundancy that could obscure the requirement of MED14 and MED24 in muscle development. Another candidate transcription factor within the myogenic network based on ChIP-on-chip experiments is the transcriptional repressor Tramtrack69 (Ttk69). Ttk69 is expressed in the primordium of visceral and, more transiently, somatic muscle. In ttk69 mutant embryos, homozygous for a loss-of-function allele, myoblast fusion is delayed, the myoblasts aggregate in clusters, and fail to migrate towards the ectodermal attachment sites. Two distinct myoblast populations, a founder cell and multiple fusion competent myoblasts, contribute to each muscle fibre. As revealed by immunohistochemistry and in situ hybridization, in ttk69 mutants there are significantly more founder cells formed while the number of fusion competent myoblasts is decreased. Consistently, ectopic expression of Ttk69 in the founder cells, but not fusion competent myoblasts, gives rise to severe myoblast fusion defects. These phenotypic analyses suggest a model where Ttk69 is required for specification of fusion competent myoblasts and in its absence, their conversion to a founder cell-like fate may occur. According to the proposed model, Ttk69 would repress founder cell genes within the fusion competent myoblasts. To determine whether this holds true on a global scale, I performed a high-resolution ChIP-on-chip experiment in 6-8 hour wild type embryos. Indeed, Ttk69 binding was significantly enriched in the vicinity of founder cell-specific genes as compared to fusion competent myoblast-specific genes. ChIP-on-chip data generated for Lame duck, a transcriptional activator essential for fusion competent myoblast determination, showed the opposite tendency. It therefore appears that proper specification of fusion competent myoblast identity requires both positive input from Lame duck and inhibition of founder cell-specific genes by Ttk69. These findings advance our limited knowledge about the role of transcriptional repression within the myogenic regulatory network. Finally, I re-evaluated the role of Snail, a well-established transcriptional repressor involved in early mesoderm specification and gastrulation. Multiple observations suggested that Snail may also play a positive role in regulating mesodermal genes. To investigate this possibility, I performed luciferase assays with previously characterized mesodermal enhancers and showed that Snail can elevate their activation levels. In one case, this ability of Snail was suppressed upon mutagenesis of putative Snail binding motifs, both in cell culture and in vivo. Moreover, expression of the enhancers and their associated genes is significantly reduced in snail mutant embryos. Snail thus seems to play a dual role in repressing non-mesodermal genes, but also in contributing to the activation of some early mesodermal genes

Combinatorial binding leads to diverse regulatory responses:Lmd is a tissue-specific modulator of Mef2 activity

Understanding how complex patterns of temporal and spatial expression are regulated is central to deciphering genetic programs that drive development. Gene expression is initiated through the action of transcription factors and their cofactors converging on enhancer elements leading to a defined activity. Specific constellations of combinatorial occupancy are therefore often conceptualized as rigid binding codes that give rise to a common output of spatio-temporal expression. Here, we assessed this assumption using the regulatory input of two essential transcription factors within the Drosophila myogenic network. Mutations in either Myocyte enhancing factor 2 (Mef2) or the zinc-finger transcription factor lame duck (lmd) lead to very similar defects in myoblast fusion, yet the underlying molecular mechanism for this shared phenotype is not understood. Using a combination of ChIP-on-chip analysis and expression profiling of loss-of-function mutants, we obtained a global view of the regulatory input of both factors during development. The majority of Lmd-bound enhancers are co-bound by Mef2, representing a subset of Mef2's transcriptional input during these stages of development. Systematic analyses of the regulatory contribution of both factors demonstrate diverse regulatory roles, despite their co-occupancy of shared enhancer elements. These results indicate that Lmd is a tissue-specific modulator of Mef2 activity, acting as both a transcriptional activator and repressor, which has important implications for myogenesis. More generally, this study demonstrates considerable flexibility in the regulatory output of two factors, leading to additive, cooperative, and repressive modes of co-regulation

Enhancer loops appear stable during development and are associated with paused polymerase

Developmental enhancers initiate transcription and are fundamental to our understanding of developmental networks, evolution and disease. Despite their importance, the properties governing enhancer-promoter interactions and their dynamics during embryogenesis remain unclear. At the beta-globin locus, enhancer-promoter interactions appear dynamic and cell-type specific(1,2), whereas at the HoxD locus they are stable and ubiquitous, being present in tissues where the target genes are not expressed(3,4). The extent to which preformed enhancer-promoter conformations exist at other, more typical, loci and how transcription is eventually triggered is unclear. Here we generated a high-resolution map of enhancer three-dimensional contacts during Drosophila embryogenesis, covering two developmental stages and tissue contexts, at unprecedented resolution. Although local regulatory interactions are common, long-range interactions are highly prevalent within the compact Drosophila genome. Each enhancer contacts multiple enhancers, and promoters with similar expression, suggesting a role in their co-regulation. Notably, most interactions appear unchanged between tissue context and across development, arising before gene activation, and are frequently associated with paused RNA polymerase. Our results indicate that the general topology governing enhancer contacts is conserved from flies to humans and suggest that transcription initiates from preformed enhancer-promoter loops through release of paused polymerase

Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes

International audienceGiven the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions

Regulatory graph for the signalling/transcriptional network controlling drosophila mesoderm specification.

<p>Built with the software GINsim, this regulatory graph encompasses the main regulatory factors and interactions involved in mesoderm specification (stages 8–10), as documented by published (molecular) genetic and functional genomic data. Ellipses denote Boolean nodes, whereas rectangles denote multilevel nodes. Light green filling denotes input nodes, most corresponding to factors expressed in and acting from the ectoderm. Yellow filling denotes output factors, mostly effector genes and tissue markers. Blue or grey filling denotes internal nodes expressed in the mesoderm. Green arrows and red blunt arrows denote activations and inhibitions, respectively. Logical rules are further associated with each node to define its behaviour depending on regulatory inputs (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005073#pcbi.1005073.s006" target="_blank">S1 Table</a>). To ease the dynamical analysis of this regulatory graph, we performed a reduction of this regulatory graph (cf. Material and methods), making implicit the twelve grey components. This logical model is provided as supporting data, including comprehensive annotations and bibliographical references.</p

Key signalling pathways and markers genes involved in mesoderm specification.

<p>A, B: In situ hybridizations for Tin and Bin during mesoderm specification at stages 8 and 9–10. Tin is implicated in the formation of VM and H, while Bin participates only in the development of VM. Initially, the expression of Tin is mainly due to Twist activation. Later, Tin expression needs the presence of Dpp, Tin itself, in combination with Pan. C: Graphical representations of the main pathways activated by signals coming from the ectoderm, encompassing target transcription factors and cross-regulations underlying the specification of VM, H, FB and SM. In the absence of these factors, these tissues do not form or are severely reduced. Black and light grey arcs denote active and inactive regulations, depending on stage or tissue. Normal and blunt end arrows denote activations and inhibitions, respectively.</p

Early stages of drosophila mesoderm specification.

<p><b>A-C:</b> Schematic description of the establishment of mesoderm anterior-posterior and dorsal-ventral patterning. At stage 8, the presumptive mesoderm is largely homogeneous. At stage 9, ectodermal signals outline a characteristic pattern, with stripes of Hh, Eve and En alternating with stripes of Wg and Slp, which delimit anterior/posterior segmental borders, respectively. Dpp signalling further delimits dorsal versus ventral mesoderm domains. Mesoderm specification is achieved at stage 10, when Wg/Slp domains give rise to heart precursors (H, in red, dorsally located) and somatic muscles (SM, orange, ventrally located), whereas En/Eve/Hh domains give rise to visceral mesoderm (VM, blue, dorsal) and fat body (FB, green, ventral). <b>D:</b> Schematic representation of the four main tissues originating from the mesoderm in each segment, with key associated markers (e.g. Srp expression for FB).</p

Simulations of known genetic perturbations.

<p>The results of selected simulations of loss-of-function (lof), gain-of-function (gof) mutations, and of combination thereof are shown in the form of coloured square vignettes, along with references to articles presenting matching data. The first vignette (top left) correspond to the wild type situation, with VM, H, FB and SM presumptive territories coloured in blue, red, green and orange, respectively. In the following vignettes, the coloration of the four presumptive territories are modified to reflect the absence or important markers, or the combination of markers associated with different tissues. Wg lof leads to the loss of Wg/Slp domain, resulting in an expansion of the En/Hh domain; consequently, the model correctly predicts the loss of H along with a potential perturbation of SM (yellow domains). Dpp lof leads to the loss of dorsal derivatives (VM and H), along with an expansion of FB. Dpp gof leads to an expansion of VM at the expense of FB, along with a perturbation of SM. Tin lof shows a loss of dorsal tissues, while Bap lof exhibits only the loss of VM. Finally, the combination of Wg gof and Hh lof leads to a dorsal expansion of H, along with a loss of FB, while the combination of Dpp gof, Hh gof and Wg lof leads to an expansion of VM in the whole mesoderm.</p

Systematic simulations of double mutants.

<p>This matrix displays the results of systematic perturbations. Loss- and gain-of-function mutations (rows and column) were simulated iteratively using a set of Python scripts, along with pairwise combinations (cf. Material and methods). The results of the simulations of single mutants are displayed on the diagonal of the matrix. The predicted phenotype for each double mutant is shown at the intersection of the corresponding column and row. Note that the cells corresponding to the crossing of a lof and a gof for the same gene are left empty. Simulation results are graphically depicted using vignettes as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005073#pcbi.1005073.g004" target="_blank">Fig 4</a>, with specific colours denoting situations with miss-expressed genes (cf. colour key top right). This presentation eases the comparison of the results of multiple mutant simulations and enables the identification of dominant or synergic effects. This matrix encompasses numerous predictions, along with a few dozens of documented phenotypes. The web version of the matrix (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005073#pcbi.1005073.s008" target="_blank">S2 File</a>) further provides access to detailed information regarding the predicted patterns of expression for each mutant in each region. We have selected six perturbations (four single and two double ones, surrounded by tick squares in the matrix) for experimental validation (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005073#pcbi.1005073.g006" target="_blank">Fig 6</a>).</p

In situ RNA staining for two sets of single and double mutants.

<p><b>A:</b> Slp lof and Med lof each results in a perturbation of H. The double mutant displays an even stronger disruption of H, along with a clear expansion of Srp expression. These experimental results are largely consistent with our model predictions and further provide interpretational clues regarding mixed expression patterns. <b>B:</b> Slp gof exhibits a loss of VM, while FB appears perturbed in both Doc gof and Slp gof mutants. The combination of these perturbations leads to stronger losses of FB and VM, while H and SM are barely affected. These results qualitatively agree with model predictions.</p