Quantitative analysis of gene regulation in Drosophila

In this project, characterization of the MAPK signaling cascade was done using the terminal patterning of Drosophila embryo as an experimental system. MAPK acts downstream of the Torso receptor tyrosine kinase (RTK) whose activation is limited to the poles of the embryo. This results in a localized activation of MAPK which is essential for the specification of the embryonic termini. Active MAPK controls the expression of the tailless (tll) and huckebein (hkb) gap genes by downregulating a transcriptional repressor, Capicua (Cic). In this work we are interested in understanding the mechanisms that allow differential specification and precise positioning of gene expression boundaries. We quantitatively analyzed information flow from MAPK to Cic and the two target genes, tll and hkb, by constructing input-output maps in wild type embryos. Our results show that the sharp MAPK activation is translated into a graded gradient of Cic concentration and switch-like responses of tll and hkb genes. This indicates that the ultrasensitive response occurs at the level of the Cic repression which led us to test the hypothesis that Cic alone could be sufficient for tll and hkb boundary specification. However, data from mutants containing lower levels of Cic showed that boundaries of two genes become indistinguishable in terms of Cic concentration while still clearly different in terms of position. This suggests that MAPK downregulation of Cic alone is not sufficient to explain the observed localization and that, other factors are required for the robust patterning of the termini of the Drosophila embryo. To find the missing components of this regulatory system we have started an in vitro Yeast One Hybrid screen. This technique, allowing detection of protein-DNA interaction, was optimized for high-throughput screening. Together with a library Drosophila transcription factors we hope to identify the missing regulators of tll and hkb genes.

Development and application of experimental tools to elucidate the gene regulatory network underlying adipogenesis

Carine Delattre-Gubelmann

Excess fat mass in obese individuals, which is characterized by an increase of white adipocyte cell size and number, significantly raises the risk of developing metabolic syndrome symptoms as well as cancer. A detailed understanding of the transcriptional mechanisms mediating white adipocyte differentiation (i.e., adipogenesis) is required to counteract the growing obesity epidemics. In this thesis, we have implemented two combinatory approaches to detect new protein-DNA interactions in the mouse, one of which targets the adipogenic gene regulatory network (GRN) that underlies the white adipocyte differentiation process. As a first approach to shed light on GRNs, we have developed and validated a powerful approach that combines a high-throughput yeast one-hybrid-based (Y1H) system to detect transcription factors (TFs)-DNA element interactions, together with a novel microfluidics-based technique, enabling the identification and characterization of individual binding sites for detected TFs within the respective regulatory sequences at unprecedented throughput and resolution. Using well-described regulatory elements as well as an uncharacterized enhancer which can play also a role in adipogenesis, we show that our approach in a first phase uncovers many known as well as novel TF-DNA interactions, of which a large proportion were independently validated. In addition, we further demonstrate how our approach can be used in a second phase to characterize detected interactions by enabling the generation of a relative DNA occupancy landscape over the length of the respective DNA element. Furthermore, we provide evidence that this system enables the detection of novel interactions that may have in vivo relevance. Given the strict requirements for direct and well-characterized protein-DNA interaction data to generate and model gene regulatory networks, the presented two-tiered approach should be of great value to enhance our understanding of the regulatory principles underlying mouse gene expression. To identify transcription factors involved in white adipocyte differentiation, we overexpressed TFs in preadipocytes and quantified their effect on differentiation. To this end, we have created a mouse open-reading frame resource consisting of more than 750 fully sequence-verified TF clones. We transferred these TF clones to a Tet-On lentiviral vector and used them to infect white preadipocytes. Based on microscopy-assisted quantification of lipids after induction of differentiation, we identified putatively novel TF candidates that strongly enhanced differentiation based on the increased lipid accumulation, indicating a potential role in adipogenesis. Gene expression and ChIP-seq experiments have been performed for the three top candidates (ZEB1, ZFP30 and ZFP277) to further study their function within the adipogenic regulatory network. To assess whether the effect of these TFs on adipogenesis could be reproduced in vivo, murine adipose stromal-vascular fraction containing adipocyte precursor cells were implanted into mice fed a high-fat diet, after each of the three TF candidates were knockdown or overexpressed in these cells. This experiment and the RNA-seq analyses are currently being processed while this thesis is being written. Furthermore, due to the absence of quantitative real-time PCR (qPCR) primer designing tools that would allow for gene-specific expression profiling in a high-throughput fashion, we design a user-friendly platform: GETPrime. This platform uniquely combines and automates several features critical to optimal qPCR primer design for all annotated Homo sapiens, Mus musculus, Caenorhabditis elegans, Drosophila melanogaster and Danio rerio genes in the Ensembl database. GETPrime primers have been extensively validated experimentally, demonstrating their high quality as well as high transcript specificity in complex samples.

EPFL2013

Genomic Context and KRAB/KAP1-mediated Transcriptional Repression

Sylvain Meylan

Chromatin structure and its impact on transcriptional activity represent one of the major revolutions in the biomedical field over the passed decade. The implications concern as much fundamental as clinically-oriented research; diseases and therapeutical strategies are being approached from a new point of view (Mackay et al., 2008; Richman et al., 2009). The current work dissects the different susceptibilities of genes in the human genome and how these react to the changes in chromatin landscape upon the recruitment of a transcriptional corepressor. The KRAB-Zinc finger proteins are the largest family of transcriptional repressors in the human genome. These repressors use the KRAB domain as a docking site for KAP-1 which in turn recruits a macromolecular chromatin-remodelling complex leading to heterochromatin formation. Yet current knowledge of their impact on gene regulation is limited. By artificially recruiting a KRAB-repressor in the context of genes, our lab has previously shown that this repression can act over several tens of kilobases (Groner et al., 2010). Specifically, transcriptional silencing depends on the spreading of repressive marks to the upstream promoter. However, such spreading to the trapped promoter is not systematic, suggesting potential counter-acting influences. To identify the determinants of repression, we designed a high-throughput approach of our experimental system to accumulate thousands of KRAB docking sites for which the trapped promoter is regulable and docking sites for which the promoter is immune to repression. Firstly, our data indicate that KRAB/KAP-1-repression can act efficiently over distances of 20 kb albeit in a very variable fashion, but is limited beyond that. Second, propagation of repression in the transcribed regions was favoured in most open chromatin. Third, we observed that the broader context of genes sustaining repression was that of most euchromatic regions, recapitulating the genetic context of natural targets of KAP1 repression. On the other hand, we have no evidence that barrier elements such as CTCF counteract this KRAB-mediated repression within genes. Finally, work on cases where repression acts over distances greater than 40 kb hints at the regulation of enhancers by our KRAB repressor rather than promoter. In conclusion, this work broadens the knowledge in the emerging field of gene regulation and provides further insight on the complexity of chromatin architecture and its meaning at a functional level.

EPFL2010

Stabilizing patterning in the Drosophila segment polarity network by selecting models in silico

Mirko Bischofberger, Felix Naef, Jacques Rougemont

The segmentation of Drosophila is a prime model to study spatial patterning during embryogenesis. The spatial expression of segment polarity genes results from a complex network of interacting proteins whose expression products are maintained after successful segmentation. This prompted us to investigate the stability and robustness of this process using a dynamical model for the segmentation network based on Boolean states. The model consists of intra-cellular as well as inter-cellular interactions between adjacent cells in one spatial dimension. We quantify the robustness of the dynamical segmentation process by a systematic analysis of mutations. Our starting point consists in a previous Boolean model for Drosophila segmentation. We define mathematically the notion of dynamical robustness and show that the proposed model exhibits limited robustness in gene expression under perturbations. We applied in silico evolution (mutation and selection) and discover two classes of modified gene networks that have a more robust spatial expression pattern. We verified that the enhanced robustness of the two new models is maintained in differential equations models. By comparing the predicted model with experiments on mutated flies, we then discuss the two types of enhanced models. Drosophila patterning can be explained by modelling the underlying network of interacting genes. Here we demonstrate that simple dynamical considerations and in silico evolution can enhance the model to robustly express the expected pattern, helping to elucidate the role of further interactions.