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

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.

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

Automated protein-DNA interaction screening of Drosophila regulatory elements

Julien Bryois, Bart Deplancke, Jean-Daniel Feuz, Antonina Iagovitina, Alina Isakova, Andreas Massouras

Drosophila melanogaster has one of the best characterized metazoan genomes in terms of functionally annotated regulatory elements. To explore how these elements contribute to gene regulation, we need convenient tools to identify the proteins that bind to them. Here we describe the development and validation of a high-throughput yeast one-hybrid platform, which enables screening of DNA elements versus an array of full-length, sequence-verified clones containing over 85% of predicted Drosophila transcription factors. Using six well-characterized regulatory elements, we identified 33 transcription factor-DNA interactions of which 27 were previously unidentified. To simultaneously validate these interactions and locate the binding sites of involved transcription factors, we implemented a powerful microfluidics-based approach that enabled us to retrieve DNA-occupancy data for each transcription factor throughout the respective target DNA elements. Finally, we biologically validated several interactions and identified two new regulators of sine oculis gene expression and hence eye development.

2011

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

Carine Delattre-Gubelmann

EPFL2013