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Reference : UMR5089-ANNGON-002
Workplace : TOULOUSE
Date of publication : Tuesday, September 10, 2019
Scientific Responsible name : Anne Gonzalez de Peredo/ Yoann Rombouts
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 November 2019
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly
Description of the thesis topic
Innate immunity against infectious diseases rely on a large range of receptors expressed at the surface of macrophages and other immune cells, that are able to recognize pattern motifs from pathogens. C-type lectin receptors (CLRs) represent a family of such pattern recognition receptors, which can be classified based on their intracellular signaling domains, with either activating or inhibitory properties. However, there is an important flexibility in the signaling and effector outcomes that can be triggered by the different CLRs containing a particular canonical motif, depending on many factors such as their specific amino acid sequence, as well as their multimerization state, the formation of hetero-complexes, or the affinity of their stimulating ligand(s). The molecular mechanisms underlying these signalling events and the way they can be modulated are still largely unexplored.
Phosphoproteomics has emerged in the last decade as a powerful approach to characterize extensively and in a hypothesis-free fashion the phosphorylation cascades associated to signalling pathways in biological systems. Recent advances in mass-spectrometry (MS) has greatly improved our ability to finely map such networks, with high sensitivity and in a quantitative manner. This makes now possible not only an in-depth characterization of the cellular phosphoproteome at steady state, but also the analysis of its dynamics upon stimulation of cells, as well as the comparison of many different stimulation conditions. Such studies are performed on high-throughput instruments, generating raw data that must be processed with dedicated bioinformatic tools. Datasets must then be submitted to statistical analysis and mined to extract biological information, reconstruct networks, kinetics, kinase-substrate relationships and perform functional annotation.
The aim of this PhD project will be to dissect the different signalling pathways engaged upon stimulation of various macrophage CLRs, using large-scale phosphoproteomic approaches, and analyse the factors that can modulate these signalling mechanisms and influence their inflammatory outcome. It will thus involve biochemical sample preparation, and proteomic analysis of the samples by mass spectrometry on last generation instruments. An important part of the project will also be devoted to bioinformatic analysis and data mining. These studies will increase our current knowledge about CLRs and their role in the initiation and regulation of immunity and inflammation. They will lead to the identification of novel signalling molecules that will be further studied in relevant macrophage models for functional validation.
This is a collaborative project between the infection biology and immunology group headed by Dr. O. Neyrolles and the proteomics group headed by Dr. O. Schiltz at IPBS. The student will benefit from the strong scientific background in infection biology and innate immunity provided by the group of O. Neyrolles, which has been working for many years in the field of host-pathogen interactions and immunity to M. tuberculosis. He/she will be supervised in this lab by Dr. Y. Rombouts, a glycobiologist currently working on C-type lectins and macrophage biology in the context of tuberculosis. The analytical part will be performed in the proteomics team, under the supervision of Dr. A. Gonzalez de Peredo for phosphoproteomics analysis. The group has advanced expertise in mass spectrometry and protein analysis methods, and will provide state-of-the art instrumentation on high-speed mass spectrometers. It has also been involved in the development of new software tools for proteomics, and will offer an optimal environment for data processing and bioinformatic analysis.
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