The membrane-bound mucin MUC1 is expressed at the apical pole of epithelial cells and bears a long glycosylated extracellular domain (500 nm). MUC1 interacts with ErbB receptor family and plays a role in signal transduction. MUC1 contributes to tumour invasion by simultaneously disrupting existing interactions between opposing cells (anti-adhesion) and establishing new ligands for interaction between the invading cell and the adjoining cells (adhesion). Cancer cells may also use mucins to protect themselves from adverse cellular growth conditions and control the molecular microenvironment during invasion and metastasis. In Renal clear cell carcinoma (RCC), immunohistochemical studies have shown that MUC1 is frequently overexpressed. Its level of expression is correlated with Fuhrman grade and tumour progression and delocalization of its expression is associated with a worse prognosis. Clinical data suggest that MUC1 might be involved in renal tumour progression. In this project, we will test this hypothesis and evaluate the role of MUC1 on RCC by using in vitro and in vivo models. Recently, we have observed an epithelial-mesenchymal transition (EMT) concomitant to the overexpression of MUC1 in two human renal cell lines. EMT is an important mechanism during development, tissue regeneration and tumour progression (metastasis). To better understand the potential role of MUC1 during EMT, we will develop a murine model of renal regeneration. Our goals are (i) to decipher the role of MUC1 in renal tumour and regeneration, (ii) to identify new therapeutic targets and (iii) to propose new biological tools to the clinicians to ameliorate patient care.

Patients with metabolic diseases are affected by a loss of microbiome species and a reduced number of metabolites detected in their blood, which hints that the missed metabolites may be top mechanistic mediator candidates of the human-gut microbiome interaction. For a better understanding of this interaction, improved metabolomics strategies must be developed, capable of exhaustive characterizations and of delving into the previously undetected metabolites (the co-called “dark metabolome”). Our objective is to illuminate the blood “dark metabolome” with the development of an “all-in-all” framework combining state-of-the-art instrumentation with analytical methods using computational approaches (such as chemoinformatics and chemometrics) for addressing key roadblocks in the field of liquid chromatography coupled to high-resolution mass spectrometry metabolomics and, in doing so, bringing altogether next generation metabolomics. This analytical framework will be developed at 3 levels, corresponding to my 3 work tasks: Task 1, for maximizing the number of mass fragmentation spectra obtained; Task 2, for increasing the number of annotations and the quality of these annotations; and Task 3, for the design of data-driven methods to evaluate the role of the new detected compounds. I will set up this framework with plasma samples from the Euroepan cohort study METACARDIS, descriptive of individuals with cardiometabolic diseases, and validate the biological findings with samples of the Lille cohort study Heart&Brain (comprising diabetic patients without initial diabetes-related complications). To sum up, this project will enhance our understanding of the underexplored microbiome-derived metabolites present in the human blood, as well as their roles in metabolism and physiology, that could ultimately lead to new diagnostic tools and novel treatment and prevention strategies.