Mots-Clés

biomarkers kidney transplantation prediction genomics transcriptomics data integration multi-omics machine learning

Description

Title: Prediction of chronic kidney allograft rejection from blood “omics” biomarkers and clinical data

Supervisors: Pr. Sophie Limou (sophie.limou@ec-nantes.fr) and Pr Bertrand Michel (bertrand.michel@ec-nantes.fr)

Location: Center for Research in Transplantation and Translational Immunology (CR2TI), UMR1064, CHU de Nantes, 30 bd Jean Monnet, Nantes, France.

Background: Kidney transplantation is currently the best treatment for end-stage kidney disease, improving both patient survival and quality of life. Development of efficient immunosuppressive treatments has significantly improved the 1-year graft survival rate, but the long-term graft survival has remained stable over the past 30 years (60% at 10-years) and is still a major issue in kidney transplantation. Limiting chronic graft loss is all the more essential as the number of patients on waiting list has been increasing for a stable number of available grafts over the last decades. Graft loss is a multifactorial event, with a central role for immune responses against the graft. However, the molecular triggers are still largely unknown and the treatment options are limited, which call for further investigation.

Objectives: Our multi-omics project ambitions to generate multiple layers of molecular features in a well-defined clinical cohort to pinpoint the molecular mechanisms at stake in chronic kidney graft rejection pathophysiology and to predict graft rejection from non-invasive blood biomarkers. For that, we collected transcriptomic (RNAseq and miRNAseq) data on 167 blood samples from kidney transplanted patients experiencing or not chronic allograft rejection. In addition, we have collected GWAS genomic data for over 4,400 individuals. The intern will leverage these large datasets to investigate the impact of blood biomarkers (genetic or expression level) from kidney transplanted patients on chronic allograft rejection and prioritize molecular regulation networks. In a second step, the intern will assess the predictive and diagnosis potential of the identified biomarkers using AI tools. This internship will require the use of genomic and bioinformatic tools (e.g. Snakemake, bcftools) and the manipulation of R and Python packages.

Important: The internship is adapted for bioinformaticians or data scientists with an interest for the biomedical field (the biological skills can be acquired during the internship).