Mots-Clés
Kidney transplantation
ABMR
genomic
transcriptomic
eQTL
TWAS
Description
TITLE: Single cell genomic-transcriptomic interaction networks involved in antibody-mediated rejection of kidney allograft.
SUPERVISORS: Agathe Bugnon, Pr. Sophie Limou, Dr. Martin Morin.
LOCALISATION: Centre de Recherche Translationnelle en Transplantation et Immunologie (CR2TI), Inserm UMR1064, CHU de Nantes, 30 bd Jean Monnet, Nantes.
BACKGROUND: Limiting kidney allograft loss is a major challenge in kidney transplantation, especially due to organ scarcity and the growing number of patients on the waiting list. Chronic AntiBody-Mediated Rejection (ABMR) represents the main cause of late transplant failure. However, its underlying pathophysiological mechanisms remain poorly understood beyond the role of HLA donor-recipient compatibility, and its current diagnosis through biopsy is an invasive and risky procedure. To tackle this issue, we employed a multi-omics approach in Peripheral Blood Mononuclear Cells (PBMCs) to describe the molecular interaction networks involved in ABMR of kidney allografts, and thereby identify non-invasive biomarkers of this phenotype. For this purpose, we gathered genomic data (~8 million SNPs) by genotyping 1,969 kidney transplanted donor-recipient pairs from the KiT-GENIE biobank. We also collected transcriptomic data via bulk RNA sequencing (~14,000 expressed genes) from a subset encompassing 167 samples of recipients’ PBMCs. We deciphered genomic-transcriptomic interaction networks, also called expression Quantitative Trait Loci (eQTLs), which define the influence of common genetic variations on gene expression levels. Then, we leveraged these eQTLs in the rest of the KiT-GENIE cohort to conduct a Transcriptome-Wide Association Study (TWAS), allowing us to identify genes associated with ABMR in kidney transplantation.
OBJECTIVES: Since PBMCs constitute a mixed population of several cell types with their own specific transcriptomic profiles, we now aim to study the ABMR molecular interaction networks at the cell-type level. To this end, we have determined the cell proportions and the cell-type specific transcriptomic data (in silico single-cell RNA sequencing for B cells, T cells, NK cells and monocytes) using deconvolution and a reference single-cell RNA sequencing dataset. This internship project aims to define cell-specific eQTLs and TWASs from this data using our in-house scripts and pipelines. A subsequent goal is to compare the biological results obtained in bulk and within each cell type to identify common and cell-specific ABMR signatures, which will contribute to characterize non-invasive biomarkers associated with ABMR in kidney transplantation.
REQUIREMENT: The internship is designed for students interested in multi-omics data analysis. It requires comprehension of some bioinformatics tools (e.g. Snakemake, PLINK/BCFtools, Python and R packages), as well as the ability to compare analytical protocols and interpret biological results (e.g. immunogenetic, cellular and molecular biology).