Mots-Clés
autoimmunity
immune receptor repertoires
systems immunology
Description
Project Title: Identifying prognostic biomarkers in BCR/TCR repertoires in Sjögren’s disease
Supervisors: Encarnita Mariotti-Ferrandiz (encarnita.mariotti@sorbonne-universite.fr) & Adrien Six (adrien.six@sorbonne-universite.fr)
Tools and methodologies: high-throughput sequencing analysis; bioinformatics; machine learning
Summary of lab’s interests: The i3 laboratory is interested in systems immunology approaches to identify novel biomarkers for diagnostic and therapy with a particular focus on autoimmune disorders (AD). One of the unique expertise of the laboratory consists in identifying antigen-specific T cells though the analysis of T cell receptor repertoire using deep sequencing.
Summary of the proposed project: Immune dysregulation caused by systemic autoimmune diseases can lead to lymphoma: the immune system moves beyond attacking the host to create a microenvironment conducive to malignant transformation. A striking example is Sjögren’s disease (SjD), a chronic condition marked by the immune-mediated destruction of salivary and lacrimal glands. While primary symptoms severely impair quality of life, SjD patients face a high risk of non-Hodgkin lymphoma (NHL), with an incidence 15 to 20 times higher than the general population (PMID: 16287762). The mechanisms governing this transition from benign autoimmunity to malignancy remain poorly understood, which results in a lack of reliable biomarkers for patient stratification, particularly for those at high risk of lymphoma.
The central driver of SjD is thought to be the persistent crosstalk between T and B cells, evidenced by the formation of germinal center (GC)-like structures within the salivary glands. Over the past decade, research of T- and B-cell receptor (TCR and BCR) repertoires, grouped under the term Adaptive Immune Receptor Repertoire (AIRR), has advanced significantly and now provides the resolution and throughput capacity necessary to decipher and co-analyze these antigen-dependent responses at the clonal level. Demonstrating this capacity, the host laboratory developed computational tools identifying specific immune receptor signatures in blood to predict cardiac repair following cardiovascular disease (PMID: 42522569), proving these repertoires constitute true molecular fingerprints that predict distinct clinical outcomes.
The project is based on the hypothesis that the clinical progression of SjD and its evolution towards lymphoproliferative complications result from antigen-dependent immune responses that can now be studied at clonal resolution. The objective is to conduct a joint analysis of paired bulk BCR and TCR repertoires in SjD to identify novel biomarkers of disease progression to lymphoma. To achieve this, the applicant will benefit from access to already generated datasets of bulk TCR and BCR repertoires from an established clinical cohort stratified by lymphoma risk, as well as from healthy volunteers. These bulk TCR/BCR repertoire datasets were derived from sorted circulating subsets of B and T cells. The applicant will work within a strong, collaborative team of bioinformaticians with deep expertise in systems immunology, AIRR analysis, and machine learning. The candidate will have full access to dedicated bioinformatic resources and high-performance computing infrastructure optimized for large-scale data storage and processing.
Candidate profile: The candidate is expected to have a training in in bioinformatics/computational biology, or in immunology with strong computational skills, or in mathematics/computational field with interest in systems biology approaches.
Lab description: The laboratory is offering a unique interdisciplinary environment, with biologists, immunologists, clinicians, computer scientists and bioinformaticians. The candidates will be based in the i3 laboratory located on the Pitié‐Salpêtrière hospital campus in Paris (13ème).
Supervisor’s publications (related to the project):
Le Gouge K et al, Cardiovasc Res 2026 ;
Albalaa C et al, Immunol Rev 2026 ;
Glauzy S et al, Arthritis&Rheumatology 2017 ;
Mhanna V et al, Diabetes 2021 ;
Barennes P et al, Nature Biotechnologies 2020 ;
Saadoun D et al, Arthritis&Rheumatology 2013