Biology subjects

Sellam, J.

Publications and source records attributed to Sellam, J..

4 recordsLinked to original sources

bioRxiv2026

A regulatory T cell signature provides a shared molecular basis for the therapeutic window of opportunity in rheumatic disease

Rheumatic diseases, including rheumatoid arthritis (RA), spondyloarthritis (SpA) and osteoarthritis (OA), show distinct phenotypes yet respond to overlapping therapies, implicating shared immune mechanisms. In the Transimmunom cohort, we profiled peripheral blood from 240 individuals (47 healthy, 44 OA, 91 RA, 58 SpA) across deep immunophenotyping, immunoproteomics and Treg-Teff transcriptomics. Single-layer analyses revealed broader Treg than Teff remodeling, along with a shared pattern of reduced activated Tregs and expanded Helios+ Tregs across all diseases, alongside a decrease in functional Treg subpopulations, including CTLA4+ and CD45RA- Tregs. In RA specifically, LAG3+ Tregs were also expanded. Combining omics layers outperformed single-layer approaches for disease classification. Among individual layers, Treg transcriptomes were most discriminative, and integration uncovered disease-specific programs. Unsupervised clustering identified a cross-disease cluster independent of activity, treatment and age, mapping to early disease (<= years) and dominated by a Treg dysfunction-associated program. These results provide a biological rationale for the therapeutic "window of opportunity" concept and duration-stratified Treg-directed trials.

immunology↗

bioRxiv2024

Immunological Profiling in Knee Osteoarthritis: Treg Dysfunction as Key Driver of Pain

Pain is the hallmark symptom of osteoarthritis (OA) and its biological drivers remain poorly understood. While the role of innate immunity in OA has been extensively studied, the involvement of adaptive immunity, in particular regulatory T cells (Tregs), is not well understood. Using a comprehensive multi-omic approach on the peripheral blood from 46 knee OA patients with similar radiographic stage, including deep immunophenotyping, cytokine profiling, transcriptomic and T-cell receptor analysis on sorted CD4 Tregs and effector T cells (Teff), we identified an immunological signature associated with OA-related pain. Cytokines promoting Treg expansion and activation (with increases of sIL2-RA, sTNFR1, sTNFR2) were correlated with the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain subscore, suggesting a potential Treg dysfunction. Nineteen T cell subsets were correlated with WOMAC pain. Notably, we found a negative correlation of cell subsets associated with Treg expansion and activation (FoxP3+CTLA4+, CD4+CD57+, Treg CD95+, CD4 Treg CD45RA-). Differential gene expression analysis between patients with low and high WOMAC pain intensity (threshold [≥] 40/100) revealed an upregulation of inflammasome-related genes such as IL1RL1, IL31RA, IFITM3, NLRP3, IFNG in Tregs. Functional enrichment analysis highlighted an overrepresentation of innate immune response, IL-8, and interferon activation pathways suggesting a pro-inflammatory state in Tregs of patients with high pain intensity. Collectively, our systems immunology approach highlights multiple associations between Treg dysfunctionality and OA-related pain, providing new insights into the adaptive immune systems contribution to OA-related pain.

immunology↗

bioRxiv2023

Deciphering Cell-types and Gene Signatures Associated with Disease Activity in Rheumatoid Arthritis using Single Cell RNA-sequencing

ObjectiveSingle cell profiling of synovial tissue has previously identified gene signatures associated with rheumatoid arthritis (RA) pathophysiology, but synovial tissue is difficult to obtain. This study leverages single cell sequencing of peripheral blood mononuclear cells (PBMCs) from patients with RA and matched healthy controls to identify disease relevant cell subsets and cell type specific signatures of disease. MethodsSingle-cell RNA sequencing (scRNAseq) was performed on peripheral blood mononuclear cells (PBMCs) from 18 RA patients and 18 matched controls, accounting for age, gender, race, and ethnicity). Samples were processed using standard CellRanger and Scanpy pipelines, pseudobulk differential gene expression analysis was performed using DESeq2, and cell-cell communication analysis using CellChat. ResultsWe identified 18 distinct PBMC subsets, including a novel IFITM3+ monocyte subset. CD4+ T effector memory cells were increased in patients with moderate to high disease activity (DAS28-CRP [≥] 3.2), while non-classical monocytes were decreased in patients with low disease activity or remission (DAS28-CRP < 3.2). Differential gene expression analysis identified RA-associated genes in IFITM3+ and non-classical monocyte subsets, and downregulation of pro-inflammatory genes in the V{delta} subset. Additionally, we identified gene signatures associated with disease activity, characterized by upregulation of pro-inflammatory genes TNF, JUN, EGR1, IFIT2, MAFB, G0S2, and downregulation of HLA-DQB1, HLA-DRB5, TNFSF13B. Notably, cell-cell communication analysis revealed upregulation of immune-associated signaling pathways, including VISTA, in patients with RA. ConclusionsWe provide a novel single-cell transcriptomics dataset of PBMCs from patients with RA, and identify insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

immunology↗

bioRxiv2018

Clinical data specification and coding for cross-analyses with omics data in autoimmune disease trials

ObjectivesAutoimmune and inflammatory diseases (AIDs) form a continuum of autoimmune and inflammatory diseases, yet AIDs nosology is based on syndromic classification. The TRANSIMMUNOM trial (NCT02466217) was designed to re-evaluate AIDs nosology through clinic-biological and multi-omics investigations of patients with one of 19 selected AIDs. To allow cross-analyses of clinic-biological data together with omics data, we needed to integrate clinical data in a harmonized database.\n\nMaterials and MethodsWe assembled a clinical expert consortium (CEC) to select relevant clinic-biological features to be collected for all patients and a cohort management team comprising biologists, clinicians and computer scientists to design an electronic case report form (eCRF). The eCRF design and implementation has been done on OpenClinica, an open-source CFR-part 11 compliant electronic data capture system.\n\nResultsThe CEC selected 865 clinical and biological parameters. The CMT selected coded the items using CDISC standards into 5835 coded values organized in 28 structured eCRFs. Examples of such coding are check boxes for clinical investigation, numerical values with units, disease scores as a result of an automated calculations, and coding of possible treatment formulas, doses and dosage regimens per disease.\n\nDiscussion21 CRFs were designed using OpenClinica v3.14 capturing the 5835 coded values per patients. Technical adjustment have been implemented to allow data entry and extraction of this amount of data, rarely achieved in classical eCRFs designs.\n\nConclusionsA multidisciplinary endeavour offers complete and harmonized CRFs for AID clinical investigations that are used in TRANSIMMUNOM and will benefit translational research team.

clinical trials↗