Biology subjects

Mariotti-Ferrandiz, E.

Publications and source records attributed to Mariotti-Ferrandiz, E..

12 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↗

bioRxiv2025

Transcriptomic module fingerprint reveals heterogeneity of whole blood transcriptome in type 1 diabetic patients

BackgroundType 1 diabetes (T1D) is a complex autoimmune disease resulting from {beta}-cell destruction in the pancreas. Yet, no gene signature derived from whole blood samples has been established to differentiate T1D patients from healthy donors, likely because of the high heterogeneity of the underlying pathophysiological mechanisms. MethodsWe analysed whole blood transcriptomic profiles from 39 patients and 43 healthy donors, collected as part of our observational clinical trial, using a multi-step multivariate statistical analysis. This approach combined classical differential analysis, random forest and support vector machine classifications, gene set functional enrichment analysis to construct the most stable and reliable gene signature. ResultsClassical differential analysis did not separate clearly samples into healthy and T1D clusters, but rather spread samples into three clusters. On the contrary, we show that our approach, which combined molecular signatures independently constructed, adds robustness to the analysis without compromising the specificity. This efficiency was demonstrated by the clear separation of the samples according to their diagnosis group. Also, the functional annotation of the gene modules that we obtained was more associated with T1D-related pathways compared to the classical statistical analysis. ImpactsThese results emphasize that single differential analyses are not able to capture immune continuums involved in such a complex pathophysiological process. We hypothesize that T1D patients can have different molecular pathways involved in their pathology or/and can display unsynchronized -omics profiles. These findings call for further investigations to identify the molecular pathways involved in T1D patients, and for revising the nosology of autoimmune diseases.

bioinformatics↗

bioRxiv2024

Benchmarking unsupervised methods for inferring TCR specificity

Identifying T cell receptor (TCR) specificity is crucial for advancing the understanding of adaptive immunity. Despite the development of computational methods to infer TCR specificity, their performance has not been thoroughly compared. We addressed this by curating a unified database of 190,670 human TCRs with known specificities for 2,313 epitopes across 121 organisms, combining data from IEDB, McPAS-TCR, and VDJdb. Nine methods for clustering TCRs based on similarity were benchmarked against this dataset. DeepTCR demonstrated the best retention, while ClusTCR, TCRMatch, and GLIPH2 excelled in cluster purity but had lower retention. DeepTCR, Levenshtein distance, and TCRdist3 generated large clusters, with DeepTCR showing high purity. Smaller, antigen-specific clusters were produced by Hamming distance, GIANA, and iSMART. GLIPH2 and DeepTCR were the most sensitive in capturing antigen-specific TCRs. This study offers a unified TCR database and a benchmark of specificity inference methods, guiding researchers in selecting appropriate tools.

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↗

bioRxiv2024

AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis

The analysis of bulk adaptive immune receptor repertoires (AIRR) enables the understanding of immune responses in both normal and pathological conditions. However, the complexity of AIRR calls for advanced, specialized methods to extract meaningful biological insights. These sophisticated approaches often present challenges for researchers with limited bioinformatics expertise, hindering access to comprehensive immune system analysis. To address this challenge, we developed AnalyzAIRR, an AIRR-compliant R package enabling advanced bulk AIRR sequencing data. The tool integrates state-of-the-art statistical and visualization methods applicable at various levels of granularity. It offers a platform for general data exploration, filtering and manipulation, and in-depth cross-comparisons of AIRR datasets, aimed at answering specific biological questions. We illustrate AnalyzAIRR functionalities using of a published murine dataset of 18 T-cell receptor repertoires from three diferrent T cell subsets. We first detected and removed a major contaminant in a group of samples, before proceeding with to the comparative analysis. Subsequent cross-sample analysis revealed differences in repertoire diversity that aligned with the respective cell phenotypes, and in repertoire convergence among the studied subsets. AnalyzAIRRs set of analytical metrics is integrated into a Shiny web application and complemented with a tutorial to help users in their analytical strategy, making it user-friendly for biologists with little or no background in bioinformatics.

bioinformatics↗

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↗

bioRxiv2023

Tissue-resident uterine regulatory T cells support fetal growth

Regulatory T cells (Tregs) are known to contribute to successful allogeneic pregnancy by promoting maternal-fetal tolerance. Here, phenotypic studies and parabionts identified a population of uterine tissue-resident Tregs (uTregs) with trophic functions in the non-pregnant mouse endometrium. Phenotypically, uTregs are CD25-/lo and they present characteristic phenotypic attributes of tissue-resident memory T cells with effector functions. Transcriptionally, uTregs are distinguishable from other peripheral or tissue-resident Tregs by their expression of genes related to extracellular matrix remodeling and vasculogenesis, and they express a polyclonal T cell receptor (TCR) repertoire. Pregnancy triggers the expansion of uTregs, which remain CD25-/lo, retain their polyclonal TCR repertoire, and increase the expression of genes involved in regulation of hypoxia and vasculogenesis. Functionally, we show that uTregs support the proliferation of primary uterine microvascular endothelial cells in vitro and that a specific depletion of uTregs in pregnant mice leads to fetal intra-uterine growth retardation (IUGR). Thus, a previously unidentified population of CD25-/lo uTregs promotes uterine tissue remodeling before and during pregnancy, and contributes to fetal growth.

immunology↗

bioRxiv2020

Human thymopoiesis selects unconventional CD8+ α/β T cells that respond to multiple viruses.

T cell receptors (TCRs) are formed by stochastic gene rearrangements, theoretically generating >1019 sequences. They are selected during thymopoiesis, which releases a repertoire of about 108 unique TCRs per individual. How evolution shaped a process that produces TCRs that can effectively handle a countless and evolving set of infectious agents is a central question of immunology. The paradigm is that a diverse enough repertoire of TCRs should always provide a proper, though rare, specificity for any given need. Expansion of such rare T cells would provide enough fighters for an effective immune response and enough antigen-experienced cells for memory. We show here that human thymopoiesis releases a large population of CD8+ T cells harboring /{beta} paired TCRs that (i) have high generation probabilities and (ii) a preferential usage of some V and J genes, (iii) are shared between individuals and (iv) can each recognize and be activated by multiple unrelated viral peptides, notably from EBV, CMV and influenza. These polyspecific T cells may represent a first line of defense that is mobilized in response to infections before a more specific response subsequently ensures viral elimination. Our results support an evolutionary selection of polyspecific /{beta} TCRs for broad antiviral responses and heterologous immunity.

immunology↗

bioRxiv2019

Characterizing blood modular transcriptional repertoire perturbations in patients with RSV infection: a hands-on workshop using public datasets as a source of training material

Biomarkers to assess the severity of acute respiratory syncytial virus (RSV) infection are needed. We conducted a meta-analysis of 490 unique profiles from six public RSV blood transcriptome datasets. A repertoire of 382 well-characterized transcriptional modules was used to define dominant host responses to RSV infection. The consolidated RSV cohort was stratified according to four traits: "interferon response" (IFN), "neutrophil-driven inflammation" (Infl), "cell cycle" (CC), and "erythrocytes" (Ery). Eight prevalent blood transcriptome phenotypes were thus identified. Among those three Ery+ phenotypes comprised higher proportions of patients requiring intensive care. We posit that the erythrocyte module is linked to an overabundance of immunosuppressive erythroid cells that might underlie progression to severe RSV infection. These findings outline potential priority areas for biomarker development and investigations into the immune biology of RSV infection. The approach that was employed here will also permit to delineate prevalent blood transcriptome phenotypes in other settings.

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↗

bioRxiv2018

Representativeness and robustness of TCR repertoire diversity assessment by high-throughput sequencing

High-throughput sequencing (HTS) has the potential to decipher the diversity of T cell repertoires and their dynamics during immune responses. Applied to T cell subsets such as T effector and T regulatory cells, it should help identify novel biomarkers of diseases. However, given the extreme diversity of TCR repertoires, understanding how the sequencing conditions, including cell numbers, biological and technical sampling and sequencing depth, impact the experimental outcome is critical to properly use of these data. Here we assessed the representativeness and robustness of TCR repertoire diversity assessment according to experimental conditions. By comparative analyses of experimental datasets and computer simulations, we found that (i) for small samples, the number of clonotypes recovered is often higher than the number of cells per sample, even after removing the singletons; (ii) high sequencing depth for small samples alters the clonotype distributions, which can be corrected by filtering the datasets using Shannon entropy as a threshold; (iii) a single sequencing run at high depth does not ensure a good coverage of the clonotype richness in highly polyclonal populations, which can be better covered using multiple sequencing. Altogether, our results warrant better understanding and awareness of the limitation of TCR diversity analyses by HTS and justify the development of novel computational tools for improved modelling of the highly complex nature of TCR repertoires.

immunology↗

bioRxiv2017

High-resolution repertoire analysis of Tfr and Tfh cells reveals unexpectedly high diversities indicating a bystander activation of follicular T cells

T follicular helper (Tfh) and regulatory (Tfr) cells regulate B cell activation and ultimately antibody production. While concordant results show that Tfh cells are specific for the immunizing antigens, limited and even controversial results have been reported regarding the specificity of Tfr cells. Here we used high-throughput T cell receptor (TCR) sequencing to address this issue. We observed that although the Tfh- and Tfr-cell repertoires are less diverse than those of effector (Teff) and regulatory T (Treg) cells, they still represent thousands of clonotypes after immunization with a single antigen. T-cell receptor beta variable (TRBV) gene usage distinguishes both follicular T cells (Tfol) from non-Tfol cells, as well as helper (Teff and Tfh) vs. regulatory (Treg and Tfr) cells. Analysis of the sharing of clonotypes between samples revealed that a specific response to the immunizing antigen can only be detected in Tfh cells immunized with a non-self-antigen and Tfr cells immunized with a self-antigen. Finally, the Tfr TCR repertoire is more similar to that of Tregs than to that of Tfh or Teff cells. Altogether, our results highlight a bystander Tfol-cell activation during antigenic response in the germinal centres and support the Treg cell origin of Tfr cells.\n\nSignificance StatementFollicular helper T (Tfh) cells promote high-affinity antibody production by B cells while follicular regulatory T (Tfr) cells represses it. The question of the specificity of follicular T (Tfol) cells is of utmost importance in the understanding of the antibody response specificity and our work is the first to analysed the global Tfol TCR repertoire in wild type mice. This allowed us not only to portray the overall global structure of these repertoires, but also to substantiate the fact that Tfr cells respond to self-antigen while Tfh cells respond to non self-antigen, a still controversial issue. Importantly, our work revealed an unexpected bystander activation of Tfol cells. We think and discuss that it has a general significance in immune responses and possibly immunopathologies.

immunology↗