Paul Stys

Pierre Barennes

Michèle Barbié

Roberta Lorenzon

Marie Surroque

Alexandra Roux

Michelle Rozenzwagj

Adrien Six

Encarnita Mariotti-Ferrandiz

David Klatzmann

TCR repertoire as biomarker of immune diseases : applications to autoimmune diseases and COVID-19

ü We developed a general pipeline that identifies discriminant TCR signatures from circulating blood ü This strategy works well for a viral disease (COVID-19) as well as for several AIDs applied to Teff as well as Treg TCR repertoires. Analyzing cell subtypes will allow a better understanding of the underlying mechanisms.

ü We show how a signature can be used as a biomarker of immune diseases and their progression q We still need to characterize the specificity of these signatures

Adapta0ve T-cell repertoire diversity

The T-cell repertoire (TCR) is a dynamic biological object whose modifica9ons will dependent on cell popula9ons and the amplitude of the response according to the types of an9gens encountered (microbial, self-an9gens). We launched two observa9onal trial, TRANSIMMUNOM (NCT02466217) to revisit the nosology of autoimmune diseases (AIDs) and SirocCo (ANR-21-CO12-0005) to inves9gate the mechanisms of cell subsets in COVID-19 pa9ents. For each trial, we sorted the CD4 T-cell subpopula9ons from peripheral blood: effector T-cells (Teff) and regulatory T-cells (Treg), and sequenced their TCR𝛼β repertoire. The radviz represents the CDR3aa expression of the 4 AID signatures for each paJent. The signatures were calculated on 75% of the dataset of each disease. 6 random draws of the dataset were made (iteraJons). Finally, for each iteraJon and each signature, the equivalent number of unique CDR3aa was randomly selected from the corresponding dataset. The random signatures do not separate the pa9ents, regardless of the iteraJons, which shows that the strategy is reproducible and that the results are not due to chance. Machine learning models were trained on the signatures obtained on 75% of the dataset. The models were then tested on the remaining 25% of the data. The results are compared to a random selecJon of unique CDR3aa whose size is equivalent to the signature of each condiJon (disease and cell subsets). The boxplots represent the percentage of paJents, from the naive data, correctly classified. In all cases, the models derived from the signatures predict berer than random signature. Moreover, the signatures correctly predicted the disease of a pa9ent in 81% to 100% depending on the condiJons.

Laboratory dataset

Classifica0on

External dataset

RA

The RA Teff β signature was searched in the Spreafico et al. dataset with a Leveinstein distance of 1. Samples were taken from blood and synovial fluid. CirculaJng pathogenic lymphocytes (CPL) idenJfied by Spreafico in juvenile idiopathic arthriJs (JIA) paJents are correlated with disease acJvity. The RA signature from Transimmunom is staJsJcally more present in CPL compared to whole blood but also more found in synovial fluid than in whole blood without being significant. The signature is thus found in another dataset, treated differently, and also assimilated to the disease.

ECMO and death recovered

HALTCR•

  is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come   L'archive ouverte pluridisciplinaire est destinée au dépôt   Distributed under a Creative Commons Attribution -NonCommercial -NoDerivatives| 4.0 International License TCR repertoire as biomarker of immune diseases : applications to autoimmune diseases and COVID-19 Paul Stys, Pierre Barennes, Encarnita Mariotti-Ferrandiz, David Klatzmann To cite this version: Paul Stys, Pierre Barennes, Encarnita Mariotti-Ferrandiz, David Klatzmann. TCR repertoire as biomarker of immune diseases : applications to autoimmune diseases and COVID-19. AIRR Community Meeting VI, May 2022, San diego, United States. hal-03976936 Iden%fy disease-specific TCRs from the repertoire of sorted T cells • Determine the specificity and biological roles of these TCRs to be4er understand the diseases • Use these sequences to classify and predict pa9ent outcome Paul Stys 1 ★ , Pierre Barennes 1,2 ★ , Michèle Barbie 2 , Roberta Lorenzon 2 , Marie Surroque 2 , Alexandra Roux 2 , Transimmunom Consor?um, Michelle Rozenzwagj 1,2 , Adrien Six 1 , Encarnita MarioD-Ferrandiz 1 ♦ , David Klatzmann 1,2♦ 1. Sorbonne Université, INSERM, Immunology-Immunopathology-Immunotherapy (i3), Paris, France ; 2. AP-HP, Hôpital PiJé-Salpêtrière, Biotherapy (CIC-BTi) and InflammaJon-Immunopathology-Biotherapy Department (i2B), Paris, France ★ These authors contributed equally to this work ; ♦ These authors contributed equally to this work • Lorenzon R, MarioX-Ferrandiz E, Aheng C, et al (2018) Clinical and mulJ-omics crossphenotyping of paJents with autoimmune and autoinflammatory diseases: the observaJonal TRANSIMMUNOM protocol BMJ Open 2018;8:e021037 • Barennes, P., Quiniou, V., Shugay, M. et al. (2021) Benchmarking of T cell receptor repertoire profiling methods reveals large systemaJc biases. Nat Biotechnol 39, 236-245 • Spreafico R, RosseX M, van Loosdregt J, et al. A circulaJng reservoir of pathogenic-like CD4+ T cells shares a geneJc and phenotypic signature with the inflamed synovial microenvironment. Ann Rheum Dis. 2016;75(2):459-465. doi:10.1136/annrheumdis-2014-206226 • Nolan, Sean et al. "A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associaJons from natural and syntheJc exposure to SARS-CoV-2." Research squarers.3.rs-51964. 4 Aug. 2020, doi:10.21203/rs.3.rs-51964/v1. Preprint. • Schultheiß, Christoph et al. "Next-GeneraJon Sequencing of T and B Cell Receptor Repertoires from COVID-19 PaJents Showed Signatures Associated with Severity of Disease" Immunity, doi:10.1016/j.immuni.2020.06.024.

  Ward clustering methods applied on AID (top) and COVID-19 (borom) signatures discriminate samples with the same unique CDR3aa sequences from each idenJfied signature (here, for CD4 Teff alpha and beta chains, there is one enriched and one depleted signature for each disease). The selecJon of CDR3aa works well on a viral disease like COVID-19 since the clustering shows a perfect separa9on of paJents from HV. The strategy also separates AIDs pa9ents from HVs but to a lesser extent.

  lev1 proportion by patient repertoire (%)

  idenJfied on paJents recovering from COVID-19 on the Nolan et al. dataset. It was compared to the Schultheiß et al. dataset with a Leveinstein distance of 1. This dataset has the parJcularity to contain paJents who died from COVID-19 or had severe forms (ECMO) and paJents in recovery. The idenJfied signature is much more found in the larer, and more and more over Jme. This allows us to predict pa9ent outcomes.