Evidence map›Paper›PMID 32328061›Full record

ArticleFrontiers in immunology2020

The TCR Repertoire Reconstitution in Multiple Sclerosis: Comparing One-Shot and Continuous Immunosuppressive Therapies.

Roberta Amoriello, Victor Greiff, Alessandra Aldinucci, Elena Bonechi, Alberto Carnasciali, Benedetta Peruzzi, Anna Maria Repice, Alice Mariottini, Riccardo Saccardi, Benedetta Mazzanti and 2 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 37 citations in OpenAlex.

  1. Article
  2. Review
  3. AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis.Immunoinformatics (Amsterdam, Netherlands) · 2025
    Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Risk HLA Variants Affect the T-Cell Repertoire in Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2023
    Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 2 countries.

Roberta AmorielloDipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Florence, Italy.
Victor GreiffDepartment of Immunology, University of Oslo, Oslo, Norway.
Alessandra AldinucciDipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Florence, Italy.
Elena BonechiDipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Florence, Italy.
Alberto CarnascialiDipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Florence, Italy.
Benedetta PeruzziCentro Diagnostico di Citofluorimetria e Immunoterapia, Careggi University Hospital, Florence, Italy.
Anna Maria RepiceDipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Florence, Italy.
Alice MariottiniDipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Florence, Italy.
Riccardo SaccardiSODc Terapie Cellulari e Medicina Trasfusionale, Careggi University Hospital, Florence, Italy.
Benedetta MazzantiDipartimento di Medicina Sperimentale e Clinica (DMSC), University of Florence, Florence, Italy.
Luca MassacesiDipartimento di Neuroscienze, Psicologia, Area del Farmaco e Salute del Bambino (NEUROFARBA), University of Florence, Florence, Italy.
Clara BalleriniDipartimento di Medicina Sperimentale e Clinica (DMSC), University of Florence, Florence, Italy.
University of Florence · ITAzienda Ospedaliero-Universitaria Careggi · ITUniversity of Oslo · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natalizumab (NTZ) and autologous hematopoietic stem cell transplantation (AHSCT) are two successful treatments for relapsing-remitting multiple sclerosis (RRMS), an autoimmune T-cell-driven disorder affecting the central nervous system that is characterized by relapses interspersed with periods of complete or partial recovery. Both RRMS treatments have been documented to impact T-cell subpopulations and the T-cell receptor (TCR) repertoire in terms of clone frequency, but, so far, the link between T-cell naive and memory populations, autoimmunity, and treatment outcome has not yet been established hindering insight into the post-treatment TCR landscape of MS patients. To address this important knowledge gap, we tracked peripheral T-cell subpopulations (naïve and memory CD4+ and CD8+) across 15 RRMS patients before and after two years of continuous treatment (NTZ) and a single treatment course (AHSCT) by high-throughput TCRß sequencing. We found that the two MS treatments left treatment-specific multidimensional traces in patient TCRß repertoire dynamics with respect to clonal expansion, clonal diversity and repertoire architecture. Comparing MS TCR sequences with published datasets suggested that the majority of public TCRs belonged to virus-associated sequences. In summary, applying multi-dimensional computational immunology to a TCRß dataset of treated MS patients, we show that qualitative changes of TCRß repertoires encode treatment-specific information that may be relevant for future clinical trials monitoring and personalized MS follow-up, diagnosis and treatment regimes. Natalizumab (NTZ) and autologous hematopoietic stem cell transplantation (AHSCT) are two successful treatments for relapsing-remitting multiple sclerosis (RRMS), an autoimmune T-cell-driven disorder affecting the central nervous system that is characterized by relapses interspersed with periods of complete or partial recovery. Both RRMS treatments have been documented to impact T-cell subpopulations and the T-cell receptor (TCR) repertoire in terms of clone frequency, but, so far, the link between T-cell naive and memory populations, autoimmunity, and treatment outcome has not yet been established hindering insight into the posttreatment TCR landscape of MS patients. To address this important knowledge gap, we tracked peripheral T-cell subpopulations (naive and memory CD4+ and CD8+) across 15 RRMS patients before and after 2 years of continuous treatment (NTZ) and a single treatment course (AHSCT) by high-throughput TCRβ sequencing. We found that the two MS treatments left treatment-specific multidimensional traces in patient TCRβ repertoire dynamics with respect to clonal expansion, clonal diversity, and repertoire architecture. Comparing MS TCR sequences with published datasets suggested that the majority of public TCRs belonged to virus-associated sequences. In summary, applying multidimensional computational immunology to a TCRβ dataset of treated MS patients, we show that qualitative changes of TCRβ repertoires encode treatment-specific information that may be relevant for future clinical trials monitoring and personalized MS follow-up, diagnosis, and treatment regimens.

Indexed as

AdultCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesFemaleHematopoietic Stem Cell TransplantationHumansImmunologic FactorsImmunosuppression TherapyMaleMiddle AgedMultiple Sclerosis, Relapsing-RemittingNatalizumabReceptors, Antigen, T-Cell, alpha-betaTransplantation, AutologousImmunologic FactorsNatalizumabReceptors, Antigen, T-Cell, alpha-betadisease-modifying therapiesmultiple sclerosissystem immunologyT-cell repertoire diversityT-cell subpopulations

Identifiers

PMID32328061
PMCPMC7160336
OpenAlexW3015232327

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.