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T cells, more than antibodies, may prevent symptoms developing from respiratory syncytial virus infections in older adults.

Salaun, B; De Smedt, J; Vernhes, C; Moureau, A; Öner, D; Bastian, AR; Janssens, M; Balla-Jhagjhoorsingh, S; Aerssens, J; Lambert, C; et al. Salaun, B; De Smedt, J; Vernhes, C; Moureau, A; Öner, D; Bastian, AR; Janssens, M; Balla-Jhagjhoorsingh, S; Aerssens, J; Lambert, C; Coenen, S; Butler, CC; Drysdale, SB; Wildenbeest, JG; Pollard, AJ; Openshaw, PJM; Bont, L (2023) T cells, more than antibodies, may prevent symptoms developing from respiratory syncytial virus infections in older adults. Front Immunol, 14. p. 1260146. ISSN 1664-3224 https://doi.org/10.3389/fimmu.2023.1260146
SGUL Authors: Drysdale, Simon Bruce

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Abstract

INTRODUCTION: The immune mechanisms supporting partial protection from reinfection and disease by the respiratory syncytial virus (RSV) have not been fully characterized. In older adults, symptoms are typically mild but can be serious in patients with comorbidities when the infection extends to the lower respiratory tract. METHODS: This study formed part of the RESCEU older-adults prospective-cohort study in Northern Europe (2017-2019; NCT03621930) in which a thousand participants were followed over an RSV season. Peripheral-blood samples (taken pre-season, post-season, during illness and convalescence) were analyzed from participants who (i) had a symptomatic acute respiratory tract infection by RSV (RSV-ARTI; N=35) or (ii) asymptomatic RSV infection (RSV-Asymptomatic; N=16). These analyses included evaluations of antibody (Fc-mediated-) functional features and cell-mediated immunity, in which univariate and machine-learning (ML) models were used to explore differences between groups. RESULTS: Pre-RSV-season peripheral-blood biomarkers were predictive of symptomatic RSV infection. T-cell data were more predictive than functional antibody data (area under receiver operating characteristic curve [AUROC] for the models were 99% and 76%, respectively). The pre-RSV season T-cell phenotypes which were selected by the ML modelling and which were more frequent in RSV-Asymptomatic group than in the RSV-ARTI group, coincided with prominent phenotypes identified during convalescence from RSV-ARTI (e.g., IFN-γ+, TNF-α+ and CD40L+ for CD4+, and IFN-γ+ and 4-1BB+ for CD8+). CONCLUSION: The evaluation and statistical modelling of numerous immunological parameters over the RSV season suggests a primary role of cellular immunity in preventing symptomatic RSV infections in older adults.

Item Type: Article
Additional Information: Copyright © 2023 Salaun, De Smedt, Vernhes, Moureau, Öner, Bastian, Janssens, Balla-Jhagjhoorsingh, Aerssens, Lambert, Coenen, Butler, Drysdale, Wildenbeest, Pollard, Openshaw and Bont. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: CD4+ T cell, T-cell memory, antibody function, cell-mediated immunity, correlate of protection, interferon-gamma, machine learning, respiratory syncytial virus, Humans, Aged, Respiratory Syncytial Virus Infections, T-Lymphocytes, Cohort Studies, Prospective Studies, Convalescence, Respiratory Syncytial Virus, Human, Antibodies, Viral, T-Lymphocytes, Humans, Respiratory Syncytial Virus, Human, Respiratory Syncytial Virus Infections, Convalescence, Antibodies, Viral, Cohort Studies, Prospective Studies, Aged, respiratory syncytial virus, cell-mediated immunity, correlate of protection, interferon-gamma, T-cell memory, CD4+T cell, antibody function, machine learning, 1107 Immunology, 1108 Medical Microbiology
SGUL Research Institute / Research Centre: Academic Structure > Infection and Immunity Research Institute (INII)
Journal or Publication Title: Front Immunol
ISSN: 1664-3224
Language: eng
Dates:
DateEvent
13 October 2023Published
25 September 2023Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
116019Innovative Medicines Initiative 2 Joint UndertakingUNSPECIFIED
PubMed ID: 37936699
Web of Science ID: WOS:001094840400001
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/115931
Publisher's version: https://doi.org/10.3389/fimmu.2023.1260146

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