Hodgson, D. orcid.org/0000-0002-5585-8974, Jarju, S., Gatcombe, L. et al. (16 more authors) (2026) Mucosal IgA to pre-fusion F protein predicts protection from RSV infection in a high burden setting. Nature Communications, 17 (1). 9056. ISSN: 2041-1723
Abstract
Respiratory syncytial virus (RSV) causes over 100,000 annual deaths, predominantly in low- and middle-income countries, yet immunological correlates of protection remain poorly defined in high-burden settings. This study aims to identify immunological correlates of protection against RSV infection, and to compare the relative contribution of systemic and mucosal antibody responses. We use data from an intensively sampled prospective household cohort study in The Gambia, following 342 participants over 12 months with weekly PCR testing and longitudinal serum and nasal sampling. Using a Bayesian hierarchical framework jointly modelling antibody kinetics, household transmission dynamics, and imperfect PCR ascertainment, we characterise humoral immunity to RSV-A antigens across systemic and mucosal compartments. Mucosal IgA to the pre-fusion F (pre-F) protein is the strongest predictor of protection against infection (AUC = 0.72), outperforming serum anti-Pre-F IgG (AUC = 0.66). Serum and mucosal responses are largely compartment-independent (r = 0.02–0.28), with a combined serum and mucosal anti-Pre-F model outperforming either alone, and both biomarkers correlating with RSV-A neutralising titres. These findings have direct implications for designing RSV infection-preventing vaccine strategies.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © The Author(s) 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/ licenses/by/4.0/. |
| Keywords: | Computational models; Predictive markers; Viral infection |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
| Date Deposited: | 03 Sep 2026 08:30 |
| Last Modified: | 03 Sep 2026 08:30 |
| Status: | Published |
| Publisher: | Springer Science and Business Media LLC |
| Refereed: | Yes |
| Identification Number: | 10.1038/s41467-026-77315-4 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:245037 |

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