Mucosal IgA to pre-fusion F protein predicts protection from RSV infection in a high burden setting

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.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2026-08-26
DOI
https://doi.org/10.1038/s41467-026-77315-4
Primary Topic
Respiratory viral infections research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mucosal IgA to pre-fusion F protein predicts protection from RSV infection in a high burden setting

Benjamin B. Lindsey, Irina Grouneva, Madikoi Danso, Stefan Flasche et al.
Nature Communications
Respiratory viral infections research
article

Mucosal IgA to pre-fusion F protein predicts protection from RSV infection in a high burden setting

Benjamin B. Lindsey, Irina Grouneva, Madikoi Danso, Stefan Flasche, Thushan I. de Silva, Mary Wu, Giulia Dowgier, Ya Jankey Jagne, Beate Kampmann, Laura Gatcombe, Marie Gomes, Natalie Barratt, David Hodgson, Tom Coleman, Sheikh Jarju, Rhys D. Wenlock, Ashley Otter, Sasha Francis, Adam Kucharski
article en

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.

Nature CommunicationsVol. 17(1)
Wiltshire Council (GB), The Francis Crick Institute (GB), London School of Hygiene & Tropical Medicine (GB), MRC Unit the Gambia (GM), Charité - Universitätsmedizin Berlin (DE), University of Sheffield (GB)
Wellcome Trust, Francis Crick Institute, Cancer Research UK, Einstein Stiftung Berlin, Medical Research Council
No poverty
Openalex Percentile: Top 78%
Respiratory viral infections research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.