Accounting for seasonal variation of nasal gene expression improves transcriptome analysis of asthma
Year: 2026
Type: article
Abstract: BACKGROUND: Environmental seasonal changes, including allergen exposure, pollutants, and viral prevalence, impact gene expression across human tissues. Therefore, season of sample collection is critical to consider when analyzing disease-associated transcriptional programs. OBJECTIVE: We sought to investigate the effect of season on identification of asthma-associated genes. METHODS: We analyzed RNA sequencing data from nasal brushes of patients with asthma (n = 361) and healthy control participants (n = 57) included in the ATLANTIS study. Samples were compared across 2 time periods defined by a data-driven approach: winter-spring (November 15 through May 14) and summer-autumn (May 15 through November 14), correcting for age, sex, smoking, and asthma status. Gene set variation analysis scores of the obtained seasonal gene signatures were compared using Mann-Whitney U test in the independent Prevention and Incidence of Asthma and Mite Allergy cohort (n = 325) for replication. RESULTS: We identified 1731 genes differentially expressed between the 2 time periods (813 higher expression and 918 lower expression in winter-spring compared with summer-autumn; false discovery rate < .05). This signature was replicated in the Prevention and Incidence of Asthma and Mite Allergy cohort nasal gene expression data and was independent of asthma or allergy status. Samples collected in winter-spring showed a significantly lower predicted proportion of ciliated cells (P = .005) and a higher proportion of basal cells (P = .04) compared with samples from summer-autumn. Crucially, including season as a covariate in models for asthma-associated gene expression nearly doubled the number of differentially expressed genes, from 126 to 271 (false discovery rate < .05). CONCLUSIONS: Season of sampling strongly affects the nasal transcriptome. We demonstrate that season-adjusted analyses significantly enhance sensitivity for detecting disease-associated changes.
Authors: Tatiana Karp, Alen Faiz, Huib A.M. Kerstjens, Martijn C. Nawijn, Tessa E. Gillett, Yidan Sun, Elin T.G. Kersten, Judith M. Vonk, Hui Wen, Monica Kraft, Bianca Beghé, Klaus F. Rabe, Alessio Piraino, Alberto Papi, Chris E. Brightling, Dave Singh, Janwillem W.H. Kocks, Salman Siddiqui, Gerard H. Koppelman, Maarten van den Berge, Victor Guryev
Institutions: University Medical Center Groningen, University of Groningen, University of Technology Sydney, University of Arizona, University of Modena and Reggio Emilia, Christian-Albrechts-Universität zu Kiel, German Center for Lung Research, LungenClinic Grosshansdorf, Chiesi (Italy), University of Ferrara, University of Leicester, NIHR Leicester Biomedical Research Centre, University of Manchester, IQVIA (United Kingdom), Manchester University NHS Foundation Trust, Medicines Evaluation Unit, General Practitioners Research Institute, Observational & Pragmatic Research Institute, Imperial College London
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,900
OpenAlex ID: W7222846866