Accounting for seasonal variation of nasal gene expression improves transcriptome analysis of asthma.

INTRODUCTION

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. In this study we aimed to investigate the effect of season on identification of asthma-associated genes.

METHODS

We analyzed RNA-seq data from nasal brushes of asthma patients (n=361) and healthy controls (n=57) included in the ATLANTIS study. Samples were compared across two time periods defined by a data-driven approach: winter-spring (Nov 15 -May 14) and summer-autumn (May 15 - Nov 14), correcting for age, sex, smoking, and asthma status. Gene Set Variation Analysis (GSVA) scores of the obtained seasonal gene signatures were compared using Mann-Whitney U test in the independent PIAMA cohort (n=325) for replication.

RESULTS

We identified 1,731 genes differentially expressed between the two time periods (813 higher and 918 lower in winter-spring compared with summer-autumn; FDR < 0.05). This signature was replicated in the PIAMA 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 = 0.005) and a higher proportion of basal cells (p = 0.04) compared to those 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 (FDR < 0.05). We conclude that season of sampling strongly affects the nasal transcriptome, and we demonstrate that season-adjusted analyses significantly enhance sensitivity for detecting disease-associated changes.