Abstract
Public respiratory-virus transcriptomes are valuable for studying host responses, but differences in tissue source, control definition, and study design can confound pooled analyses. We developed a compartment-aware computational workflow to determine whether reproducible host-response activity can be identified while preserving nasal and blood biological context. The paired GSE117827 paediatric cohort served as the anchor dataset, comprising nasal-swab and whole-blood transcriptomes from symptomatic picornavirus infection, symptomatic respiratory syncytial virus infection, asymptomatic picornavirus detection, and virus-negative controls. After HUGO Gene Nomenclature Committee (HGNC) filtering, 27,685 genes were analyzed. Separate 50-gene protein-coding nasal and blood modules were defined from the top positive responses and locked before external evaluation. Gene-level nasal and blood effects were nearly independent (Pearson r = 0.015), and the modules shared six exploratory rank-overlap genes (Jaccard index = 0.064). Nevertheless, the nasal module separated infection from controls in independent upper-airway cohorts, with areas under the receiver operating characteristic curve (AUROCs) of 0.749, 0.693, and 0.609, whereas the blood module achieved AUROCs of 0.832, 0.924, and 0.870 in external blood cohorts. In longitudinal natural-infection data, matched scores decreased from acute illness to discharge, with paired deltas of 0.436 for nasal samples and 0.330 for blood. Three additional benchmark datasets comprising 666 external samples, together with random-gene nulls, module-size sweeps, bootstrap stability, marker-program correlations, and variance partitioning, defined the robustness and limitations of the workflow. The Pandya 33-messenger ribonucleic acid (mRNA) set remained stronger for viral-versus-bacterial discrimination, while the blood module also increased in bacterial pneumonia. These findings support compartment-specific modules as reusable host-response activity scores for cohort comparison and recovery tracking, rather than universal pan-tissue biomarkers or stand-alone pathogen classifiers.