Rawlinson, D. P. | Search bioRxiv

bioRxiv2026

Viral replication and host response in airway epithelial cells predicts coronavirus pathogenicity and identifies HCoV-229E as an atypical seasonal coronavirus

Human coronaviruses (HCoVs) range from mild seasonal strains to pathogenic viruses that cause severe pneumonia. The virological and host determinants that distinguish pathogenic coronaviruses (MERS-CoV, SARS-CoV-2) from seasonal strains (HCoV-229E, HCoV-NL63 and HCoV-OC43) remain poorly defined. To identify determinants of pathogenicity, we systematically compared replication, cellular tropism, innate responses and cytopathogenicity of pathogenic compared with seasonal HCoVs across nasal, bronchial, small airway and embryonic stem cell derived alveolar type II (AT2) epithelial cell cultures. All HCoVs preferentially infected ciliated cells in the upper airway, but only pathogenic viruses replicated to high titres in AT2 cells. Pathogenic HCoVs reprogrammed AT2 function and suppressed key innate immune signatures, consistent with distal lung epithelial dysfunction. In contrast, seasonal HCoVs were associated with limited alveolar replication and milder disruption of AT2 homeostasis. Strikingly, despite modest replication in all cell types, HCoV-229E induced pronounced epithelial injury, cilia loss and strong interferon stimulated gene expression, as well as induction of cellular stress/death programmes resembling those induced by MERS-CoV. These findings highlight the striking differences in cellular responses throughout the airways and between distinct HCoV strains and emphasizes the importance of assessing distal lung tropism and epithelial host responses to evaluate emergent coronaviruses to inform pandemic preparedness strategies.

microbiology↗

bioRxiv2024

A flexible framework for minimal biomarker signature discovery from clinical omics studies without library size normalisation

Application of transcriptomics, proteomics and metabolomics technologies to clinical cohorts has uncovered a variety of signatures for predicting disease. Many of these signatures require the full omics data for evaluation on unseen samples, either explicitly or implicitly through library size normalisation. Translation to low-cost point-of-care tests requires development of signatures which measure as few analytes as possible without relying on direct measurement of library size. To achieve this, we have developed a feature selection method (Forward Selection-Partial Least Squares) which generates minimal disease signatures from high-dimensional omics datasets with applicability to continuous, binary or multi-class outcomes. Through extensive benchmarking, we show that FS-PLS has comparable performance to commonly used signature discovery methods while delivering signatures which are an order of magnitude smaller. We show that FS-PLS can be used to select features predictive of library size, and that these features can be used to normalize unseen samples, meaning that the features in the complete model can be measured in isolation for making new predictions. By enabling discovery of small, high-performance signatures, FS-PLS addresses an important impediment for the further development of precision medical care.

bioinformatics↗