bioRxivMicrobiologyPreprint
Resolving in vitro heterogeneity of host functional responses to HCoV-229E via single-cell analyses
Anna Hendrika Cornelia VlotiD, Vrinda VenuiD, Samantha AdikariiD, Eric M. SmalliD, Cullen RothiD, Kyle A. SullivaniD, Shawn R. StarkenburgiD, Daniel JacobsoniD, Christina R. SteadmaniD
Abstract
Understanding host-pathogen interactions at the molecular level is essential to elucidating the mechanisms that govern immunity and pathogenesis. While diverse responses to viral infection have been documented, it remains unclear if this variability arises from the complex host milieu or represents an intrinsic property of the infection itself. To investigate this, we performed single-cell (sc) RNA sequencing analysis of infected MRC-5 fibroblast cells with human coronavirus 229E (HCoV-229E), which revealed pronounced variation in viral load across time points and biological replicates. While viral sequences were higher at 24 hours post-infection (hpi), more host genes were differentially expressed at 48 hpi, indicating dynamic responses during infection progression. Cells with higher viral load upregulated expression of unfolded protein response genes and NF-{kappa}B regulators, and downregulated expression of fibroblast identity and cell cycle genes, reflecting a shift toward a stress-adaptive, antiviral state. Network analyses identified NF-{kappa}B signaling as a central regulatory axis, with negative regulators overexpressed in high-viral-load clusters. Additionally, ATAC-seq analysis showed that regions of increased chromatin accessibility were enriched for NF-{kappa}B subunits, further supporting its role in transcriptional control. These findings demonstrate that even within ostensibly uniform cell cultures, viral infection induces distinct heterogeneous transcriptional and regulatory responses.
- organism: human
- assay: scRNA-seq
- assay: ATAC-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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