Seasonal influenza A virus lineages exhibit divergent abilities to antagonize interferon induction and signaling.
Joel Rivera-CardonaNeeha KakuturuElizabeth F RowlandQi Wen TeoElizabeth A ThayerTimothy J C TanJiayi SunCollin KiefferNicholas C WuChristopher B BrookePublished in: bioRxiv : the preprint server for biology (2024)
The circulation of seasonal influenza A viruses (IAVs) in humans relies on effective evasion and subversion of the host immune response. While the evolution of seasonal H1N1 and H3N2 viruses to avoid humoral immunity is well characterized, relatively little is known about the evolution of innate immune antagonism phenotypes in these viruses. Numerous studies have established that only a small subset of infected cells is responsible for initiating the type I and type III interferon (IFN) response during IAV infection, emphasizing the importance of single cell studies to accurately characterize the IFN response during infection. We developed a flow cytometry-based method to examine transcriptional changes in IFN and interferon stimulated gene (ISG) expression at the single cell level. We observed that NS segments derived from seasonal H3N2 viruses are more efficient at antagonizing IFN signaling but less effective at suppressing IFN induction, compared to the pdm2009 H1N1 lineage. We compared a collection of NS segments spanning the natural history of the current seasonal IAV lineages and demonstrate long periods of stability in IFN antagonism potential, punctuated by occasional phenotypic shifts. Altogether, our data reveal significant differences in how seasonal and pandemic H1N1 and H3N2 viruses antagonize the human IFN response at the single cell level.
Keyphrases
- immune response
- dendritic cells
- single cell
- rna seq
- flow cytometry
- high throughput
- poor prognosis
- type iii
- toll like receptor
- innate immune
- gene expression
- coronavirus disease
- genome wide
- cell proliferation
- oxidative stress
- sars cov
- dengue virus
- cell death
- long non coding rna
- artificial intelligence
- zika virus
- endoplasmic reticulum stress
- dna methylation
- human health
- deep learning
- genome wide identification