Abstract
Background Transcriptome studies of a selected gene set (ReprogVirus) had identified unbalanced ROS/RNS levels, which connected to increased aerobic fermentation that linked to alpha-tubulin-based cell restructuration and cell cycle control, as a major complex trait for early de novo programming (CoV-MAC-TED) upon SARS-CoV-2 infection. Recently, CoV-MAC-TED was confirmed as promising marker by using primary target human nasal epithelial cells (NECs) infected by two SARS-CoV-2 variants with different effects on disease severity. To further explore this marker/cell system as a standardized tool for identifying anti-viral targets in general, testing of further virus types is required. Results: Transcriptome level profiles of H3N2 influenza-infected NECs indicated ROS/RNS level changes and increased transcript accumulation of genes related to glycolysis, lactic fermentation and α-tubulin at 8 hours post infection. These early changes linked to energy-dependent, IRF9-marked rapid immunization. However, ReprogVirus-marker genes indicated the absence of initial cell cycle progress, which contrasted our findings during infections with two SARS-CoV-2 variants, where cell cycle progress was linked to delayed IRF9 response. Our results point to the possibility of CoV-MAC-TED-assisted, rapid individual host cell response identification upon virus infections. Conclusion: The complex trait CoV-MAC-TED can identify similar and differential early responses of SARS-CoV-2 and influenza H3N2 viruses. This indicates its appropriateness to search for anti-viral targets in view of therapeutic design strategies. For standardization, human NECs can be used. This marker/cell system is promising to identify differential early cell responses upon viral infections also depending on cell origins.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This version has been revised during the review process provided by the journal Vaccines. Also, we integrated an additional author, Carlos Noceda, in order to improve statistical analysis of our data.