Major Complex Trait for Early De Novo Programming ‘CoV-MAC-TED’ Detected in Human Nasal Epithelial Cells Infected by Two SARS-CoV-2 Variants Is Promising to Help in Designing Therapeutic Strategies

Background: Early metabolic reorganization was only recently recognized as an essentially integrated part of immunology. In this context, unbalanced ROS/RNS levels connected to increased aerobic fermentation, which is linked to alpha-tubulin-based cell restructuring and control of cell cycle progression, were identified as a major complex trait for early de novo programming (‘CoV-MAC-TED’) during SARS-CoV-2 infection. This trait was highlighted as a critical target for developing early anti-viral/anti-SARS-CoV-2 strategies. To obtain this result, analyses had been performed on transcriptome data from diverse experimental cell systems. A call was released for wide data collection of the defined set of genes for transcriptome analyses, named ‘ReprogVirus’, which should be based on strictly standardized protocols and data entry from diverse virus types and variants into the ‘ReprogVirus Platform’. This platform is currently under development. However, so far, an in vitro cell system from primary target cells for virus attacks that could ideally serve for standardizing the data collection of early SARS-CoV-2 infection responses has not been defined. Results: Here, we demonstrate transcriptome-level profiles of the most critical ‘ReprogVirus’ gene sets for identifying ‘CoV-MAC-TED’ in cultured human nasal epithelial cells infected by two SARS-CoV-2 variants differing in disease severity. Our results (a) validate ‘Cov-MAC-TED’ as a crucial trait for early SARS-CoV-2 reprogramming for the tested virus variants and (b) demonstrate its relevance in cultured human nasal epithelial cells. Conclusion: In vitro-cultured human nasal epithelial cells proved to be appropriate for standardized transcriptome data collection in the ‘ReprogVirus Platform’. Thus, this cell system is highly promising to advance integrative data analyses with the help of artificial intelligence methodologies for designing anti-SARS-CoV-2 strategies.