Abstract Background Early metabolic reorganization was only recently recognized as essentially integrated part of immunology. In this context, unbalanced ROS/RNS levels that connected to increased aerobic fermentation, which linked to alpha-tubulin-based cell restructuration and control of cell cycle progression, was identified as ma jor c omplex t rait for e arly d e novo programming (‘CoV-MAC-TED’) during SARS-CoV-2 infection. This trait was highlighted as 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 data collection of early SARS-CoV-2 infection responses was not 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. Our results (a) validate ‘Cov-MAC-TED’ as crucial trait for early SARS-CoV-2 reprogramming for both 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 by help of Artificial Intelligence methodologies for designing anti-SARS-CoV-2 strategies.

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 for designing therapeutic strategies