Longitudinal host transcriptional responses to SARS-CoV-2 infection in adults with extremely high viral load.
Vasanthi AvadhanulaChad J CreightonLaura Ferlic-StarkRichard SucgangYiqun ZhangDivya NagarajErin G NicholsonAnubama RajanVipin Kumar MenonHarshavardhan DoddapaneniDonna Marie MuznyGinger MetcalfSara Joan Javornik CregeenKristi Louise HoffmanRichard A GibbsJoseph PetrosinoPedro A PiedraPublished in: bioRxiv : the preprint server for biology (2023)
Current understanding of viral dynamics of SARS-CoV-2 and host responses driving the pathogenic mechanisms in COVID-19 is rapidly evolving. Here, we conducted a longitudinal study to investigate gene expression patterns during acute SARS-CoV-2 illness. Cases included SARS-CoV-2 infected individuals with extremely high viral loads early in their illness, individuals having low SARS-CoV-2 viral loads early in their infection, and individuals testing negative for SARS-CoV-2. We could identify widespread transcriptional host responses to SARS-CoV-2 infection that were initially most strongly manifested in patients with extremely high initial viral loads, then attenuating within the patient over time as viral loads decreased. Genes correlated with SARS-CoV-2 viral load over time were similarly differentially expressed across independent datasets of SARS-CoV-2 infected lung and upper airway cells, from both in vitro systems and patient samples. We also generated expression data on the human nose organoid model during SARS-CoV-2 infection. The human nose organoid-generated host transcriptional response captured many aspects of responses observed in the above patient samples, while suggesting the existence of distinct host responses to SARS-CoV-2 depending on the cellular context, involving both epithelial and cellular immune responses. Our findings provide a catalog of SARS-CoV-2 host response genes changing over time.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- gene expression
- endothelial cells
- immune response
- case report
- dna methylation
- transcription factor
- genome wide
- big data
- intensive care unit
- deep learning
- artificial intelligence
- oxidative stress
- single cell
- hepatitis b virus
- acute respiratory distress syndrome
- data analysis
- mechanical ventilation