Back

Tissue-Specific Metabolomic Reprogramming Determines The Disease Pathophysiology Of SARS-COV-2 Variants In Hamster Model

Sardarni, U. K.; Ambikan, A.; Acharya, A.; Johnson, S. D.; Avedissian, S. N.; Vegvari, A.; Neogi, U.; Byrareddy, S.

2024-02-27 systems biology
10.1101/2024.02.25.581989 bioRxiv
Show abstract

Despite significant effort, a clear understanding of host tissue-specific responses and their implications for immunopathogenicity against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant infection has remained poorly defined. To shed light on the interaction between organs and specific SARS-CoV-2 variants, we sought to characterize the complex relationship among acute multisystem manifestations, dysbiosis of the gut microbiota, and the resulting implications for SARS-CoV-2 variant-specific immunopathogenesis in the Golden Syrian Hamster (GSH) model using multi-omics approaches. Our investigation revealed increased viremia in diverse tissues of delta-infected GSH compared to the omicron variant. Multi-omics analyses uncovered distinctive metabolic responses between the delta and omicron variants, with the former demonstrating dysregulation in synaptic transmission proteins associated with neurocognitive disorders. Additionally, delta-infected GSH exhibited an altered fecal microbiota composition, marked by increased inflammation-associated taxa and reduced commensal bacteria compared to the omicron variant. These findings underscore the SARS-CoV-2-mediated tissue insult, characterized by modified host metabolites, neurological protein dysregulation, and gut dysbiosis, highlighting the compromised gut-lung-brain axis during acute infection. TeaserIn hamsters at acute infection, SARS-CoV-2 variant-specific metabolic responses and gut dysbiosis dysregulate synaptic transmission proteins.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.