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The trans-omics landscape of COVID-19.

ABSTRACT: The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

SUBMITTER: Wu P

PROVIDER: S-EPMC8316550 | biostudies-literature |

REPOSITORIES: biostudies-literature

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The Trans-omics Landscape of COVID-19

Project description:System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment.

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