Abstract
Objective: Since the COVID-19 pandemic began in early 2020, SARS-CoV2 has claimed more than six million lives world-wide, with over 510 million cases to date. To reduce healthcare burden, we must investigate how to prevent non-acute disease from progressing to severe infection requiring hospitalization.
Methods: To achieve this goal, we investigated metabolic signatures of both non-acute (out-patient) and severe (requiring hospitalization) COVID-19 samples by profiling the associated plasma metabolomes of 84 COVID-19 positive University of Virginia hospital patients. We utilized supervised and unsupervised machine learning and metabolic modeling approaches to identify key metabolic drivers that are predictive of COVID-19 disease severity. Using metabolic pathway enrichment analysis, we explored potential metabolic mechanisms that link these markers to disease progression.
Results: Enriched metabolites associated with tryptophan in non-acute COVID-19 samples suggest mitigated innate immune system inflammatory response and immunopathology related lung damage prevention. Increased prevalence of histidine- and ketone-related metabolism in severe COVID-19 samples offers potential mechanistic insight to musculoskeletal degeneration-induced muscular weakness and host metabolism that has been hijacked by SARS-CoV2 infection to increase viral replication and invasion.
Conclusions: Our findings highlight the metabolic transition from an innate immune response coupled with inflammatory pathway inhibition in non-acute infection to rampant inflammation and associated metabolic systemic dysfunction in severe COVID-19.
Keywords: COVID-19; Genome-scale metabolic modeling; Machine learning; Metabolomics.
【저자키워드】 COVID-19, metabolomics, machine learning, Genome-scale metabolic modeling, 【초록키워드】 Inflammation, metabolomics, Severe infection, SARS-CoV2, innate immune response, severe COVID-19, Hospitalization, COVID-19 pandemic, machine learning, hospital, Genome, Infection, immunopathology, SARS-CoV2 infection, metabolism, COVID-19 disease, innate immune system, Prevalence, Disease progression, viral replication, healthcare, pathway, Tryptophan, Virginia, disease, patients, mechanism, metabolite, COVID-19 samples, marker, Inflammatory response, Inflammatory, Pathway enrichment analysis, Invasion, lung damage, histidine, Predictive, acute disease, dysfunction, Musculoskeletal, University of Virginia, metabolic pathway, plasma metabolome, COVID-19 disease severity, ketone, weakness, machine, positive, offer, Host, approach, Prevent, highlight, identify, investigated, reduce, Increased, claimed, COVID-19 sample, driver, muscular, 【제목키워드】 disease severity, alteration, identify, functional, with COVID-19,