Leveraging metabolic modeling to identify functional metabolic alterations associated with COVID-19 disease severity

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.

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

“…In an early study, Danlos et al (2021) investigated plasma samples from a well-stratified cohort of COVID-19 patients to identify up to 77 metabolites (amino acids, polyamines, sugars and their derivatives among others) that differ between critical and mild patients. These results were further confirmed and extended by subsequent studies in plasma as well ( Albóniga et al, 2022 ; Dillard et al, 2022 ), using other matrices ( Buyukozkan et al, 2022 ; Dewulf et al, 2022 ) or as a part of a multi-omic study ( Su et al, 2020 ; Wu et al, 2021 ). All these contributions highlight the critical role that kynerurate pathway has in the metabotype associated to the disease severity ( D’Amora et al, 2021 ; Marín-Corral et al, 2021 ; Roberts et al, 2022 ).…”

Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

“…Dillard et al combined different machine learning approaches with genome-scale metabolic modelling using COVID-19 patient plasma metabolomes (the samples were collected within the proposed study) [ 104 ]. The Recon3D model [ 21 ] was first adapted to match the measured metabolites with the corresponding metabolites in the model.…”

Molecular and cellular mechanisms involved in tissue-specific metabolic modulation by SARS-CoV-2

“…A study conducted Pasini et al (2021) , in elderly of both sexes affected by long-COVID-19, with a mean age of 72 years, showed an increase in ferritin and D-dimer concentration, as well as a reduction in hemoglobin and albumin levels. Dillard et al (2022) conducted a study capable of showing differences in tryptophan metabolism in samples of patients affected by COVID-19 in a non-acute manner, which may be associated with a decreased inflammation. In the same study, patients with the severe COVID-19 presented increase in the histidine and ketone metabolism, which can be linked to musculoskeletal damage.…”