Journal
GASTROENTEROLOGY
Volume 164, Issue 2, Pages 272-288Publisher
W B SAUNDERS CO-ELSEVIER INC
DOI: 10.1053/j.gastro.2022.09.024
Keywords
Gut Microbiome; Fecal Metabolome; Cytokine Storm; Gut-Lung Axis
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This study investigates the interrelationships among gut microbes, metabolites, and cytokines in COVID-19 and its complications, and validates the results with follow-up data. The results show multiple correlations between COVID-19-related microbes, gut metabolites, and inflammatory cytokine dynamics, especially in severe disease and pneumonia. The study also confirms the concordance of altered metabolites in COVID-19 with their corresponding microbial functional genes.
BACKGROUND & AIMS: We investigate interrelationships be-tween gut microbes, metabolites, and cytokines that charac-terize COVID-19 and its complications, and we validate the results with follow-up, the Japanese 4D (Disease, Drug, Diet, Daily Life) microbiome cohort, and non-Japanese data sets. METHODS: We performed shotgun metagenomic sequencing and metabolomics on stools and cytokine measurements on plasma from 112 hospitalized patients with SARS-CoV-2 infection and 112 non-COVID-19 control individuals matched by important confounders. RESULTS: Multiple correlations were found between COVID-19-related microbes (eg, oral mi-crobes and short-chain fatty acid producers) and gut metabo-lites (eg, branched-chain and aromatic amino acids, short-chain fatty acids, carbohydrates, neurotransmitters, and vitamin B6). Both were also linked to inflammatory cytokine dynamics (eg, interferon g, interferon l3, interleukin 6, CXCL-9, and CXCL-10). Such interrelationships were detected highly in severe disease and pneumonia; moderately in the high D-dimer level, kidney dysfunction, and liver dysfunction groups; but rarely in the diarrhea group. We confirmed concordances of altered metab-olites (eg, branched-chain amino acids, spermidine, putrescine, and vitamin B6) in COVID-19 with their corresponding micro-bial functional genes. Results in microbial and metabolomic alterations with severe disease from the cross-sectional data set were partly concordant with those from the follow-up data set. Microbial signatures for COVID-19 were distinct from diabetes, inflammatory bowel disease, and proton-pump inhibitors but overlapping for rheumatoid arthritis. Random forest classifier models using microbiomes can highly predict COVID-19 and severe disease. The microbial signatures for COVID-19 showed moderate concordance between Hong Kong and Japan. CONCLUSIONS: Multiomics analysis revealed multiple gut microbe-metabolite-cytokine interrelationships in COVID-19 and COVID-19related complications but few in gastrointes-tinal complications, suggesting microbiota-mediated immune responses distinct between the organ sites. Our results un-derscore the existence of a gut-lung axis in COVID-19.
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