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Gastrointestinal microbiota: A predictor of COVID-19 severity?

ABSTRACT: Coronavirus disease 2019 (COVID-19), caused by a severe acute respiratory syndrome coronavirus 2 infection, has raised serious concerns worldwide over the past 3 years. The severity and clinical course of COVID-19 depends on many factors (e.g., associated comorbidities, age, etc) and may have various clinical and imaging findings, which raises management concerns. Gut microbiota composition is known to influence respiratory disease, and respiratory viral infection can also influence gut microbiota. Gut and lung microbiota and their relationship (gut-lung axis) can act as modulators of inflammation. Modulating the intestinal microbiota, by improving its composition and diversity through nutraceutical agents, can have a positive impact in the prophylaxis/treatment of COVID-19.

SUBMITTER: Neag MA

PROVIDER: S-EPMC9753053 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Gastrointestinal microbiota: A predictor of COVID-19 severity?

Neag Maria Adriana MA Vulturar Damiana-Maria DM Gherman Diana D Burlacu Codrin-Constantin CC Todea Doina Adina DA Buzoianu Anca Dana AD

World journal of gastroenterology 20221201 45

Coronavirus disease 2019 (COVID-19), caused by a severe acute respiratory syndrome coronavirus 2 infection, has raised serious concerns worldwide over the past 3 years. The severity and clinical course of COVID-19 depends on many factors (<i>e.g.,</i> associated comorbidities, age, <i>etc</i>) and may have various clinical and imaging findings, which raises management concerns. Gut microbiota composition is known to influence respiratory disease, and respiratory viral infection can also influenc ...[more]

PMID: 36533107

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

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Gastrointestinal microbiota: A predictor of COVID-19 severity?

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Gastrointestinal microbiota: A predictor of COVID-19 severity?

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