Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets
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Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada
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Department of Animal Science, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada
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Author to whom correspondence should be addressed.
Metabolites 2021, 11(1), 44; https://doi.org/10.3390/metabo11010044
Received: 16 November 2020
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Revised: 30 December 2020
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Accepted: 4 January 2021
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Published: 9 January 2021
(This article belongs to the Special Issue Systematic Reviews and Meta-Analyses (SR-MA) of Metabolites and Disease Risk)
The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.
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Keywords:
COVID-19; metabolomics; mass spectrometry; meta-analysis; coronavirus
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style
Pang, Z.; Zhou, G.; Chong, J.; Xia, J. Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets. Metabolites 2021, 11, 44. https://doi.org/10.3390/metabo11010044
AMA Style
Pang Z, Zhou G, Chong J, Xia J. Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets. Metabolites. 2021; 11(1):44. https://doi.org/10.3390/metabo11010044
Chicago/Turabian StylePang, Zhiqiang, Guangyan Zhou, Jasmine Chong, and Jianguo Xia. 2021. "Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets" Metabolites 11, no. 1: 44. https://doi.org/10.3390/metabo11010044
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