Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract
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Rootes, Christina L
Stenos, John
Foo, Chwan Hong
Cowled, Christopher
Stewart, Cameron R
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Purohit, Purvi
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Abstract
Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.
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PLoS One
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17
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4
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© 2022 Farr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Clinical sciences
Medical virology
Respiratory diseases
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
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Farr, RJ; Rootes, CL; Stenos, J; Foo, CH; Cowled, C; Stewart, CR, Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract, PLoS One, 2022, 17 (4), pp. e0265670