Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract | Zendy

Ryan J. Farr | Zendy; Christina L. Rootes | Zendy; John Stenos | Zendy; Chwan Hong Foo | Zendy; Christopher Cowled | Zendy; Cameron R. Stewart | Zendy

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Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract

Author(s) -

Ryan J. Farr,

Christina L. Rootes,

John Stenos,

Chwan Hong Foo,

Christopher Cowled,

Cameron R. Stewart

Publication year - 2022

Publication title -

plos one

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.99

H-Index - 332

ISSN - 1932-6203

DOI - 10.1371/journal.pone.0265670

Subject(s) - microrna , covid-19 , gene expression profiling , transcriptome , computational biology , biology , bioinformatics , medicine , virology , gene expression , pathology , gene , infectious disease (medical specialty) , genetics , disease , outbreak

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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