Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract.
Ryan J FarrChristina L RootesJohn StenosChwan Hong FooChristopher CowledCameron R StewartPublished in: PloS one (2022)
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.
Keyphrases
- coronavirus disease
- sars cov
- respiratory syndrome coronavirus
- machine learning
- minimally invasive
- respiratory tract
- cell proliferation
- gene expression
- loop mediated isothermal amplification
- long non coding rna
- real time pcr
- poor prognosis
- label free
- transcription factor
- artificial intelligence
- deep learning
- genome wide
- binding protein
- data analysis