Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add filters

Database
Language
Document Type
Year range
1.
PLoS One ; 17(4): e0265670, 2022.
Article in English | MEDLINE | ID: covidwho-1775445

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.


Subject(s)
COVID-19 , MicroRNAs , Biomarkers , COVID-19/diagnosis , Gene Expression Profiling , Humans , MicroRNAs/genetics , MicroRNAs/metabolism , Respiratory System/metabolism , SARS-CoV-2/genetics
2.
PLoS Pathog ; 17(7): e1009759, 2021 07.
Article in English | MEDLINE | ID: covidwho-1329138

ABSTRACT

The host response to SARS-CoV-2 infection provide insights into both viral pathogenesis and patient management. The host-encoded microRNA (miRNA) response to SARS-CoV-2 infection, however, remains poorly defined. Here we profiled circulating miRNAs from ten COVID-19 patients sampled longitudinally and ten age and gender matched healthy donors. We observed 55 miRNAs that were altered in COVID-19 patients during early-stage disease, with the inflammatory miR-31-5p the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-423-5p, miR-23a-3p and miR-195-5p) independently classified COVID-19 cases with an accuracy of 99.9%. In a ferret COVID-19 model, the three-miRNA signature again detected SARS-CoV-2 infection with 99.7% accuracy, and distinguished SARS-CoV-2 infection from influenza A (H1N1) infection and healthy controls with 95% accuracy. Distinct miRNA profiles were also observed in COVID-19 patients requiring oxygenation. This study demonstrates that SARS-CoV-2 infection induces a robust host miRNA response that could improve COVID-19 detection and patient management.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/genetics , MicroRNAs/genetics , SARS-CoV-2 , Adult , Aged , Animals , COVID-19/blood , Case-Control Studies , Diagnosis, Differential , Disease Models, Animal , Female , Ferrets , Gene Expression , Host Microbial Interactions/genetics , Humans , Influenza A Virus, H1N1 Subtype , Longitudinal Studies , Male , MicroRNAs/blood , Middle Aged , Orthomyxoviridae Infections/diagnosis , Orthomyxoviridae Infections/genetics , Pandemics , Supervised Machine Learning
SELECTION OF CITATIONS
SEARCH DETAIL