1. Altered microRNA expression in COVID-19 patients enables identification of SARS-CoV-2 infection
- Author
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Chwan Hong Foo, Christina L. Rootes, Katherine Kedzierska, Thi H. O. Nguyen, Glenn A. Marsh, Lukasz Kedzierski, Gough G. Au, Allen C. Cheng, Ryan J. Farr, Seshadri S. Vasan, Christopher Cowled, Louise C. Rowntree, Luca Hensen, and Cameron R. Stewart
- Subjects
0301 basic medicine ,RNA viruses ,Male ,Viral Diseases ,Pulmonology ,Coronaviruses ,Physiology ,Viral pathogenesis ,medicine.medical_treatment ,Gene Expression ,Disease ,medicine.disease_cause ,Biochemistry ,0302 clinical medicine ,Medical Conditions ,COVID-19 Testing ,Influenza A Virus, H1N1 Subtype ,Immune Physiology ,Gene expression ,Influenza A virus ,Medicine and Health Sciences ,030212 general & internal medicine ,Longitudinal Studies ,Biology (General) ,Pathology and laboratory medicine ,Mammals ,Innate Immune System ,virus diseases ,Eukaryota ,Medical microbiology ,Middle Aged ,Nucleic acids ,Cytokine ,Infectious Diseases ,Viruses ,Vertebrates ,Biomarker (medicine) ,Cytokines ,Female ,Supervised Machine Learning ,SARS CoV 2 ,Pathogens ,Research Article ,Adult ,SARS coronavirus ,QH301-705.5 ,Immunology ,Microbiology ,Diagnosis, Differential ,03 medical and health sciences ,Respiratory Disorders ,Orthomyxoviridae Infections ,Virology ,microRNA ,medicine ,Genetics ,Animals ,Humans ,Non-coding RNA ,Molecular Biology ,Pandemics ,Aged ,Natural antisense transcripts ,Biology and life sciences ,Host Microbial Interactions ,business.industry ,SARS-CoV-2 ,Case-control study ,Organisms ,Viral pathogens ,Ferrets ,COVID-19 ,Covid 19 ,RC581-607 ,Molecular Development ,Influenza ,Gene regulation ,Microbial pathogens ,MicroRNAs ,Disease Models, Animal ,030104 developmental biology ,Immune System ,Case-Control Studies ,Amniotes ,Respiratory Infections ,RNA ,Parasitology ,Immunologic diseases. Allergy ,business ,Zoology ,Biomarkers ,Developmental Biology - 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., Author summary While it is recognized that the host response to infection plays a critical role in determining the severity and outcome of COVID-19, the host microRNA (miRNA) response to SARS-CoV-2 infection is poorly defined. Here we have used next-generation sequencing and bioinformatics to profile circulating miRNAs in 10 COVID-19 patients that were sampled longitudinally over time. COVID-19 was associated with altered expression of 55 plasma miRNAs, with miR-776-3p and miR-1275 among the most strongly down-regulated, and miR-4742-3p, miR-31-5p and miR-3215-3p the most up-regulated. An artificial intelligence methodology was used to identify a miRNA signature, consisting of miR423-5p, miR-23a-3p, miR-195-5p, which could independently classify COVID-19 patients from healthy controls with 99.9% accuracy. When applied to the ferret model of COVID-19, the same signature classified COVID-19 cases with 99.8% accuracy and could distinguish between COVID-19 and influenza A(H1N1) infection with >95% accuracy. In summary this study profiles the host miRNA response to COVID-19 and suggests that the measurement of select host molecules may have potential to independently detect disease cases.
- Published
- 2021