Altered microRNA expression in COVID-19 patients accurately identifies SARS-CoV-2 infection
About the session
By examining the host response to SARS-CoV-2 infection, we gain valuable insights into viral pathogenesis and COVID-19 progression. MicroRNAs (miRNAs), a class of small (18-22nt), non-coding RNAs, often play central roles in the host-pathogen interface and have been recognized as promising biomarkers of infectious disease. In this study, we profiled the circulating miRNAs from 10 longitudinally sampled COVID-19 patients and their age and gender-matched controls. We found 55 differentially expressed miRNAs in early-stage disease, including miRNAs with known pro- and anti-inflammatory roles. Machine learning also identified a three-miRNA signature of COVID-19 that predicted infection with 99.9% accuracy. This signature faded away as the patients recovered. When this three-miRNA signature was applied to ferrets (a common model of respiratory infections, including COVID-19), the signature predicted SARS-CoV-2 infection with 99.8% accuracy and could distinguish between SARS-CoV-2, influenza (H1N1), and uninfected controls with >95% accuracy.
This study demonstrates that SARS-CoV-2 infection results in a significant host miRNA response that aligns with our current knowledge of COVID-19-induced inflammation. Using a multivariate machine learning approach, we developed a robust miRNA biomarker signature of COVID-19. This signature could complement existing diagnostic tests by providing a new approach to detecting cases that might otherwise be missed.
Ryan Farr is a molecular biologist, virologist and bioinformatician who, as a Research Scientist with the Commonwealth Scientific and Industrial Research Organization (CSIRO), conducts molecular disease-related investigations as part of their Host Response Team, Health and Biosecurity (H&B) at the Australian Centre for Disease Preparedness (ACDP). His studies focus on characterization of host molecular responses to disease using next-generation sequencing (NGS) and advanced machine learning algorithms. This approach identifies biomarkers that aid in health monitoring and disease management. Most recently, Ryan has investigated the use of biomarkers in several diseases, including diabetes, rabies, SARS-CoV-2 and bovine mastitis, to enable clinicians and veterinarians to implement early, effective treatment. To that end, heworks closely with industry professionals, including biosensing and nanofabrication experts, to understand how these biomarkers can be used in clinical practice.
Ryan completed his Ph.D. in Medicine at the NHMRC Clinical Trials Centre, University of Sydney, Australia, in 2017. Following his degree, he completed a three-year Postdoctoral Research Fellowship at the CSIRO Australian Centre for Disease Preparedness (ACDP), a high-containment virology laboratory based in Geelong, Australia. In March 2020, Ryan joined the CSIRO as Research Scientist, where he continues today. Ryan thoroughly enjoys scientific communication and outreach and participates in these activities whenever possible.