Affiliations 

  • 1 Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina, USA; Duke Global Health Institute, Duke University, Durham, North Carolina, USA; NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
  • 2 Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina, USA; Duke Global Health Institute, Duke University, Durham, North Carolina, USA
  • 3 Programme in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore, Singapore
  • 4 Julia Jones Matthews Department of Public Health, Texas Tech University Health Sciences Center, Abilene, TX, USA
  • 5 Virology Department, National Institute of Veterinary Research, Hanoi, Vietnam
  • 6 Clinical Research Center, Sibu Hospital, Ministry of Health Malaysiaand Faculty of Medicine, SEGi University, Kota Damansara, Malaysia
  • 7 Expert Consultant to Duke University, Atlanta, GA, USA
  • 8 Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina, USA; Duke Global Health Institute, Duke University, Durham, North Carolina, USA; Programme in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore, Singapore; Global Health Research Center, Duke-Kunshan University, Kunshan, China. Electronic address: gregory.gray@duke.edu
J Clin Virol, 2020 07;128:104391.
PMID: 32403008 DOI: 10.1016/j.jcv.2020.104391

Abstract

BACKGROUND: During the past two decades, three novel coronaviruses (CoVs) have emerged to cause international human epidemics with severe morbidity. CoVs have also emerged to cause severe epidemics in animals. A better understanding of the natural hosts and genetic diversity of CoVs are needed to help mitigate these threats.

OBJECTIVE: To design and evaluate a molecular diagnostic tool for detection and identification of all currently recognized and potentially future emergent CoVs from the Orthocoronavirinae subfamily.

STUDY DESIGN AND RESULTS: We designed a semi-nested, reverse transcription RT-PCR assay based upon 38 published genome sequences of human and animal CoVs. We evaluated this assay with 14 human and animal CoVs and 11 other non-CoV respiratory viruses. Through sequencing the assay's target amplicon, the assay correctly identified each of the CoVs; no cross-reactivity with 11 common respiratory viruses was observed. The limits of detection ranged from 4 to 4 × 102 copies/reaction, depending on the CoV species tested. To assess the assay's clinical performance, we tested a large panel of previously studied specimens: 192 human respiratory specimens from pneumonia patients, 5 clinical specimens from COVID-19 patients, 81 poultry oral secretion specimens, 109 pig slurry specimens, and 31 aerosol samples from a live bird market. The amplicons of all RT-PCR-positive samples were confirmed by Sanger sequencing. Our assay performed well with all tested specimens across all sample types.

CONCLUSIONS: This assay can be used for detection and identification of all previously recognized CoVs, including SARS-CoV-2, and potentially any emergent CoVs in the Orthocoronavirinae subfamily.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.