Blog
About

6
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2

      Coronaviridae Study Group of the International Committee on Taxonomy of Viruses

      John.Ziebuhr@viro.med.uni-giessen.de
      1

      Nature Microbiology

      Nature Publishing Group UK

      Microbiology, Biodiversity, Diseases, Virology, Applied microbiology

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The present outbreak of a coronavirus-associated acute respiratory disease called coronavirus disease 19 (COVID-19) is the third documented spillover of an animal coronavirus to humans in only two decades that has resulted in a major epidemic. The Coronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses, which is responsible for developing the classification of viruses and taxon nomenclature of the family Coronaviridae, has assessed the placement of the human pathogen, tentatively named 2019-nCoV, within the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus, and designates it as SARS-CoV-2. In order to facilitate communication, the CSG proposes to use the following naming convention for individual isolates: SARS-CoV-2/host/location/isolate/date. While the full spectrum of clinical manifestations associated with SARS-CoV-2 infections in humans remains to be determined, the independent zoonotic transmission of SARS-CoV and SARS-CoV-2 highlights the need for studying viruses at the species level to complement research focused on individual pathogenic viruses of immediate significance. This will improve our understanding of virus–host interactions in an ever-changing environment and enhance our preparedness for future outbreaks.

          Related collections

          Most cited references 51

          • Record: found
          • Abstract: found
          • Article: not found

          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A Novel Coronavirus from Patients with Pneumonia in China, 2019

            Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

              Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
                Bookmark

                Author and article information

                Contributors
                A.E.Gorbalenya@lumc.nl
                Journal
                Nat Microbiol
                Nat Microbiol
                Nature Microbiology
                Nature Publishing Group UK (London )
                2058-5276
                2 March 2020
                2 March 2020
                : 1-9
                Affiliations
                [1 ]ISNI 0000000089452978, GRID grid.10419.3d, Department of Biomedical Data Sciences, , Leiden University Medical Center, ; Leiden, the Netherlands
                [2 ]ISNI 0000000089452978, GRID grid.10419.3d, Department of Medical Microbiology, , Leiden University Medical Center, ; Leiden, the Netherlands
                [3 ]ISNI 0000 0001 2342 9668, GRID grid.14476.30, Faculty of Bioengineering and Bioinformatics and Belozersky Institute of Physico-Chemical Biology, , Lomonosov Moscow State University, ; Moscow, Russia
                [4 ]ISNI 0000 0001 1089 6558, GRID grid.164971.c, Department of Microbiology and Immunology, , Loyola University of Chicago, Stritch School of Medicine, ; Maywood, IL USA
                [5 ]ISNI 0000 0001 1034 1720, GRID grid.410711.2, Department of Epidemiology, , University of North Carolina, ; Chapel Hill, NC USA
                [6 ]ISNI 0000000120346234, GRID grid.5477.1, Division of Virology, Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, , Utrecht University, ; Utrecht, the Netherlands
                [7 ]ISNI 0000 0001 2218 4662, GRID grid.6363.0, Institute of Virology, , Charité – Universitätsmedizin Berlin, ; Berlin, Germany
                [8 ]ISNI 000000040459992X, GRID grid.5645.2, Viroscience Lab, Erasmus MC, ; Rotterdam, the Netherlands
                [9 ]GRID grid.264762.3, Texas A&M University-Texarkana, ; Texarkana, TX USA
                [10 ]ISNI 0000 0004 1936 8294, GRID grid.214572.7, Department of Microbiology and Immunology, , University of Iowa, ; Iowa City, IA USA
                [11 ]ISNI 0000000121742757, GRID grid.194645.b, Centre of Influenza Research & School of Public Health, , The University of Hong Kong, ; Hong Kong, People’s Republic of China
                [12 ]ISNI 0000000119578126, GRID grid.5515.4, Department of Molecular and Cell Biology, , National Center of Biotechnology (CNB-CSIC), Campus de Cantoblanco, ; Madrid, Spain
                [13 ]ISNI 0000 0001 2165 8627, GRID grid.8664.c, Institute of Medical Virology, , Justus Liebig University Giessen, ; Giessen, Germany
                Article
                695
                10.1038/s41564-020-0695-z
                7095448
                © The Author(s), under exclusive licence to Springer Nature Limited 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Categories
                Consensus Statement

                Comments

                Comment on this article