Multiple spillovers from humans and onward transmission of SARS-CoV-2 in white-tailed deer

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Significance

The results provide strong evidence of extensive SARS-CoV-2 infection of white-tailed deer, a free-living wild animal species with widespread distribution across North, Central, and South America. The analysis shows infection of deer resulted from multiple spillovers from humans, followed by efficient deer-to-deer transmission. The discovery of widespread infection of white-tailed deer indicates their establishment as potential reservoir hosts for SARS-CoV-2, a finding with important implications for the ecology, long-term persistence, and evolution of the virus, including the potential for spillback to humans.

Abstract

Many animal species are susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and could act as reservoirs; however, transmission in free-living animals has not been documented. White-tailed deer, the predominant cervid in North America, are susceptible to SARS-CoV-2 infection, and experimentally infected fawns can transmit the virus. To test the hypothesis that SARS-CoV-2 is circulating in deer, 283 retropharyngeal lymph node (RPLN) samples collected from 151 free-living and 132 captive deer in Iowa from April 2020 through January of 2021 were assayed for the presence of SARS-CoV-2 RNA. Ninety-four of the 283 (33.2%) deer samples were positive for SARS-CoV-2 RNA as assessed by RT-PCR. Notably, following the November 2020 peak of human cases in Iowa, and coinciding with the onset of winter and the peak deer hunting season, SARS-CoV-2 RNA was detected in 80 of 97 (82.5%) RPLN samples collected over a 7-wk period. Whole genome sequencing of all 94 positive RPLN samples identified 12 SARS-CoV-2 lineages, with B.1.2 ( n = 51; 54.5%) and B.1.311 ( n = 19; 20%) accounting for ∼75% of all samples. The geographic distribution and nesting of clusters of deer and human lineages strongly suggest multiple human-to-deer transmission events followed by subsequent deer-to-deer spread. These discoveries have important implications for the long-term persistence of the SARS-CoV-2 pandemic. Our findings highlight an urgent need for a robust and proactive “One Health” approach to obtain enhanced understanding of the ecology, molecular evolution, and dissemination of SARS-CoV-2.

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The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

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A new coronavirus associated with human respiratory disease in China

Fan Wu, Su Zhao, Bin Yu … (2020)

Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health 1–3 . Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing 4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China 5 . This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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Author and article information

Journal

Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc Natl Acad Sci U S A

Journal ID (hwp): pnas

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Electronic): 25 January 2022

Publication date (Print): 8 February 2022

Publication date PMC-release: 25 January 2022

Volume: 119

Issue: 6

Electronic Location Identifier: e2121644119

Affiliations

[1] ^aAnimal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University , University Park, PA 16802;

[2] ^bHuck Institutes of Life Sciences, The Pennsylvania State University , University Park, PA 16802;

[3] ^cWildlife Bureau, Iowa Department of Natural Resources , Des Moines, IA 50319;

[4] ^dDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University , Ames, IA 50011;

[5] ^eThe Center for Infectious Disease Dynamics, Department of Biology and Huck Institutes of the Life Sciences, The Pennsylvania State University , University Park, PA 16802;

[6] ^fDepartment of Chemical Engineering, The Pennsylvania State University , University Park, PA 16802;

[7] ^gDepartment of Animal Science, The Pennsylvania State University , University Park, PA 16802;

[8] ^hDisease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge , Cambridge CB3 0ES, United Kingdom;

[9] ⁱLaboratory of Molecular and Translational Human Infectious Disease Research, Center for Infectious Diseases, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, Houston Methodist Hospital , Houston, TX 77030;

[10] ^jDepartment of Pathology and Laboratory Medicine, Weill Cornell Medical College , New York, NY 10021;

[11] ^kDepartment of Microbiology and Immunology, Weill Cornell Medical College , New York, NY 10021;

[12] ^lUniversity of Chicago Consortium for Advanced Science and Engineering, University of Chicago and Division of Data Science and Learning, Argonne National Laboratory , Lemont, IL 60439

Author notes

¹To whom correspondence may be addressed. Email: skuchipudi@ 123456psu.edu or vkapur@ 123456psu.edu .

Edited by Peter Palese, Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY; received November 30, 2021; accepted December 16, 2021

Author contributions: S.V.K., R.M.R., J.M.M., and V.K. designed research; S.V.K., M.S.-N., R.M.R., M.Y., R.H.N., L.L., N.L., R.J.O., and V.K. performed research; S.V.K., M.S.-N., K.J.V., R.K.N., B.M.J., C.D.M., N.L., K.W., A.J.K.C., R.J.O., J.J.D., J.M.M., P.J.H., and V.K. analyzed data; and S.V.K., M.S.-N., R.M.R., K.J.V., R.K.N., L.L., B.M.J., C.D.M., N.L., K.W., A.J.K.C., R.J.O., J.J.D., J.M.M., P.J.H., and V.K. wrote the paper.