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      SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues

      research-article
      1 , 2 , 3 , 4 , 5 , 6 , 50 , 2 , 4 , 5 , 7 , 50 , 2 , 4 , 5 , 8 , 9 , 50 , 10 , 11 , 50 , 1 , 2 , 4 , 5 , 1 , 2 , 4 , 5 , 12 , 4 , 4 , 4 , 13 , 4 , 13 , 14 , 5 , 15 , 2 , 3 , 4 , 5 , 7 , 2 , 4 , 5 , 7 , 1 , 4 , 5 , 16 , 2 , 4 , 5 , 7 , 17 , 18 , 2 , 4 , 5 , 2 , 3 , 4 , 5 , 7 , 5 , 18 , 2 , 3 , 4 , 5 , 7 , 12 , 12 , 19 , 20 , 21 , 22 , 23 , 21 , 22 , 22 , 24 , 25 , 26 , 27 , 27 , 18 , 28 , 29 , 29 , 29 , 29 , 29 , 30 , 31 , 17 , 32 , 33 , 34 , 35 , 17 , 36 , 31 , 36 , 37 , 38 , 37 , 38 , 39 , 38 , 40 , 41 , 42 , 18 , 28 , 43 , 44 , 45 , 23 , 21 , 22 , 20 , 20 , 10 , 11 , 46 , 34 , 35 , 37 , 38 , 4 , 5 , 47 , 9 , 48 , 12 , 17 , 32 , 33 , 12 , 4 , 13 , 4 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 16 , 33 , 49 , 51 , 52 , , 5 , 16 , 18 , 49 , 51 , 52 , 53 , ∗∗ , HCA Lung Biological Network ∗∗∗
      Cell
      The Authors. Published by Elsevier Inc.
      scRNA-seq, interferon, ISG, ACE2, SARS-CoV-2, COVID-19, influenza, non-human primate, human, mouse
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          Summary

          There is pressing urgency to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2), which causes the disease COVID-19. SARS-CoV-2 spike (S) protein binds angiotensin-converting enzyme 2 (ACE2), and in concert with host proteases, principally transmembrane serine protease 2 (TMPRSS2), promotes cellular entry. The cell subsets targeted by SARS-CoV-2 in host tissues and the factors that regulate ACE2 expression remain unknown. Here, we leverage human, non-human primate, and mouse single-cell RNA-sequencing (scRNA-seq) datasets across health and disease to uncover putative targets of SARS-CoV-2 among tissue-resident cell subsets. We identify ACE2 and TMPRSS2 co-expressing cells within lung type II pneumocytes, ileal absorptive enterocytes, and nasal goblet secretory cells. Strikingly, we discovered that ACE2 is a human interferon-stimulated gene (ISG) in vitro using airway epithelial cells and extend our findings to in vivo viral infections. Our data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection.

          Graphical Abstract

          Highlights

          • Meta-analysis of human, non-human primate, and mouse single-cell RNA-seq datasets for putative SARS-CoV-2 targets

          • Type II pneumocytes, nasal secretory cells, and absorptive enterocytes are ACE2 + TMPRSS2 +

          • Interferon and influenza increase ACE2 in human nasal epithelia and lung tissue

          • Mouse Ace2 is not upregulated by interferon, raising implications for disease modeling

          Abstract

          Analysis of single-cell RNA-seq datasets from human, non-human primate, and mouse barrier tissues identifies putative cellular targets of SARS-CoV-2 on the basis of ACE2 and TMPRSS2 expression. ACE2 represents a previously unappreciated interferon-stimulated gene in human, but not mouse, epithelial tissues, identifying anti-viral induction of a host tissue-protective mechanism, but also a potential means for viral exploitation of the host response.

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          Most cited references159

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          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.
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            Clinical Characteristics of Coronavirus Disease 2019 in China

            Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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              Is Open Access

              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                The Authors. Published by Elsevier Inc.
                0092-8674
                1097-4172
                27 April 2020
                27 April 2020
                Affiliations
                [1 ]Program in Health Sciences & Technology, Harvard Medical School & Massachusetts Institute of Technology, Boston, MA 02115, USA
                [2 ]Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [3 ]Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [4 ]Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA
                [5 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [6 ]Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA
                [7 ]Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [8 ]Program in Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [9 ]Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [10 ]Africa Health Research Institute, Durban, South Africa
                [11 ]School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
                [12 ]University of Massachusetts Medical School, Worcester, MA 01655, USA
                [13 ]Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
                [14 ]Program in Virology, Harvard Medical School, Boston, MA 02115, USA
                [15 ]John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA 02138, USA
                [16 ]Program in Immunology, Harvard Medical School, Boston, MA 02115, USA
                [17 ]Division of Pediatric Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115, USA
                [18 ]Division of Gastroenterology, Hepatology, and Nutrition, Boston Children’s Hospital, Boston, MA 02115, USA
                [19 ]Aix-Marseille University, INSERM, INRA, C2VN, Marseille, France
                [20 ]Université Côte d’Azur, CNRS, IPMC, Sophia-Antipolis, France
                [21 ]Comprehensive Pneumology Center & Institute of Lung Biology and Disease, Helmholtz Zentrum München, Munich, Germany
                [22 ]German Center for Lung Research, Munich, Germany
                [23 ]Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
                [24 ]Research Unit Lung Repair and Regeneration, Helmholtz Zentrum München, Munich, Germany
                [25 ]Division of Neonatology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
                [26 ]Divison of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
                [27 ]Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
                [28 ]Division of Gastroenterology, Brigham and Women’s Hospital, Boston, MA 02115, USA
                [29 ]Division of Allergy and Clinical Immunology, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
                [30 ]University of California, Berkeley, CA 94720, USA
                [31 ]University of Washington, Seattle, WA 98195, USA
                [32 ]Dana Farber Cancer Institute, Boston, MA 02115, USA
                [33 ]Harvard Medical School, Boston, MA 02115, USA
                [34 ]Stem Cell & Gene Therapy Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
                [35 ]Department of Medicine, University of Washington, Seattle, WA 98195, USA
                [36 ]Division of Gastroenterology and Hepatology, Seattle Children’s Hospital, Seattle, WA 98145, USA
                [37 ]Department of Microbiology & Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15219, USA
                [38 ]Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
                [39 ]Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
                [40 ]UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
                [41 ]Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15224, USA
                [42 ]Department of Infection, Immunity & Cardiovascular Disease, The Medical School and The Florey Institute for Host Pathogen Interactions, University of Sheffield, Sheffield, S10 2TN, UK
                [43 ]Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
                [44 ]Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN 37240, USA
                [45 ]Department of Veterans Affairs Medical Center, Nashville, TN 37212, USA
                [46 ]Department of Infection & Immunity, University College London, London, UK
                [47 ]Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
                [48 ]Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [49 ]Harvard Stem Cell Institute, Cambridge, MA 02138, USA
                Author notes
                []Corresponding author shalek@ 123456mit.edu
                [∗∗∗ ]Corresponding author
                [50]

                These authors contributed equally

                [51]

                These authors contributed equally

                [52]

                Senior author

                [53]

                Lead Contact

                Article
                S0092-8674(20)30500-6
                10.1016/j.cell.2020.04.035
                7252096
                32413319
                df7b7593-3376-46e1-aa95-16235a1c93ec
                © 2020 The Authors

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 13 March 2020
                : 3 April 2020
                : 20 April 2020
                Categories
                Article

                Cell biology
                scrna-seq,interferon,isg,ace2,sars-cov-2,covid-19,influenza,non-human primate,human,mouse
                Cell biology
                scrna-seq, interferon, isg, ace2, sars-cov-2, covid-19, influenza, non-human primate, human, mouse

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