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      SARS-CoV-2 (COVID-19) structural and evolutionary dynamicome: Insights into functional evolution and human genomics

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      1 , 2 , 1 , 2 , 3 , 4 , 4 , 1 , 5 , 5 , 1 , 1 , 1 , 1 , 1 , 2 , 6 , 1 , 2 , 6 , 6 , 1 , 7 , 7 , 7 , 8 , 9 , 10 , 1 , 11 , 12 , 1 , 2 , *
      The Journal of Biological Chemistry
      American Society for Biochemistry and Molecular Biology
      molecular dynamics, molecular evolution, human genetics, RNA virus, virus entry, protein structure, receptor structure-function, post-translational modification (PTM), COVID-19, severe acute respiratory coronavirus 2 (SARS-CoV-2), receptor structure-function

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          Abstract

          The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has challenged the speed at which laboratories can discover the viral composition and study health outcomes. The small ∼30-kb ssRNA genome of coronaviruses makes them adept at cross-species spread while enabling a robust understanding of all of the proteins the viral genome encodes. We have employed protein modeling, molecular dynamics simulations, evolutionary mapping, and 3D printing to gain a full proteome- and dynamicome-level understanding of SARS-CoV-2. We established the Viral Integrated Structural Evolution Dynamic Database (VIStEDD at RRID:SCR_018793) to facilitate future discoveries and educational use. Here, we highlight the use of VIStEDD for nsp6, nucleocapsid (N), and spike (S) surface glycoprotein. For both nsp6 and N, we found highly conserved surface amino acids that likely drive protein–protein interactions. In characterizing viral S protein, we developed a quantitative dynamics cross-correlation matrix to gain insights into its interactions with the angiotensin I–converting enzyme 2 (ACE2)–solute carrier family 6 member 19 (SLC6A19) dimer. Using this quantitative matrix, we elucidated 47 potential functional missense variants from genomic databases within ACE2/SLC6A19/transmembrane serine protease 2 (TMPRSS2), warranting genomic enrichment analyses in SARS-CoV-2 patients. These variants had ultralow frequency but existed in males hemizygous for ACE2. Two ACE2 noncoding variants (rs4646118 and rs143185769) present in ∼9% of individuals of African descent may regulate ACE2 expression and may be associated with increased susceptibility of African Americans to SARS-CoV-2. We propose that this SARS-CoV-2 database may aid research into the ongoing pandemic.

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          Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

          Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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            SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

            Summary The recent emergence of the novel, pathogenic SARS-coronavirus 2 (SARS-CoV-2) in China and its rapid national and international spread pose a global health emergency. Cell entry of coronaviruses depends on binding of the viral spike (S) proteins to cellular receptors and on S protein priming by host cell proteases. Unravelling which cellular factors are used by SARS-CoV-2 for entry might provide insights into viral transmission and reveal therapeutic targets. Here, we demonstrate that SARS-CoV-2 uses the SARS-CoV receptor ACE2 for entry and the serine protease TMPRSS2 for S protein priming. A TMPRSS2 inhibitor approved for clinical use blocked entry and might constitute a treatment option. Finally, we show that the sera from convalescent SARS patients cross-neutralized SARS-2-S-driven entry. Our results reveal important commonalities between SARS-CoV-2 and SARS-CoV infection and identify a potential target for antiviral intervention.
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              COVID-19: consider cytokine storm syndromes and immunosuppression

              As of March 12, 2020, coronavirus disease 2019 (COVID-19) has been confirmed in 125 048 people worldwide, carrying a mortality of approximately 3·7%, 1 compared with a mortality rate of less than 1% from influenza. There is an urgent need for effective treatment. Current focus has been on the development of novel therapeutics, including antivirals and vaccines. Accumulating evidence suggests that a subgroup of patients with severe COVID-19 might have a cytokine storm syndrome. We recommend identification and treatment of hyperinflammation using existing, approved therapies with proven safety profiles to address the immediate need to reduce the rising mortality. Current management of COVID-19 is supportive, and respiratory failure from acute respiratory distress syndrome (ARDS) is the leading cause of mortality. 2 Secondary haemophagocytic lymphohistiocytosis (sHLH) is an under-recognised, hyperinflammatory syndrome characterised by a fulminant and fatal hypercytokinaemia with multiorgan failure. In adults, sHLH is most commonly triggered by viral infections 3 and occurs in 3·7–4·3% of sepsis cases. 4 Cardinal features of sHLH include unremitting fever, cytopenias, and hyperferritinaemia; pulmonary involvement (including ARDS) occurs in approximately 50% of patients. 5 A cytokine profile resembling sHLH is associated with COVID-19 disease severity, characterised by increased interleukin (IL)-2, IL-7, granulocyte-colony stimulating factor, interferon-γ inducible protein 10, monocyte chemoattractant protein 1, macrophage inflammatory protein 1-α, and tumour necrosis factor-α. 6 Predictors of fatality from a recent retrospective, multicentre study of 150 confirmed COVID-19 cases in Wuhan, China, included elevated ferritin (mean 1297·6 ng/ml in non-survivors vs 614·0 ng/ml in survivors; p 39·4°C 49 Organomegaly None 0 Hepatomegaly or splenomegaly 23 Hepatomegaly and splenomegaly 38 Number of cytopenias * One lineage 0 Two lineages 24 Three lineages 34 Triglycerides (mmol/L) 4·0 mmol/L 64 Fibrinogen (g/L) >2·5 g/L 0 ≤2·5 g/L 30 Ferritin ng/ml 6000 ng/ml 50 Serum aspartate aminotransferase <30 IU/L 0 ≥30 IU/L 19 Haemophagocytosis on bone marrow aspirate No 0 Yes 35 Known immunosuppression † No 0 Yes 18 The Hscore 11 generates a probability for the presence of secondary HLH. HScores greater than 169 are 93% sensitive and 86% specific for HLH. Note that bone marrow haemophagocytosis is not mandatory for a diagnosis of HLH. HScores can be calculated using an online HScore calculator. 11 HLH=haemophagocytic lymphohistiocytosis. * Defined as either haemoglobin concentration of 9·2 g/dL or less (≤5·71 mmol/L), a white blood cell count of 5000 white blood cells per mm3 or less, or platelet count of 110 000 platelets per mm3 or less, or all of these criteria combined. † HIV positive or receiving longterm immunosuppressive therapy (ie, glucocorticoids, cyclosporine, azathioprine).
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                Author and article information

                Journal
                J Biol Chem
                J. Biol. Chem
                jbc
                jbc
                JBC
                The Journal of Biological Chemistry
                American Society for Biochemistry and Molecular Biology (11200 Rockville Pike, Suite 302, Rockville, MD 20852-3110, U.S.A. )
                0021-9258
                1083-351X
                14 August 2020
                25 June 2020
                25 June 2020
                : 295
                : 33
                : 11742-11753
                Affiliations
                [1 ]Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
                [2 ]Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan, USA
                [3 ]Calvin University, Grand Rapids, Michigan, USA
                [4 ]Department of Mathematics, University of North Alabama, Florence, Alabama, USA
                [5 ]Grand Rapids Community College, Grand Rapids, Michigan, USA
                [6 ]Walsh University, North Canton, Ohio, USA
                [7 ]HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
                [8 ]Department of Critical Care Medicine and Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
                [9 ]Spectrum Health Medical Genetics, Grand Rapids, Michigan, USA
                [10 ]Department of Physiology, Michigan State University, East Lansing, Michigan, USA
                [11 ]Pediatric Intensive Care Unit, Helen DeVos Children's Hospital, Grand Rapids, Michigan, USA
                [12 ]Office of Research, Spectrum Health, Grand Rapids, Michigan, USA
                Author notes
                [* ]For correspondence: Jeremy W Prokop, jprokop54@ 123456gmail.com .

                Edited by Craig E. Cameron

                Author information
                https://orcid.org/0000-0002-1642-329X
                https://orcid.org/0000-0001-8823-3690
                https://orcid.org/0000-0002-1818-9695
                Article
                RA120.014873
                10.1074/jbc.RA120.014873
                7450099
                32587094
                4d0d3b02-1fa8-462e-8de2-8b4085d90806
                © 2020 Gupta et al.

                Published under exclusive license by The American Society for Biochemistry and Molecular Biology, Inc. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                : 22 June 2020
                : 23 June 2020
                Funding
                Funded by: HHS | NIH | National Institute of General Medical Sciences (NIGMS), open-funder-registry 10.13039/100000057;
                Award ID: R01-GM108618
                Award Recipient :
                Funded by: HHS | NIH | National Institute of Environmental Health Sciences (NIEHS), open-funder-registry 10.13039/100000066;
                Award ID: K01-ES025435
                Award Recipient :
                Categories
                Genomics and Proteomics

                Biochemistry
                molecular dynamics,molecular evolution,human genetics,rna virus,virus entry,protein structure,receptor structure-function,post-translational modification (ptm),covid-19,severe acute respiratory coronavirus 2 (sars-cov-2)

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