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      SARS-CoV-2 genomic characterization and clinical manifestation of the COVID-19 outbreak in Uruguay

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      1 , * , 2 , 3 , 2 , 4 , 4 , 5 , 1 , 6 , 1 , 1 , 7 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 8 , 9 , 4 , 5 , * , 5 , *
      medRxiv
      Cold Spring Harbor Laboratory
      SARS-CoV-2, COVID-19 outbreak, Uruguay, South America, full genome amplicon sequencing, spike D614G genetic mutation, phylogeographic BEAST analysis, clinical correlations

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          Abstract

          COVID-19 is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared by the World Health Organization a global public health emergency. Among the severe outbreaks across South America, Uruguay has become known for curtailing SARS-CoV-2 exceptionally well. To understand the SARS-CoV-2 introductions, local transmissions, and associations with genomic and clinical parameters in Uruguay, we sequenced the viral genomes of 44 outpatients and inpatients in a private healthcare system in its capital, Montevideo, from March to May 2020. We performed a phylogeographic analysis using sequences from our cohort and other studies that indicate a minimum of 23 independent introductions into Uruguay, resulting in five major transmission clusters. Our data suggest that most introductions resulting in chains of transmission originate from other South American countries, with the earliest seeding of the virus in late February 2020, weeks before the borders were closed to all non-citizens and a partial lockdown implemented. Genetic analyses suggest a dominance of S and G clades (G, GH, GR) that make up >90% of the viral strains in our study. In our cohort, lethal outcome of SARS-CoV-2 infection significantly correlated with arterial hypertension, kidney failure, and ICU admission (FDR < 0.01), but not with any mutation in a structural or non-structural protein, such as the spike D614G mutation. Our study contributes genetic, phylodynamic, and clinical correlation data about the exceptionally well-curbed SARS-CoV-2 outbreak in Uruguay, which furthers the understanding of disease patterns and regional aspects of the pandemic in Latin America.

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          Most cited references45

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          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%.
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            A new coronavirus associated with human respiratory disease in China

            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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              Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

              Abstract Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                11 October 2020
                : 2020.10.08.20208546
                Affiliations
                [1 ]Laboratorio de Biología Molecular, Asociación Española Primera en Salud, Montevideo, Uruguay
                [2 ]South African Medical Research Council Capacity Development Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
                [3 ]Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
                [4 ]Genome Technology Center, Office for Science and Research, NYU Langone Health, New York, New York, United States of America
                [5 ]Department of Pathology, NYU Grossman School of Medicine, New York, New York, United States of America
                [6 ]Departamento de Desarrollo Biotecnológico, Instituto de Higiene, Facultad de Medicina, Udelar, Montevideo, Uruguay
                [7 ]Departamento de Biodiversidad y Genética. Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay
                [8 ]Spatial Epidemiology Lab. (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
                [9 ]Department of Microbiology, Immunology and Transplantation, Rega Institute, Leuven, Belgium
                Author notes

                Contributors

                VE1, AH, and RD conceived research goals, experiments, and analyses. GWH, BM, SS, VP, and RD performed formal analyses. VE1, RB, and AH acquired funding for the project. PZ, CM, VP, NM, CB, SI, MF, VP, VE2, GM, AS, FR, MN, LP, SB, and MNZ performed experiments and/or were involved in sample acquisition. BM, MM, GWH, SD, AH, and RD developed methodologies, designed, and/or implemented computer codes. VE1, GWH, MM, SD, RB, AH, and RD supervised the research activity and validated the research output. GWH, BM, SD, VP, and RD prepared figures and tables. VE1, GWH, AH, and RD wrote the manuscript. All authors reviewed and edited the manuscript.

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                Shared first authors

                [* ]Corresponding authors: Victoria Elizondo, Ph.D., totiep@ 123456gmail.com , Adriana Heguy, Ph.D., Adriana.Heguy@ 123456nyulangone.org , Ralf Duerr, MD, Ph.D., Ralf.Duerr@ 123456nyulangone.org
                Article
                10.1101/2020.10.08.20208546
                7553156
                33052352
                68d1bd1e-ab4f-46e4-ad3d-98a4584c0bcf

                It is made available under a CC-BY-NC-ND 4.0 International license.

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                sars-cov-2,covid-19 outbreak,uruguay,south america,full genome amplicon sequencing,spike d614g genetic mutation,phylogeographic beast analysis,clinical correlations

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