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      Evolutionary Dynamics and Dissemination Pattern of the SARS-CoV-2 Lineage B.1.1.33 During the Early Pandemic Phase in Brazil

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      1 , * , 2 , 3 , 4 , 1 , 1 , 1 , 1 , 1 , 1 , 5 , 5 , 6 , 6 , 6 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 13 , 14 , 15 , 16 , 17 , 18 , * , 1
      Frontiers in Microbiology
      Frontiers Media S.A.
      coronavirus disease 2019, severe acute respiratory syndrome coronavirus-2, coronavirus, Brazil, genetic lineages, community transmission

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          Abstract

          A previous study demonstrates that most of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) Brazilian strains fell in three local clades that were introduced from Europe around late February 2020. Here we investigated in more detail the origin of the major and most widely disseminated SARS-CoV-2 Brazilian lineage B.1.1.33. We recovered 190 whole viral genomes collected from 13 Brazilian states from February 29 to April 31, 2020 and combined them with other B.1.1 genomes collected globally. Our genomic survey confirms that lineage B.1.1.33 is responsible for a variable fraction of the community viral transmissions in Brazilian states, ranging from 2% of all SARS-CoV-2 genomes from Pernambuco to 80% of those from Rio de Janeiro. We detected a moderate prevalence (5–18%) of lineage B.1.1.33 in some South American countries and a very low prevalence (<1%) in North America, Europe, and Oceania. Our study reveals that lineage B.1.1.33 evolved from an ancestral clade, here designated B.1.1.33-like, that carries one of the two B.1.1.33 synapomorphic mutations. The B.1.1.33-like lineage may have been introduced from Europe or arose in Brazil in early February 2020 and a few weeks later gave origin to the lineage B.1.1.33. These SARS-CoV-2 lineages probably circulated during February 2020 and reached all Brazilian regions and multiple countries around the world by mid-March, before the implementation of air travel restrictions in Brazil. Our phylodynamic analysis also indicates that public health interventions were partially effective to control the expansion of lineage B.1.1.33 in Rio de Janeiro because its median effective reproductive number ( R e ) was drastically reduced by about 66% during March 2020, but failed to bring it to below one. Continuous genomic surveillance of lineage B.1.1.33 might provide valuable information about epidemic dynamics and the effectiveness of public health interventions in some Brazilian states.

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          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.)
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            MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

            We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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                Author and article information

                Contributors
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                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                17 February 2021
                2020
                17 February 2021
                : 11
                : 615280
                Affiliations
                [1] 1Laboratory of Respiratory Viruses and Measles, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), SARS-CoV-2 National Reference Laboratory for the Brazilian Ministry of Health (MoH) and Regional Reference Laboratory in Americas for the Pan-American Health Organization (PAHO/WHO) , Rio de Janeiro, Brazil
                [2] 2Departamento de Biologia, Centro de Ciencias Exatas, Naturais e da Saude, Universidade Federal do Espirito Santo , Alegre, Brazil
                [3] 3Instituto Gonçalo Moniz, Fundação Oswaldo Cruz , Salvador, Brazil
                [4] 4Unidad de Genomica y Bioinformatica, Centro Universitario Regional del Litoral Norte, Universidad de la Republica , Salto, Uruguay
                [5] 5Instituto Aggeu Magalhaes, Fundação Oswaldo Cruz , Recife, Brazil
                [6] 6Instituto Evandro Chagas , Belem, Para
                [7] 7Laboratorio Central de Saude Publica do Estado de Santa Catarina (LACEN-SC) , Florianopolis, Brazil
                [8] 8Laboratorio Central de Saude Publica do Estado Espirito Santo (LACEN-ES) , Vitoria, Brazil
                [9] 9Laboratorio Central de Saude Publica do Distrito Federal (LACEN-DF) , Brazilia, Brazil
                [10] 10Laboratorio Central de Saude Publica de Alagoas (LACEN-AL) , Maceio, Brazil
                [11] 11Laboratorio Central de Saude Publica da Bahia (LACEN-BA) , Salvador, Brazil
                [12] 12Laboratorio Central de Saude Publica de Sergipe (LACEN-SE) , Aracaju, Brazil
                [13] 13Laboratorio Central de Saude Publica de Parana (LACEN-PR) , Curitiba, Brazil
                [14] 14Fiocruz Mato Grosso do Sul , Campo Grande, Brazil
                [15] 15Universidade Federal de Mato Grosso do Sul – UFMS , Campo Grande, Brazil
                [16] 16Hospital das Forças Armadas, Ministério da Defesa , Brasília, Brazil
                [17] 17Coordenadoria Geral de Laboratorios – Ministério da Saude , Brazilia, Brazil
                [18] 18Laboratorio de AIDS e Imunologia Molecular, Instituto Oswaldo Cruz, Fiocruz , Rio de Janeiro, Brazil
                Author notes

                Edited by: Josanne Hinke Verhagen, Erasmus Medical Center, Netherlands

                Reviewed by: Luiz Carlos Junior Alcantara, Oswaldo Cruz Foundation (Fiocruz), Brazil; Junki Maruyama, University of Texas Medical Branch at Galveston, United States

                *Correspondence: Paola Cristina Resende, paola@ 123456ioc.fiocruz.br

                These authors share first authorship

                These authors have contributed equally to this work

                This article was submitted to Virology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2020.615280
                7925893
                33679622
                c3097c53-fb05-40a6-b257-30bc10fc4025
                Copyright © 2021 Resende, Delatorre, Gräf, Mir, Motta, Appolinario, Paixão, Mendonça, Ogrzewalska, Caetano, Wallau, Docena, Santos, de Almeida Ferreira, Sousa Junior, Silva, Fernandes, Vianna, Souza, Ferro, Nardy, Santos, Riediger, do Carmo Debur, Croda, Oliveira, Abreu, Bello and Siqueira.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 October 2020
                : 21 December 2020
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 59, Pages: 14, Words: 0
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                coronavirus disease 2019,severe acute respiratory syndrome coronavirus-2,coronavirus,brazil,genetic lineages,community transmission

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