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Harish A. Protein structures unravel the signatures and patterns of deep time evolution. QRB DISCOVERY 2024; 5:e3. [PMID: 38616890 PMCID: PMC11016368 DOI: 10.1017/qrd.2024.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/13/2023] [Accepted: 12/12/2023] [Indexed: 04/16/2024] Open
Abstract
The formulation and testing of hypotheses using 'big biology data' often lie at the interface of computational biology and structural biology. The Protein Data Bank (PDB), which was established about 50 years ago, catalogs three-dimensional (3D) shapes of organic macromolecules and showcases a structural view of biology. The comparative analysis of the structures of homologs, particularly of proteins, from different species has significantly improved the in-depth analyses of molecular and cell biological questions. In addition, computational tools that were developed to analyze the 'protein universe' are providing the means for efficient resolution of longstanding debates in cell and molecular evolution. In celebrating the golden jubilee of the PDB, much has been written about the transformative impact of PDB on a broad range of fields of scientific inquiry and how structural biology transformed the study of the fundamental processes of life. Yet, the transforming influence of PDB on one field of inquiry of fundamental interest-the reconstruction of the distant biological past-has gone almost unnoticed. Here, I discuss the recent advances to highlight how insights and tools of structural biology are bearing on the data required for the empirical resolution of vigorously debated and apparently contradicting hypotheses in evolutionary biology. Specifically, I show that evolutionary characters defined by protein structure are superior compared to conventional sequence characters for reliable, data-driven resolution of competing hypotheses about the origins of the major clades of life and evolutionary relationship among those clades. Since the better quality data unequivocally support two primary domains of life, it is imperative that the primary classification of life be revised accordingly.
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Commichaux S, Rand H, Javkar K, Molloy EK, Pettengill JB, Pightling A, Hoffmann M, Pop M, Jayeola V, Foley S, Luo Y. Assessment of plasmids for relating the 2020 Salmonella enterica serovar Newport onion outbreak to farms implicated by the outbreak investigation. BMC Genomics 2023; 24:165. [PMID: 37016310 PMCID: PMC10074901 DOI: 10.1186/s12864-023-09245-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/13/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND The Salmonella enterica serovar Newport red onion outbreak of 2020 was the largest foodborne outbreak of Salmonella in over a decade. The epidemiological investigation suggested two farms as the likely source of contamination. However, single nucleotide polymorphism (SNP) analysis of the whole genome sequencing data showed that none of the Salmonella isolates collected from the farm regions were linked to the clinical isolates-preventing the use of phylogenetics in source identification. Here, we explored an alternative method for analyzing the whole genome sequencing data driven by the hypothesis that if the outbreak strain had come from the farm regions, then the clinical isolates would disproportionately contain plasmids found in isolates from the farm regions due to horizontal transfer. RESULTS SNP analysis confirmed that the clinical isolates formed a single, nearly-clonal clade with evidence for ancestry in California going back a decade. The clinical clade had a large core genome (4,399 genes) and a large and sparsely distributed accessory genome (2,577 genes, at least 64% on plasmids). At least 20 plasmid types occurred in the clinical clade, more than were found in the literature for Salmonella Newport. A small number of plasmids, 14 from 13 clinical isolates and 17 from 8 farm isolates, were found to be highly similar (> 95% identical)-indicating they might be related by horizontal transfer. Phylogenetic analysis was unable to determine the geographic origin, isolation source, or time of transfer of the plasmids, likely due to their promiscuous and transient nature. However, our resampling analysis suggested that observing a similar number and combination of highly similar plasmids in random samples of environmental Salmonella enterica within the NCBI Pathogen Detection database was unlikely, supporting a connection between the outbreak strain and the farms implicated by the epidemiological investigation. CONCLUSION Horizontally transferred plasmids provided evidence for a connection between clinical isolates and the farms implicated as the source of the outbreak. Our case study suggests that such analyses might add a new dimension to source tracking investigations, but highlights the need for detailed and accurate metadata, more extensive environmental sampling, and a better understanding of plasmid molecular evolution.
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Affiliation(s)
- Seth Commichaux
- Center for Food Safety and Nutrition, Food and Drug Administration, Laurel, MD, USA.
- Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA.
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.
- Biological Science Graduate Program, University of Maryland, College Park, MD, USA.
| | - Hugh Rand
- Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA
| | - Kiran Javkar
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
- Joint Institute for Food Safety and Applied Nutrition, University of Maryland, College Park, MD, USA
| | - Erin K Molloy
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - James B Pettengill
- Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA
| | - Arthur Pightling
- Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA
| | - Maria Hoffmann
- Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA
| | - Mihai Pop
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Victor Jayeola
- Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA
| | - Steven Foley
- Food and Drug Administration, National Center for Toxicological Research, Jefferson, AR, USA
| | - Yan Luo
- Center for Food Safety and Nutrition, Food and Drug Administration, College Park, MD, USA
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3
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Gong X, Khan A, Wani MY, Ahmad A, Duse A. COVID-19: A state of art on immunological responses, mutations, and treatment modalities in riposte. J Infect Public Health 2023; 16:233-249. [PMID: 36603376 PMCID: PMC9798670 DOI: 10.1016/j.jiph.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/25/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
Over the last few years, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) unleashed a global public health catastrophe that had a substantial influence on human physical and mental health, the global economy, and socio-political dynamics. SARS-CoV-2 is a respiratory pathogen and the cause of ongoing COVID-19 pandemic, which testified how unprepared humans are for pandemics. Scientists and policymakers continue to face challenges in developing ideal therapeutic agents and vaccines, while at the same time deciphering the pathology and immunology of SARS-CoV-2. Challenges in the early part of the pandemic included the rapid development of diagnostic assays, vaccines, and therapeutic agents. The ongoing transmission of COVID-19 is coupled with the emergence of viral variants that differ in their transmission efficiency, virulence, and vaccine susceptibility, thus complicating the spread of the pandemic. Our understanding of how the human immune system responds to these viruses as well as the patient groups (such as the elderly and immunocompromised individuals) who are often more susceptible to serious illness have both been aided by this epidemic. COVID-19 causes different symptoms to occur at different stages of infection, making it difficult to determine distinct treatment regimens employed for the various clinical phases of the disease. Unsurprisingly, determining the efficacy of currently available medications and developing novel therapeutic strategies have been a process of trial and error. The global scientific community collaborated to research and develop vaccines at a neck-breaking speed. This review summarises the overall picture of the COVID-19 pandemic, different mutations in SARS-CoV-2, immune response, and the treatment modalities against SARS-CoV-2.
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Affiliation(s)
- Xiaolong Gong
- Department of Clinical Microbiology and Infectious Diseases, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Amber Khan
- Department of Clinical Haematology, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mohmmad Younus Wani
- Department of Chemistry, College of Science, University of Jeddah, P.O. Box 80327, Jeddah 21589, Kingdom of Saudi Arabia
| | - Aijaz Ahmad
- Department of Clinical Microbiology and Infectious Diseases, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa,Division of Infection Control, Charlotte Maxeke Johannesburg Academic Hospital, National Health Laboratory Service, Johannesburg, South Africa,Corresponding author at: Department of Clinical Microbiology and Infectious Diseases, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Adriano Duse
- Department of Clinical Microbiology and Infectious Diseases, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa,Division of Infection Control, Charlotte Maxeke Johannesburg Academic Hospital, National Health Laboratory Service, Johannesburg, South Africa
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4
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Kong S, Pons JC, Kubatko L, Wicke K. Classes of explicit phylogenetic networks and their biological and mathematical significance. J Math Biol 2022; 84:47. [PMID: 35503141 DOI: 10.1007/s00285-022-01746-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/18/2022] [Accepted: 03/31/2022] [Indexed: 11/24/2022]
Abstract
The evolutionary relationships among organisms have traditionally been represented using rooted phylogenetic trees. However, due to reticulate processes such as hybridization or lateral gene transfer, evolution cannot always be adequately represented by a phylogenetic tree, and rooted phylogenetic networks that describe such complex processes have been introduced as a generalization of rooted phylogenetic trees. In fact, estimating rooted phylogenetic networks from genomic sequence data and analyzing their structural properties is one of the most important tasks in contemporary phylogenetics. Over the last two decades, several subclasses of rooted phylogenetic networks (characterized by certain structural constraints) have been introduced in the literature, either to model specific biological phenomena or to enable tractable mathematical and computational analyses. In the present manuscript, we provide a thorough review of these network classes, as well as provide a biological interpretation of the structural constraints underlying these networks where possible. In addition, we discuss how imposing structural constraints on the network topology can be used to address the scalability and identifiability challenges faced in the estimation of phylogenetic networks from empirical data.
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Affiliation(s)
- Sungsik Kong
- Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, USA
| | - Joan Carles Pons
- Department of Mathematics and Computer Science, University of the Balearic Islands, Palma, 07122, Spain
| | - Laura Kubatko
- Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, USA.,Department of Statistics, The Ohio State University, Columbus, OH, USA
| | - Kristina Wicke
- Department of Mathematics, The Ohio State University, Columbus, OH, USA.
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5
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Tang X, Ying R, Yao X, Li G, Wu C, Tang Y, Li Z, Kuang B, Wu F, Chi C, Du X, Qin Y, Gao S, Hu S, Ma J, Liu T, Pang X, Wang J, Zhao G, Tan W, Zhang Y, Lu X, Lu J. Evolutionary analysis and lineage designation of SARS-CoV-2 genomes. Sci Bull (Beijing) 2021; 66:2297-2311. [PMID: 33585048 PMCID: PMC7864783 DOI: 10.1016/j.scib.2021.02.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 01/03/2021] [Accepted: 02/01/2021] [Indexed: 12/24/2022]
Abstract
The pandemic due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronavirus disease 2019 (COVID-19), has caused immense global disruption. With the rapid accumulation of SARS-CoV-2 genome sequences, however, thousands of genomic variants of SARS-CoV-2 are now publicly available. To improve the tracing of the viral genomes' evolution during the development of the pandemic, we analyzed single nucleotide variants (SNVs) in 121,618 high-quality SARS-CoV-2 genomes. We divided these viral genomes into two major lineages (L and S) based on variants at sites 8782 and 28144, and further divided the L lineage into two major sublineages (L1 and L2) using SNVs at sites 3037, 14408, and 23403. Subsequently, we categorized them into 130 sublineages (37 in S, 35 in L1, and 58 in L2) based on marker SNVs at 201 additional genomic sites. This lineage/sublineage designation system has a hierarchical structure and reflects the relatedness among the subclades of the major lineages. We also provide a companion website (www.covid19evolution.net) that allows users to visualize sublineage information and upload their own SARS-CoV-2 genomes for sublineage classification. Finally, we discussed the possible roles of compensatory mutations and natural selection during SARS-CoV-2's evolution. These efforts will improve our understanding of the temporal and spatial dynamics of SARS-CoV-2's genome evolution.
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Affiliation(s)
- Xiaolu Tang
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Ruochen Ying
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xinmin Yao
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Guanghao Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
- Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Changcheng Wu
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Yiyuli Tang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
| | - Zhida Li
- Yuxi Rongjian Information Technology Co., Ltd., Yuxi 653100, China
| | - Bishan Kuang
- Yuxi Rongjian Information Technology Co., Ltd., Yuxi 653100, China
| | - Feng Wu
- Yuxi Rongjian Information Technology Co., Ltd., Yuxi 653100, China
| | - Changsheng Chi
- Yuxi Rongjian Information Technology Co., Ltd., Yuxi 653100, China
| | - Xiaoman Du
- Yuxi Rongjian Information Technology Co., Ltd., Yuxi 653100, China
| | - Yi Qin
- Yuxi Rongjian Information Technology Co., Ltd., Yuxi 653100, China
| | - Shenghan Gao
- State Key Laboratory of Microbial Resources (SKLMR), The Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Songnian Hu
- State Key Laboratory of Microbial Resources (SKLMR), The Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Juncai Ma
- The Microresource and Big Data Center, The Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Tiangang Liu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071, China
| | - Xinghuo Pang
- Beijing Center for Disease Prevention and Control (CDC) & Research Center for Preventive Medicine of Beijing, Beijing 100013, China
| | - Jianwei Wang
- NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Guoping Zhao
- Key Laboratory of Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Wenjie Tan
- NHC Key Laboratory of Biosafety, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Yaping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
| | - Xuemei Lu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
| | - Jian Lu
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
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Wang Y, Zhao Y, Pan Q. Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data. Brief Bioinform 2021; 23:6380452. [PMID: 34601563 DOI: 10.1093/bib/bbab406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/04/2021] [Accepted: 09/03/2021] [Indexed: 11/15/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized.
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Affiliation(s)
- Yue Wang
- School of Mathematical and Natural Science, Arizona State University, 4701 W Thunderbird Rd, 85306, Arizona, USA
| | - Yunpeng Zhao
- School of Mathematical and Natural Science, Arizona State University, 4701 W Thunderbird Rd, 85306, Arizona, USA
| | - Qing Pan
- Department of Statistics, George Washington University, 801 22nd St. NW, 20052, Washington DC, USA
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7
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Lee S, Choi CH, Yun MR, Kim DW, Kim SS, Choi YK, Choi YS. Evaluation of global evolutionary variations in the early stage of SARS-CoV-2 pandemic. Heliyon 2021; 7:e08170. [PMID: 34660919 PMCID: PMC8511646 DOI: 10.1016/j.heliyon.2021.e08170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/16/2021] [Accepted: 10/08/2021] [Indexed: 11/13/2022] Open
Abstract
To understand the origin of variants and their evolutionary history in the early stage of the COVID-19 pandemic, time-scaled phylogenetic and gene variation analyses were performed. The mutation patterns and evolution characteristics were examined using the Bayesian Evolutionary Analysis Sampling Trees (BEAST) with 349 whole-genome sequences available by March 2020. The results revealed five phylogenetic clusters (Groups A-E), with 408 nucleotide variants. The mutations including the deletion of three nucleotides underwent various and complicated changes in the whole genome over time, while some frequency or transient mutations were also observed. Phylogenetic analysis demonstrated that SARS-CoV-2 originated from China and was transmitted to other Asian countries, followed by North America and Europe. This study could help to comprehensively understand the evolutionary characteristics of SARS-CoV-2 with a special emphasis on its global variation patterns.
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Affiliation(s)
- Sanghyun Lee
- Division of Pathogen Resource Management, Center for Public Vaccine Development and Support, National Institute of Infectious Diseases, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju-si, Republic of Korea
- Division of Bio Bigdata, Department of Precision Medicine, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju-si, Republic of Korea
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Chi-Hwan Choi
- Division of Pathogen Resource Management, Center for Public Vaccine Development and Support, National Institute of Infectious Diseases, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju-si, Republic of Korea
| | - Mi-Ran Yun
- Division of Pathogen Resource Management, Center for Public Vaccine Development and Support, National Institute of Infectious Diseases, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju-si, Republic of Korea
| | - Dae-Won Kim
- Division of Pathogen Resource Management, Center for Public Vaccine Development and Support, National Institute of Infectious Diseases, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju-si, Republic of Korea
| | - Sung Soon Kim
- Center for Public Vaccine Development and Support, National Institute of Infectious Diseases, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju-si, Republic of Korea
| | - Young Ki Choi
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Young Sill Choi
- Division of Pathogen Resource Management, Center for Public Vaccine Development and Support, National Institute of Infectious Diseases, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju-si, Republic of Korea
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Rochman ND, Wolf YI, Faure G, Mutz P, Zhang F, Koonin EV. Ongoing global and regional adaptive evolution of SARS-CoV-2. Proc Natl Acad Sci U S A 2021; 118:e2104241118. [PMID: 34292871 PMCID: PMC8307621 DOI: 10.1073/pnas.2104241118] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Understanding the trends in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution is paramount to control the COVID-19 pandemic. We analyzed more than 300,000 high-quality genome sequences of SARS-CoV-2 variants available as of January 2021. The results show that the ongoing evolution of SARS-CoV-2 during the pandemic is characterized primarily by purifying selection, but a small set of sites appear to evolve under positive selection. The receptor-binding domain of the spike protein and the region of the nucleocapsid protein associated with nuclear localization signals (NLS) are enriched with positively selected amino acid replacements. These replacements form a strongly connected network of apparent epistatic interactions and are signatures of major partitions in the SARS-CoV-2 phylogeny. Virus diversity within each geographic region has been steadily growing for the entirety of the pandemic, but analysis of the phylogenetic distances between pairs of regions reveals four distinct periods based on global partitioning of the tree and the emergence of key mutations. The initial period of rapid diversification into region-specific phylogenies that ended in February 2020 was followed by a major extinction event and global homogenization concomitant with the spread of D614G in the spike protein, ending in March 2020. The NLS-associated variants across multiple partitions rose to global prominence in March to July, during a period of stasis in terms of interregional diversity. Finally, beginning in July 2020, multiple mutations, some of which have since been demonstrated to enable antibody evasion, began to emerge associated with ongoing regional diversification, which might be indicative of speciation.
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Affiliation(s)
- Nash D Rochman
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894;
| | - Yuri I Wolf
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894
| | - Guilhem Faure
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Pascal Mutz
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894
| | - Feng Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142;
- HHMI, Massachusetts Institute of Technology, Cambridge, MA 02139
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894;
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Intercontinental transmission and local demographic expansion of SARS-CoV-2. Epidemiol Infect 2021; 149:e94. [PMID: 33845928 PMCID: PMC8060534 DOI: 10.1017/s0950268821000777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
The global outbreak of coronavirus disease 2019 (COVID-19) is greatly threatening the public health in the world. We reconstructed global transmissions and potential demographic expansions of severe acute respiratory syndrome coronavirus 2 based on genomic information. We found that intercontinental transmissions were rare in January and early February but drastically increased since late February. After world-wide implements of travel restrictions, the transmission frequencies decreased to a low level in April. We identified a total of 88 potential demographic expansions over the world based on the star-radiative networks and 75 of them were found in Europe and North America. The expansion numbers peaked in March and quickly dropped since April. These findings are highly concordant with epidemic reports and modelling results and highlight the significance of quarantine validity on the global spread of COVID-19. Our analyses indicate that the travel restrictions and social distancing measures are effective in containing the spread of COVID-19.
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10
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Buathong R, Chaifoo W, Iamsirithaworn S, Wacharapluesadee S, Joyjinda Y, Rodpan A, Ampoot W, Putcharoen O, Paitoonpong L, Suwanpimolkul G, Jantarabenjakul W, Petcharat S, Bunprakob S, Ghai S, Prasithsirikul W, Mungaomklang A, Plipat T, Hemachudha T. Multiple clades of SARS-CoV-2 were introduced to Thailand during the first quarter of 2020. Microbiol Immunol 2021; 65:405-409. [PMID: 33835528 PMCID: PMC8251142 DOI: 10.1111/1348-0421.12883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 11/30/2022]
Abstract
In early January 2020, Thailand became the first country where a coronavirus disease 2019 (COVID‐19) patient was identified outside China. In this study, 23 whole genomes of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) from patients who were hospitalized from January to March 2020 were analyzed, along with their travel histories. Six lineages were identified including A, A.6, B, B.1, B.1.8, and B.58, among which lineage A.6 was dominant. Seven patients were from China who traveled to Thailand in January and early February. Five of them were infected with the B lineage virus, and the other two cases were infected with different lineages including A and A.6. These findings present clear evidence of the early introduction of diverse SARS‐CoV‐2 clades in Thailand.
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Affiliation(s)
- Rome Buathong
- Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | - Walairat Chaifoo
- Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | | | - Supaporn Wacharapluesadee
- Thai Red Cross Emerging Infectious Diseases Health Science Centre, World Health Organization Collaborating Centre for Research and Training on Viral Zoonoses, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yutthana Joyjinda
- Thai Red Cross Emerging Infectious Diseases Health Science Centre, World Health Organization Collaborating Centre for Research and Training on Viral Zoonoses, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Apaporn Rodpan
- Thai Red Cross Emerging Infectious Diseases Health Science Centre, World Health Organization Collaborating Centre for Research and Training on Viral Zoonoses, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Program in Biotechnology, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Weenassarin Ampoot
- Thai Red Cross Emerging Infectious Diseases Health Science Centre, World Health Organization Collaborating Centre for Research and Training on Viral Zoonoses, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Opass Putcharoen
- Thai Red Cross Emerging Infectious Diseases Clinical Centre, King Chulalongkorn Memorial Hospital, Department of Medicine, Division of Infectious Diseases, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Leilani Paitoonpong
- Thai Red Cross Emerging Infectious Diseases Clinical Centre, King Chulalongkorn Memorial Hospital, Department of Medicine, Division of Infectious Diseases, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Gompol Suwanpimolkul
- Thai Red Cross Emerging Infectious Diseases Clinical Centre, King Chulalongkorn Memorial Hospital, Department of Medicine, Division of Infectious Diseases, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Watsamon Jantarabenjakul
- Thai Red Cross Emerging Infectious Diseases Clinical Centre, King Chulalongkorn Memorial Hospital, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sininat Petcharat
- Thai Red Cross Emerging Infectious Diseases Health Science Centre, World Health Organization Collaborating Centre for Research and Training on Viral Zoonoses, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Saowalak Bunprakob
- Thai Red Cross Emerging Infectious Diseases Health Science Centre, World Health Organization Collaborating Centre for Research and Training on Viral Zoonoses, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Siriporn Ghai
- Thai Red Cross Emerging Infectious Diseases Health Science Centre, World Health Organization Collaborating Centre for Research and Training on Viral Zoonoses, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Wisit Prasithsirikul
- Bamrasnaradura Infectious Disease Institute, Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | - Anek Mungaomklang
- Institute for Urban Disease Control and Prevention, Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | - Tanarak Plipat
- Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | - Thiravat Hemachudha
- Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
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11
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Mercatelli D, Holding AN, Giorgi FM. Web tools to fight pandemics: the COVID-19 experience. Brief Bioinform 2021; 22:690-700. [PMID: 33057582 PMCID: PMC7665357 DOI: 10.1093/bib/bbaa261] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/26/2020] [Accepted: 09/13/2020] [Indexed: 01/30/2023] Open
Abstract
The current outbreak of COVID-19 has generated an unprecedented scientific response worldwide, with the generation of vast amounts of publicly available epidemiological, biological and clinical data. Bioinformatics scientists have quickly produced online methods to provide non-computational users with the opportunity of analyzing such data. In this review, we report the results of this effort, by cataloguing the currently most popular web tools for COVID-19 research and analysis. Our focus was driven on tools drawing data from the fields of epidemiology, genomics, interactomics and pharmacology, in order to provide a meaningful depiction of the current state of the art of COVID-19 online resources.
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12
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Nadeau SA, Vaughan TG, Scire J, Huisman JS, Stadler T. The origin and early spread of SARS-CoV-2 in Europe. Proc Natl Acad Sci U S A 2021; 118:e2012008118. [PMID: 33571105 PMCID: PMC7936359 DOI: 10.1073/pnas.2012008118] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The investigation of migratory patterns during the SARS-CoV-2 pandemic before spring 2020 border closures in Europe is a crucial first step toward an in-depth evaluation of border closure policies. Here we analyze viral genome sequences using a phylodynamic model with geographic structure to estimate the origin and spread of SARS-CoV-2 in Europe prior to border closures. Based on SARS-CoV-2 genomes, we reconstruct a partial transmission tree of the early pandemic and coinfer the geographic location of ancestral lineages as well as the number of migration events into and between European regions. We find that the predominant lineage spreading in Europe during this time has a most recent common ancestor in Italy and was probably seeded by a transmission event in either Hubei, China or Germany. We do not find evidence for preferential migration paths from Hubei into different European regions or from each European region to the others. Sustained local transmission is first evident in Italy and then shortly thereafter in the other European regions considered. Before the first border closures in Europe, we estimate that the rate of occurrence of new cases from within-country transmission was within the bounds of the estimated rate of new cases from migration. In summary, our analysis offers a view on the early state of the epidemic in Europe and on migration patterns of the virus before border closures. This information will enable further study of the necessity and timeliness of border closures.
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Affiliation(s)
- Sarah A Nadeau
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zürich, 4058 Basel, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Timothy G Vaughan
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zürich, 4058 Basel, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Jérémie Scire
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zürich, 4058 Basel, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Jana S Huisman
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zürich, 4058 Basel, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Department of Environmental Systems Science, Eidgenössiche Technische Hochschule Zürich, 8092 Zürich, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zürich, 4058 Basel, Switzerland;
- Computational Evolution Group, Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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13
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Rochman ND, Wolf YI, Faure G, Mutz P, Zhang F, Koonin EV. Ongoing Global and Regional Adaptive Evolution of SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.10.12.336644. [PMID: 33083804 PMCID: PMC7574262 DOI: 10.1101/2020.10.12.336644] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Understanding the trends in SARS-CoV-2 evolution is paramount to control the COVID-19 pandemic. We analyzed more than 300,000 high quality genome sequences of SARS-CoV-2 variants available as of January 2021. The results show that the ongoing evolution of SARS-CoV-2 during the pandemic is characterized primarily by purifying selection, but a small set of sites appear to evolve under positive selection. The receptor-binding domain of the spike protein and the nuclear localization signal (NLS) associated region of the nucleocapsid protein are enriched with positively selected amino acid replacements. These replacements form a strongly connected network of apparent epistatic interactions and are signatures of major partitions in the SARS-CoV-2 phylogeny. Virus diversity within each geographic region has been steadily growing for the entirety of the pandemic, but analysis of the phylogenetic distances between pairs of regions reveals four distinct periods based on global partitioning of the tree and the emergence of key mutations. The initial period of rapid diversification into region-specific phylogenies that ended in February 2020 was followed by a major extinction event and global homogenization concomitant with the spread of D614G in the spike protein, ending in March 2020. The NLS associated variants across multiple partitions rose to global prominence in March-July, during a period of stasis in terms of inter-regional diversity. Finally, beginning July 2020, multiple mutations, some of which have since been demonstrated to enable antibody evasion, began to emerge associated with ongoing regional diversification, which might be indicative of speciation.
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Affiliation(s)
- Nash D Rochman
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894
| | - Yuri I Wolf
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894
| | - Guilhem Faure
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Pascal Mutz
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894
| | - Feng Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894
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14
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Fang S, Li K, Shen J, Liu S, Liu J, Yang L, Hu CD, Wan J. GESS: a database of global evaluation of SARS-CoV-2/hCoV-19 sequences. Nucleic Acids Res 2021; 49:D706-D714. [PMID: 33045727 PMCID: PMC7778918 DOI: 10.1093/nar/gkaa808] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/10/2020] [Accepted: 09/15/2020] [Indexed: 01/11/2023] Open
Abstract
The COVID-19 outbreak has become a global emergency since December 2019. Analysis of SARS-CoV-2 sequences can uncover single nucleotide variants (SNVs) and corresponding evolution patterns. The Global Evaluation of SARS-CoV-2/hCoV-19 Sequences (GESS, https://wan-bioinfo.shinyapps.io/GESS/) is a resource to provide comprehensive analysis results based on tens of thousands of high-coverage and high-quality SARS-CoV-2 complete genomes. The database allows user to browse, search and download SNVs at any individual or multiple SARS-CoV-2 genomic positions, or within a chosen genomic region or protein, or in certain country/area of interest. GESS reveals geographical distributions of SNVs around the world and across the states of USA, while exhibiting time-dependent patterns for SNV occurrences which reflect development of SARS-CoV-2 genomes. For each month, the top 100 SNVs that were firstly identified world-widely can be retrieved. GESS also explores SNVs occurring simultaneously with specific SNVs of user's interests. Furthermore, the database can be of great help to calibrate mutation rates and identify conserved genome regions. Taken together, GESS is a powerful resource and tool to monitor SARS-CoV-2 migration and evolution according to featured genomic variations. It provides potential directive information for prevalence prediction, related public health policy making, and vaccine designs.
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Affiliation(s)
- Shuyi Fang
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA
| | - Kailing Li
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA
| | - Jikui Shen
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sheng Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Collaborative Core for Cancer Bioinformatics (CB) shared by Indiana University Simon Comprehensive Cancer Center and Purdue University Center for Cancer Research, Indianapolis, IN, USA
| | - Juli Liu
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Lei Yang
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chang-Deng Hu
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, USA
| | - Jun Wan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Collaborative Core for Cancer Bioinformatics (CB) shared by Indiana University Simon Comprehensive Cancer Center and Purdue University Center for Cancer Research, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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15
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Zhukova A, Blassel L, Lemoine F, Morel M, Voznica J, Gascuel O. Origin, evolution and global spread of SARS-CoV-2. C R Biol 2020; 344:57-75. [PMID: 33274614 DOI: 10.5802/crbiol.29] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 10/27/2020] [Indexed: 12/26/2022]
Abstract
SARS-CoV-2 is the virus responsible for the global COVID19 pandemic. We review what is known about the origin of this virus, detected in China at the end of December 2019. The genome of this virus mainly evolves under the effect of point mutations. These are generally neutral and have no impact on virulence and severity, but some appear to influence infectivity, notably the D614G mutation of the Spike protein. To date (30/09/2020) no recombination of the virus has been documented in the human host, and very few insertions and deletions. The worldwide spread of the virus was the subject of controversies that we summarize, before proposing a new approach free from the limitations of previous methods. The results show a complex scenario with, for example, numerous introductions to the USA and returns of the virus from the USA to certain countries including France.
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Affiliation(s)
- Anna Zhukova
- Hub de Bioinformatique et Biostatistique, Institut Pasteur, Paris, France
- Unité de Bioinformatique Evolutive, Institut Pasteur, Paris, France
- Unité de Biologie Computationnelle, USR3756, CNRS, Paris, France
| | - Luc Blassel
- Unité de Bioinformatique Evolutive, Institut Pasteur, Paris, France
- Unité de Biologie Computationnelle, USR3756, CNRS, Paris, France
| | - Frédéric Lemoine
- Hub de Bioinformatique et Biostatistique, Institut Pasteur, Paris, France
- Unité de Bioinformatique Evolutive, Institut Pasteur, Paris, France
- Unité de Biologie Computationnelle, USR3756, CNRS, Paris, France
| | - Marie Morel
- Unité de Bioinformatique Evolutive, Institut Pasteur, Paris, France
- Unité de Biologie Computationnelle, USR3756, CNRS, Paris, France
| | - Jakub Voznica
- Unité de Bioinformatique Evolutive, Institut Pasteur, Paris, France
- Unité de Biologie Computationnelle, USR3756, CNRS, Paris, France
| | - Olivier Gascuel
- Unité de Biologie Computationnelle, USR3756, CNRS, Paris, France
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16
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Mavian C, Marini S, Prosperi M, Salemi M. Authors' Reply to: Errors in Tracing Coronavirus SARS-CoV-2 Transmission Using a Maximum Likelihood Tree. Comment on "A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis". JMIR Public Health Surveill 2020; 6:e24661. [PMID: 33174844 PMCID: PMC7688377 DOI: 10.2196/24661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 10/01/2020] [Indexed: 11/20/2022] Open
Affiliation(s)
- Carla Mavian
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
| | - Simone Marini
- Department of Epidemiology, College of Public Health and Health Professions, Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions, Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
| | - Marco Salemi
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
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17
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Rito T, Richards MB, Pala M, Correia-Neves M, Soares PA. Phylogeography of 27,000 SARS-CoV-2 Genomes: Europe as the Major Source of the COVID-19 Pandemic. Microorganisms 2020; 8:E1678. [PMID: 33137892 PMCID: PMC7693378 DOI: 10.3390/microorganisms8111678] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/13/2022] Open
Abstract
The novel coronavirus SARS-CoV-2 emerged from a zoonotic transmission in China towards the end of 2019, rapidly leading to a global pandemic on a scale not seen for a century. In order to cast fresh light on the spread of the virus and on the effectiveness of the containment measures adopted globally, we used 26,869 SARS-CoV-2 genomes to build a phylogeny with 20,247 mutation events and adopted a phylogeographic approach. We confirmed that the phylogeny pinpoints China as the origin of the pandemic with major founders worldwide, mainly during January 2020. However, a single specific East Asian founder underwent massive radiation in Europe and became the main actor of the subsequent spread worldwide during March 2020. This lineage accounts for the great majority of cases detected globally and even spread back to the source in East Asia. Despite an East Asian source, therefore, the global pandemic was mainly fueled by its expansion across and out of Europe. It seems likely that travel bans established throughout the world in the second half of March helped to decrease the number of intercontinental exchanges, particularly from mainland China, but were less effective between Europe and North America where exchanges in both directions are visible up to April, long after bans were imposed.
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Affiliation(s)
- Teresa Rito
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; (T.R.); (M.C.-N.)
- ICVS/3B’s, PT Government Associate Laboratory, University of Minho, 4710-057 Braga, Portugal
| | - Martin B. Richards
- Department of Biological and Geographical Sciences, School of Applied Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK; (M.B.R.); (M.P.)
| | - Maria Pala
- Department of Biological and Geographical Sciences, School of Applied Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK; (M.B.R.); (M.P.)
| | - Margarida Correia-Neves
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; (T.R.); (M.C.-N.)
- ICVS/3B’s, PT Government Associate Laboratory, University of Minho, 4710-057 Braga, Portugal
| | - Pedro A. Soares
- Centre of Molecular and Environmental Biology (CBMA), Department of Biology, University of Minho, 4710-057 Braga, Portugal
- Institute of Science and Innovation for Bio-Sustainability (IB-S), University of Minho, 4710-057 Braga, Portugal
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18
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Velazquez-Salinas L, Zarate S, Eberl S, Gladue DP, Novella I, Borca MV. Positive Selection of ORF1ab, ORF3a, and ORF8 Genes Drives the Early Evolutionary Trends of SARS-CoV-2 During the 2020 COVID-19 Pandemic. Front Microbiol 2020; 11:550674. [PMID: 33193132 PMCID: PMC7644918 DOI: 10.3389/fmicb.2020.550674] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 09/28/2020] [Indexed: 11/13/2022] Open
Abstract
In this study, we analyzed full-length SARS-CoV-2 genomes from multiple countries to determine early trends in the evolutionary dynamics of the novel COVID-19 pandemic. Results indicated SARS-CoV-2 evolved early into at least three phylogenetic groups, characterized by positive selection at specific residues of the accessory proteins ORF3a and ORF8. Also, we are reporting potential relevant sites under positive selection at specific sites of non-structural proteins nsp6 and helicase. Our analysis of co-evolution showed evidence of epistatic interactions among sites in the genome that may be important in the generation of variants adapted to humans. These observations might impact not only public health but also suggest that more studies are needed to understand the genetic mechanisms that may affect the development of therapeutic and preventive tools, like antivirals and vaccines. Collectively, our results highlight the identification of ongoing selection even in a scenario of conserved sequences collected over the first 3 months of this pandemic.
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Affiliation(s)
- Lauro Velazquez-Salinas
- Foreign Animal Disease Research Unit, USDA/ARS Plum Island Animal Disease Center, Greenport, NY, United States.,College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
| | - Selene Zarate
- Posgrado en Ciencias Genómicas, Universidad Autónoma de la Ciudad de Mexico, Mexico City, Mexico
| | - Samantha Eberl
- Department of Psychological Science, Central Connecticut State University, New Britain, CT, United States
| | - Douglas P Gladue
- Foreign Animal Disease Research Unit, USDA/ARS Plum Island Animal Disease Center, Greenport, NY, United States
| | | | - Manuel V Borca
- Foreign Animal Disease Research Unit, USDA/ARS Plum Island Animal Disease Center, Greenport, NY, United States
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19
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Diagnosing the novel SARS-CoV-2 by quantitative RT-PCR: variations and opportunities. J Mol Med (Berl) 2020; 98:1727-1736. [PMID: 33067676 PMCID: PMC7567654 DOI: 10.1007/s00109-020-01992-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 12/22/2022]
Abstract
The world is currently facing a novel viral pandemic (SARS-CoV-2), and large-scale testing is central to decision-making for the design of effective policies and control strategies to minimize its impact on the global population. However, testing for the presence of the virus is a major bottleneck in tracking the spreading of the disease. Given its adaptability regarding the nucleotide sequence of target regions, RT-qPCR is a strong ally to reveal the rapid geographical spreading of novel viruses. We assessed PCR variations in the SARS-CoV-2 diagnosis taking into account public genome sequences and diagnosis kits used by different countries. We analyzed 226 SARS-CoV-2 genome sequences from samples collected by March 22, 2020. Our work utilizes a phylogenetic approach that reveals the early evolution of the virus sequence as it spreads around the globe and informs the design of RT-qPCR primers and probes. The quick expansion of testing capabilities of a country during a pandemic is largely impaired by the availability of adequately trained personnel on RNA isolation and PCR analysis, as well as the availability of hardware (thermocyclers). We propose that rapid capacity development can circumvent these bottlenecks by training medical and non-medical personnel with some laboratory experience, such as biology-related graduate students. Furthermore, the use of thermocyclers available in academic and commercial labs can be promptly calibrated and certified to properly conduct testing during a pandemic. A decentralized, fast-acting training and testing certification pipeline will better prepare us to manage future pandemics.
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20
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Pereson MJ, Mojsiejczuk L, Martínez AP, Flichman DM, Garcia GH, Di Lello FA. Phylogenetic analysis of SARS-CoV-2 in the first few months since its emergence. J Med Virol 2020; 93:1722-1731. [PMID: 32966646 PMCID: PMC7537150 DOI: 10.1002/jmv.26545] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/10/2020] [Accepted: 09/19/2020] [Indexed: 12/24/2022]
Abstract
During the first few months of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution in a new host, contrasting hypotheses have been proposed about the way the virus has evolved and diversified worldwide. The aim of this study was to perform a comprehensive evolutionary analysis to describe the human outbreak and the evolutionary rate of different genomic regions of SARS-CoV-2. The molecular evolution in nine genomic regions of SARS-CoV-2 was analyzed using three different approaches: phylogenetic signal assessment, emergence of amino acid substitutions, and Bayesian evolutionary rate estimation in eight successive fortnights since the virus emergence. All observed phylogenetic signals were very low and tree topologies were in agreement with those signals. However, after 4 months of evolution, it was possible to identify regions revealing an incipient viral lineage formation, despite the low phylogenetic signal since fortnight 3. Finally, the SARS-CoV-2 evolutionary rate for regions nsp3 and S, the ones presenting greater variability, was estimated as 1.37 × 10-3 and 2.19 × 10-3 substitution/site/year, respectively. In conclusion, results from this study about the variable diversity of crucial viral regions and determination of the evolutionary rate are consequently decisive to understand essential features of viral emergence. In turn, findings may allow the first-time characterization of the evolutionary rate of S protein, crucial for vaccine development.
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Affiliation(s)
- Matías J. Pereson
- Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Instituto de Investigaciones en Bacteriología y Virología Molecular (IBaViM)Buenos AiresArgentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos AiresArgentina
| | - Laura Mojsiejczuk
- Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Instituto de Investigaciones en Bacteriología y Virología Molecular (IBaViM)Buenos AiresArgentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos AiresArgentina
| | - Alfredo P. Martínez
- Virology Section, Centro de Educación Médica e Investigaciones Clínicas Norberto Quirno “CEMIC”Buenos AiresArgentina
| | - Diego M. Flichman
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos AiresArgentina
- Instituto de Investigaciones Biomédicas en Retrovirus y Síndrome de Inmunodeficiencia Adquirida (INBIRS) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad de Buenos AiresBuenos AiresArgentina
| | - Gabriel H. Garcia
- Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Instituto de Investigaciones en Bacteriología y Virología Molecular (IBaViM)Buenos AiresArgentina
| | - Federico A. Di Lello
- Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Instituto de Investigaciones en Bacteriología y Virología Molecular (IBaViM)Buenos AiresArgentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos AiresArgentina
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21
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Watson JA, Taylor AR, Ashley EA, Dondorp A, Buckee CO, White NJ, Holmes CC. A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices. PLoS Genet 2020; 16:e1009037. [PMID: 33035220 PMCID: PMC7577480 DOI: 10.1371/journal.pgen.1009037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/21/2020] [Accepted: 08/08/2020] [Indexed: 11/20/2022] Open
Abstract
Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results.
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Affiliation(s)
- James A. Watson
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Aimee R. Taylor
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Elizabeth A. Ashley
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Lao-Oxford-Mahosot Hospital Wellcome Trust Research Unit, Vientiane, Laos
| | - Arjen Dondorp
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Caroline O. Buckee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Nicholas J. White
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Chris C. Holmes
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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22
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Liu S, Shen J, Fang S, Li K, Liu J, Yang L, Hu CD, Wan J. Genetic Spectrum and Distinct Evolution Patterns of SARS-CoV-2. Front Microbiol 2020; 11:593548. [PMID: 33101264 PMCID: PMC7545136 DOI: 10.3389/fmicb.2020.593548] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 09/07/2020] [Indexed: 12/20/2022] Open
Abstract
Four signature groups of frequently occurred single-nucleotide variants (SNVs) were identified in over twenty-eight thousand high-quality and high-coverage SARS-CoV-2 complete genome sequences, representing different viral strains. Some SNVs predominated but were mutually exclusively presented in patients from different countries and areas. These major SNV signatures exhibited distinguishable evolution patterns over time. A few hundred patients were detected with multiple viral strain-representing mutations simultaneously, which may stand for possible co-infection or potential homogenous recombination of SARS-CoV-2 in environment or within the viral host. Interestingly nucleotide substitutions among SARS-CoV-2 genomes tended to switch between bat RaTG13 coronavirus sequence and Wuhan-Hu-1 genome, indicating the higher genetic instability or tolerance of mutations on those sites or suggesting that major viral strains might exist between Wuhan-Hu-1 and RaTG13 coronavirus.
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Affiliation(s)
- Sheng Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States.,Collaborative Core for Cancer Bioinformatics (C3B) shared by Indiana University Simon Comprehensive Cancer Center and Purdue University Center for Cancer Research, Indianapolis, IN, United States
| | - Jikui Shen
- The Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Shuyi Fang
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University - Purdue University Indianapolis, Indianapolis, IN, United States
| | - Kailing Li
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University - Purdue University Indianapolis, Indianapolis, IN, United States
| | - Juli Liu
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Lei Yang
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Chang-Deng Hu
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, United States.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, United States
| | - Jun Wan
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States.,Collaborative Core for Cancer Bioinformatics (C3B) shared by Indiana University Simon Comprehensive Cancer Center and Purdue University Center for Cancer Research, Indianapolis, IN, United States.,Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University - Purdue University Indianapolis, Indianapolis, IN, United States.,The Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
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23
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DeSalle R, Riley M. Should Networks Supplant Tree Building? Microorganisms 2020; 8:E1179. [PMID: 32756444 PMCID: PMC7466111 DOI: 10.3390/microorganisms8081179] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/21/2020] [Accepted: 07/29/2020] [Indexed: 12/15/2022] Open
Abstract
Recent studies suggested that network methods should supplant tree building as the basis of genealogical analysis. This proposition is based upon two arguments. First is the observation that bacterial and archaeal lineages experience processes oppositional to bifurcation and hence the representation of the evolutionary process in a tree like structure is illogical. Second is the argument tree building approaches are circular-you ask for a tree and you get one, which pins a verificationist label on tree building that, if correct, should be the end of phylogenetic analysis as we currently know it. In this review, we examine these questions and suggest that rumors of the death of the bacterial tree of life are exaggerated at best.
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Affiliation(s)
- Rob DeSalle
- Sackler Institute for Comparative Genomics, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024, USA;
| | - Margaret Riley
- Department of Biology, University of Massachusetts Amherst, 116 North Pleasant Street, Amherst, MA 01003, USA
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24
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Llanes A, Restrepo CM, Caballero Z, Rajeev S, Kennedy MA, Lleonart R. Betacoronavirus Genomes: How Genomic Information has been Used to Deal with Past Outbreaks and the COVID-19 Pandemic. Int J Mol Sci 2020; 21:E4546. [PMID: 32604724 PMCID: PMC7352669 DOI: 10.3390/ijms21124546] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/22/2020] [Accepted: 06/23/2020] [Indexed: 12/22/2022] Open
Abstract
In the 21st century, three highly pathogenic betacoronaviruses have emerged, with an alarming rate of human morbidity and case fatality. Genomic information has been widely used to understand the pathogenesis, animal origin and mode of transmission of coronaviruses in the aftermath of the 2002-2003 severe acute respiratory syndrome (SARS) and 2012 Middle East respiratory syndrome (MERS) outbreaks. Furthermore, genome sequencing and bioinformatic analysis have had an unprecedented relevance in the battle against the 2019-2020 coronavirus disease 2019 (COVID-19) pandemic, the newest and most devastating outbreak caused by a coronavirus in the history of mankind. Here, we review how genomic information has been used to tackle outbreaks caused by emerging, highly pathogenic, betacoronavirus strains, emphasizing on SARS-CoV, MERS-CoV and SARS-CoV-2. We focus on shared genomic features of the betacoronaviruses and the application of genomic information to phylogenetic analysis, molecular epidemiology and the design of diagnostic systems, potential drugs and vaccine candidates.
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Affiliation(s)
- Alejandro Llanes
- Centro de Biología Celular y Molecular de Enfermedades, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City 0801, Panama; (A.L.); (C.M.R.); (Z.C.)
| | - Carlos M. Restrepo
- Centro de Biología Celular y Molecular de Enfermedades, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City 0801, Panama; (A.L.); (C.M.R.); (Z.C.)
| | - Zuleima Caballero
- Centro de Biología Celular y Molecular de Enfermedades, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City 0801, Panama; (A.L.); (C.M.R.); (Z.C.)
| | - Sreekumari Rajeev
- College of Veterinary Medicine, University of Florida, Gainesville, FL 32610, USA;
| | - Melissa A. Kennedy
- College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996, USA;
| | - Ricardo Lleonart
- Centro de Biología Celular y Molecular de Enfermedades, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City 0801, Panama; (A.L.); (C.M.R.); (Z.C.)
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25
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Mavian C, Marini S, Prosperi M, Salemi M. A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis. JMIR Public Health Surveill 2020; 6:e19170. [PMID: 32412415 PMCID: PMC7265655 DOI: 10.2196/19170] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/09/2020] [Accepted: 05/12/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been growing exponentially, affecting over 4 million people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on viral sequences has already been published both in scientific journals and through non-peer-reviewed channels to investigate the genetic heterogeneity and spatiotemporal dissemination of SARS-CoV-2. However, a systematic investigation of phylogenetic information and sampling bias in the available data is lacking. Although the number of available genome sequences of SARS-CoV-2 is growing daily and the sequences show increasing phylogenetic information, country-specific data still present severe limitations and should be interpreted with caution. OBJECTIVE The objective of this study was to determine the quality of the currently available SARS-CoV-2 full genome data in terms of sampling bias as well as phylogenetic and temporal signals to inform and guide the scientific community. METHODS We used maximum likelihood-based methods to assess the presence of sufficient information for robust phylogenetic and phylogeographic studies in several SARS-CoV-2 sequence alignments assembled from GISAID (Global Initiative on Sharing All Influenza Data) data released between March and April 2020. RESULTS Although the number of high-quality full genomes is growing daily, and sequence data released in April 2020 contain sufficient phylogenetic information to allow reliable inference of phylogenetic relationships, country-specific SARS-CoV-2 data sets still present severe limitations. CONCLUSIONS At the present time, studies assessing within-country spread or transmission clusters should be considered preliminary or hypothesis-generating at best. Hence, current reports should be interpreted with caution, and concerted efforts should continue to increase the number and quality of sequences required for robust tracing of the epidemic.
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Affiliation(s)
- Carla Mavian
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
- Department of Pathology, University of Florida, Gainesville, FL, United States
| | - Simone Marini
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, University of Florida, Gainesville, FL, United States
| | - Marco Salemi
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, United States
- Department of Pathology, University of Florida, Gainesville, FL, United States
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26
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Reply to Sánchez-Pacheco et al., Chookajorn, and Mavian et al.: Explaining phylogenetic network analysis of SARS-CoV-2 genomes. Proc Natl Acad Sci U S A 2020; 117:12524-12525. [PMID: 32439706 PMCID: PMC7293629 DOI: 10.1073/pnas.2007433117] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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