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Yi B, Patrasová E, Šimůnková L, Rost F, Winkler S, Laubner A, Reinhardt S, Dahl A, Dalpke AH. Investigating the cause of a 2021 winter wave of COVID-19 in a border region in eastern Germany: a mixed-methods study, August to November 2021. Epidemiol Infect 2024; 152:e87. [PMID: 38751220 PMCID: PMC11149030 DOI: 10.1017/s0950268824000761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/15/2024] [Accepted: 04/24/2024] [Indexed: 05/31/2024] Open
Abstract
It is so far unclear how the COVID-19 winter waves started and what should be done to prevent possible future waves. In this study, we deciphered the dynamic course of a winter wave in 2021 in Saxony, a state in Eastern Germany neighbouring the Czech Republic and Poland. The study was carried out through the integration of multiple virus genomic epidemiology approaches to track transmission chains, identify emerging variants and investigate dynamic changes in transmission clusters. For identified local variants of interest, functional evaluations were performed. Multiple long-lasting community transmission clusters have been identified acting as driving force for the winter wave 2021. Analysis of the dynamic courses of two representative clusters indicated a similar transmission pattern. However, the transmission cluster caused by a locally occurring new Delta variant AY.36.1 showed a distinct transmission pattern, and functional analyses revealed a replication advantage of it. This study indicated that long-lasting community transmission clusters starting since early autumn caused by imported or locally occurring variants all contributed to the development of the 2021 winter wave. The information we achieved might help future pandemic prevention.
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Affiliation(s)
- Buqing Yi
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Eva Patrasová
- Department of Epidemiology, Regional Public Health Authority for Ustecky Kraj, Ústí nad Labem, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Lenka Šimůnková
- Department of Epidemiology, Regional Public Health Authority for Ustecky Kraj, Ústí nad Labem, Czech Republic
| | - Fabian Rost
- DRESDEN-Concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
- Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany
| | - Sylke Winkler
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- DRESDEN-Concept Genome Center, Technische Universität Dresden, Dresden, Germany
| | - Alexa Laubner
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Susanne Reinhardt
- DRESDEN-Concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Andreas Dahl
- DRESDEN-Concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Alexander H. Dalpke
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Department of Infectious Diseases, Medical Microbiology and Hygiene, University of Heidelberg, Heidelberg, Germany
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Kogoj R, Grašek M, Suljič A, Zakotnik S, Vlaj D, Kotnik Koman K, Fafangel M, Petrovec M, Avšič-Županc T, Korva M. Sequencing analysis of SARS-CoV-2 cases in Slovenian long-term care facilities to support outbreak control. Front Public Health 2024; 12:1406777. [PMID: 38813418 PMCID: PMC11133669 DOI: 10.3389/fpubh.2024.1406777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/02/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction Residents of long-term care facilities (LTCFs) are at high risk of morbidity and mortality due to COVID-19, especially when new variants of concern (VOC) emerge. To provide intradisciplinary data in order to tailor public health interventions during future epidemics, available epidemiologic and genomic data from Slovenian LTCFs during the initial phases of the COVID-19 pandemic was analyzed. Methods The first part of the study included SARS-CoV-2 reverse-transcription Real-Time PCR (rtRT-PCR) positive LTCF residents, from 21 facilities with COVID-19 outbreaks occurring in October 2020. The second part of the study included SARS-CoV-2 rtRT-PCR positive LTCF residents and staff between January and April 2021, when VOC Alpha emerged in Slovenia. Next-generation sequencing (NGS) was used to acquire SARS-CoV-2 genomes, and lineage determination. In-depth phylogenetic and mutational profile analysis were performed and coupled with available field epidemiological data to assess the dynamics of SARS-CoV-2 introduction and transmission. Results 370/498 SARS-CoV-2 positive residents as well as 558/699 SARS-CoV-2 positive residents and 301/358 staff were successfully sequenced in the first and second part of the study, respectively. In October 2020, COVID-19 outbreaks in the 21 LTCFs were caused by intra-facility transmission as well as multiple independent SARS-CoV-2 introductions. The Alpha variant was confirmed in the first LTCF resident approximately 1.5 months after the first Alpha case was identified in Slovenia. The data also showed a slower replacement of existing variants by Alpha in residents compared to staff and the general population. Discussion Multiple SARS CoV-2 introductions as well as intra-facility spreading impacted disease transmission in Slovenian LTCFs. Timely implementation of control measures aimed at limiting new introductions while controlling in-facility transmission are of paramount importance, especially as new VOCs emerge. Sequencing, in conjunction with epidemiological data, can facilitate the determination of the need for future improvements in control measures to protect LTCF residents from COVID-19 or other respiratory infections.
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Affiliation(s)
- Rok Kogoj
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Manja Grašek
- Communicable Diseases Center, National Institute of Public Health, Ljubljana, Slovenia
| | - Alen Suljič
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Samo Zakotnik
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Doroteja Vlaj
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Kaja Kotnik Koman
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Mario Fafangel
- Communicable Diseases Center, National Institute of Public Health, Ljubljana, Slovenia
| | - Miroslav Petrovec
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tatjana Avšič-Županc
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Misa Korva
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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3
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Chen Q, Wu B, Li C, Ding L, Huang S, Wang J, Zhao J. Deciphering male influence in gynogenetic Pengze crucian carp ( Carassius auratus var. pengsenensis): insights from Nanopore sequencing of structural variations. Front Genet 2024; 15:1392110. [PMID: 38784042 PMCID: PMC11111978 DOI: 10.3389/fgene.2024.1392110] [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: 02/27/2024] [Accepted: 04/11/2024] [Indexed: 05/25/2024] Open
Abstract
In this study, we investigate gynogenetic reproduction in Pengze Crucian Carp (Carassius auratus var. pengsenensis) using third-generation Nanopore sequencing to uncover structural variations (SVs) in offspring. Our objective was to understand the role of male genetic material in gynogenesis by examining the genomes of both parents and their offspring. We discovered a notable number of male-specific structural variations (MSSVs): 1,195 to 1,709 MSSVs in homologous offspring, accounting for approximately 0.52%-0.60% of their detected SVs, and 236 to 350 MSSVs in heterologous offspring, making up about 0.10%-0.13%. These results highlight the significant influence of male genetic material on the genetic composition of offspring, particularly in homologous pairs, challenging the traditional view of asexual reproduction. The gene annotation of MSSVs revealed their presence in critical gene regions, indicating potential functional impacts. Specifically, we found 5 MSSVs in the exonic regions of protein-coding genes in homologous offspring, suggesting possible direct effects on protein structure and function. Validation of an MSSV in the exonic region of the polyunsaturated fatty acid 5-lipoxygenase gene confirmed male genetic material transmission in some offspring. This study underscores the importance of further research on the genetic diversity and gynogenesis mechanisms, providing valuable insights for reproductive biology, aquaculture, and fostering innovation in biological research and aquaculture practices.
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Affiliation(s)
- Qianhui Chen
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, School of Life Sciences, South China Normal University, Guangzhou, China
| | - Biyu Wu
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, School of Life Sciences, South China Normal University, Guangzhou, China
| | - Chao Li
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, School of Life Sciences, South China Normal University, Guangzhou, China
| | - Liyun Ding
- Jiangxi Fisheries Research Institute, Nanchang, China
| | - Shiting Huang
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, School of Life Sciences, South China Normal University, Guangzhou, China
| | - Junjie Wang
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, School of Life Sciences, South China Normal University, Guangzhou, China
| | - Jun Zhao
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, School of Life Sciences, South China Normal University, Guangzhou, China
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Munson E. Biographical Feature: Charles Y. Chiu, M.D., Ph.D. J Clin Microbiol 2024; 62:e0140523. [PMID: 38619264 PMCID: PMC11077958 DOI: 10.1128/jcm.01405-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024] Open
Affiliation(s)
- Erik Munson
- Department of Medical Laboratory Science, Marquette University, Milwaukee, Wisconsin, USA
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5
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Esser E, Schulte EC, Graf A, Karollus A, Smith NH, Michler T, Dvoretskii S, Angelov A, Sonnabend M, Peter S, Engesser C, Radonic A, Thürmer A, von Kleist M, Gebhardt F, da Costa CP, Busch DH, Muenchhoff M, Blum H, Keppler OT, Gagneur J, Protzer U. Viral genome sequencing to decipher in-hospital SARS-CoV-2 transmission events. Sci Rep 2024; 14:5768. [PMID: 38459123 PMCID: PMC10923895 DOI: 10.1038/s41598-024-56162-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/02/2024] [Indexed: 03/10/2024] Open
Abstract
The SARS-CoV-2 pandemic has highlighted the need to better define in-hospital transmissions, a need that extends to all other common infectious diseases encountered in clinical settings. To evaluate how whole viral genome sequencing can contribute to deciphering nosocomial SARS-CoV-2 transmission 926 SARS-CoV-2 viral genomes from 622 staff members and patients were collected between February 2020 and January 2021 at a university hospital in Munich, Germany, and analysed along with the place of work, duration of hospital stay, and ward transfers. Bioinformatically defined transmission clusters inferred from viral genome sequencing were compared to those inferred from interview-based contact tracing. An additional dataset collected at the same time at another university hospital in the same city was used to account for multiple independent introductions. Clustering analysis of 619 viral genomes generated 19 clusters ranging from 3 to 31 individuals. Sequencing-based transmission clusters showed little overlap with those based on contact tracing data. The viral genomes were significantly more closely related to each other than comparable genomes collected simultaneously at other hospitals in the same city (n = 829), suggesting nosocomial transmission. Longitudinal sampling from individual patients suggested possible cross-infection events during the hospital stay in 19.2% of individuals (14 of 73 individuals). Clustering analysis of SARS-CoV-2 whole genome sequences can reveal cryptic transmission events missed by classical, interview-based contact tracing, helping to decipher in-hospital transmissions. These results, in line with other studies, advocate for viral genome sequencing as a pathogen transmission surveillance tool in hospitals.
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Affiliation(s)
- Elisabeth Esser
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Eva C Schulte
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany
- Department of Psychiatry, University Hospital, LMU Munich, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry, University Hospital, Medical Faculty, University of Bonn, Bonn, Germany
- Institute of Human Genetics, University Hospital, Medical Faculty, University of Bonn, Bonn, Germany
| | - Alexander Graf
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, Munich, Germany
| | - Alexander Karollus
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Nicholas H Smith
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Thomas Michler
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany
- Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Stefan Dvoretskii
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Angel Angelov
- NGS Competence Center, University of Tübingen, Tübingen, Germany
| | | | - Silke Peter
- NGS Competence Center, University of Tübingen, Tübingen, Germany
| | | | - Aleksandar Radonic
- Method development, Research Infrastructure & IT (MFI), Robert-Koch Institute (RKI), Berlin, Germany
| | - Andrea Thürmer
- Method development, Research Infrastructure & IT (MFI), Robert-Koch Institute (RKI), Berlin, Germany
| | - Max von Kleist
- Department of Mathematics and Computer Science, Freie Universität (FU) Berlin, Berlin, Germany
- Project Groups, Robert-Koch Institute (RKI), Berlin, Germany
| | - Friedemann Gebhardt
- Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany
| | - Clarissa Prazeres da Costa
- Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
| | - Dirk H Busch
- Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
| | - Maximilian Muenchhoff
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
- Max Von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Helmut Blum
- Laboratory for Functional Genome Analysis, Gene Center, LMU Munich, Munich, Germany
| | - Oliver T Keppler
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany
- Max Von Pettenkofer Institute and Gene Center, Virology, National Reference Center for Retroviruses, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Institute of Human Genetics, School of Medicine & Health, Technical University of Munich, Munich, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
| | - Ulrike Protzer
- Institute of Virology, School of Medicine & Health, Technical University of Munich/Helmholtz Munich, Munich, Germany.
- German Center for Infection Research (DZIF), Munich Partner Site, Munich, Germany.
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Ling-Hu T, Simons LM, Dean TJ, Rios-Guzman E, Caputo MT, Alisoltani A, Qi C, Malczynski M, Blanke T, Jennings LJ, Ison MG, Achenbach CJ, Larkin PM, Kaul KL, Lorenzo-Redondo R, Ozer EA, Hultquist JF. Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes. Cell Rep Med 2024; 5:101361. [PMID: 38232695 PMCID: PMC10829796 DOI: 10.1016/j.xcrm.2023.101361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/07/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with enhanced transmissibility and immune escape have emerged periodically throughout the coronavirus disease 2019 (COVID-19) pandemic, but the impact of these variants on disease severity has remained unclear. In this single-center, retrospective cohort study, we examined the association between SARS-CoV-2 clade and patient outcome over a two-year period in Chicago, Illinois. Between March 2020 and March 2022, 14,252 residual diagnostic specimens were collected from SARS-CoV-2-positive inpatients and outpatients alongside linked clinical and demographic metadata, of which 2,114 were processed for viral whole-genome sequencing. When controlling for patient demographics and vaccination status, several viral clades were associated with risk for hospitalization, but this association was negated by the inclusion of population-level confounders, including case count, sampling bias, and shifting standards of care. These data highlight the importance of integrating non-virological factors into disease severity and outcome models for the accurate assessment of patient risk.
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Affiliation(s)
- Ted Ling-Hu
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Lacy M Simons
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Taylor J Dean
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Estefany Rios-Guzman
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Matthew T Caputo
- Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Arghavan Alisoltani
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Chao Qi
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Michael Malczynski
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Timothy Blanke
- Diagnostic Molecular Biology Laboratory, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Lawrence J Jennings
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Michael G Ison
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Chad J Achenbach
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Paige M Larkin
- Department of Molecular Microbiology, Northshore University HealthSystem, Evanston, IL 60201, USA
| | - Karen L Kaul
- Department of Pathology, Northshore University HealthSystem, Evanston, IL 60201, USA
| | - Ramon Lorenzo-Redondo
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Egon A Ozer
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Judd F Hultquist
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA.
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Scotch M, Lauer K, Wieben ED, Cherukuri Y, Cunningham JM, Klee EW, Harrington JJ, Lau JS, McDonough SJ, Mutawe M, O'Horo JC, Rentmeester CE, Schlicher NR, White VT, Schneider SK, Vedell PT, Wang X, Yao JD, Pritt BS, Norgan AP. Genomic epidemiology reveals the dominance of Hennepin County in the transmission of SARS-CoV-2 in Minnesota from 2020 to 2022. mSphere 2023; 8:e0023223. [PMID: 37882516 PMCID: PMC10871168 DOI: 10.1128/msphere.00232-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/20/2023] [Indexed: 10/27/2023] Open
Abstract
IMPORTANCE We analyzed over 22,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of patient samples tested at Mayo Clinic Laboratories during a 2-year period in the COVID-19 pandemic, which included Alpha, Delta, and Omicron variants of concern to examine the roles and relationships of Minnesota virus transmission. We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after an initial predicted introduction with the virus in early 2020 from an international source, while other counties acted as transmission "sinks." In addition, major policies, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.
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Affiliation(s)
- Matthew Scotch
- Research Affiliate, Mayo Clinic, Phoenix, Arizona, USA
- Biodesign Institute, Arizona State University, Tempe, Arizona, USA
- College of Health Solutions, Arizona State University, Phoenix, Arizona, USA
| | - Kimberly Lauer
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric D. Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Julie M. Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric W. Klee
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Rochester, Minnesota, USA
| | | | - Julie S. Lau
- Center for Individualized Medicine, Rochester, Minnesota, USA
| | | | - Mark Mutawe
- Center for Individualized Medicine, Rochester, Minnesota, USA
| | - John C. O'Horo
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Chad E. Rentmeester
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Saint Mary’s University of Minnesota, Winona, Minnesota, USA
| | - Nicole R. Schlicher
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Valerie T. White
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan K. Schneider
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter T. Vedell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Xiong Wang
- Minnesota Department of Health, St. Paul, Minnesota, USA
| | - Joseph D. Yao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Bobbi S. Pritt
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
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8
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Barrett C, Bura AC, He Q, Huang FW, Li TJX, Reidys CM. Motifs in SARS-CoV-2 evolution. RNA (NEW YORK, N.Y.) 2023; 30:1-15. [PMID: 37903545 PMCID: PMC10726165 DOI: 10.1261/rna.079557.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 09/20/2023] [Indexed: 11/01/2023]
Abstract
We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of coevolving sites in the alignment (motifs), identified via coevolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the coevolutionary coupling of pairs and manifest as coevolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across 21 lineages and 27 countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness, and robustness. Lastly, we study how well each lineage is classified by such a system.
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Affiliation(s)
- Christopher Barrett
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA
- Department of Computer Science, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Andrei C Bura
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Qijun He
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Fenix W Huang
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Thomas J X Li
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Christian M Reidys
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA
- Department of Mathematics, University of Virginia, Charlottesville, Virginia 22904, USA
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9
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Kramer AM, Thornlow B, Ye C, De Maio N, McBroome J, Hinrichs AS, Lanfear R, Turakhia Y, Corbett-Detig R. Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations. Syst Biol 2023; 72:1039-1051. [PMID: 37232476 PMCID: PMC10627557 DOI: 10.1093/sysbio/syad031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 05/14/2023] [Accepted: 06/22/2023] [Indexed: 05/27/2023] Open
Abstract
Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths.
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Affiliation(s)
- Alexander M Kramer
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Bryan Thornlow
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Cheng Ye
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Jakob McBroome
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Angie S Hinrichs
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Robert Lanfear
- Department of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
| | - Yatish Turakhia
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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10
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Wadford DA, Baumrind N, Baylis EF, Bell JM, Bouchard EL, Crumpler M, Foote EM, Gilliam S, Glaser CA, Hacker JK, Ledin K, Messenger SL, Morales C, Smith EA, Sevinsky JR, Corbett-Detig RB, DeRisi J, Jacobson K. Implementation of California COVIDNet - a multi-sector collaboration for statewide SARS-CoV-2 genomic surveillance. Front Public Health 2023; 11:1249614. [PMID: 37937074 PMCID: PMC10627185 DOI: 10.3389/fpubh.2023.1249614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/27/2023] [Indexed: 11/09/2023] Open
Abstract
Introduction The SARS-CoV-2 pandemic represented a formidable scientific and technological challenge to public health due to its rapid spread and evolution. To meet these challenges and to characterize the virus over time, the State of California established the California SARS-CoV-2 Whole Genome Sequencing (WGS) Initiative, or "California COVIDNet". This initiative constituted an unprecedented multi-sector collaborative effort to achieve large-scale genomic surveillance of SARS-CoV-2 across California to monitor the spread of variants within the state, to detect new and emerging variants, and to characterize outbreaks in congregate, workplace, and other settings. Methods California COVIDNet consists of 50 laboratory partners that include public health laboratories, private clinical diagnostic laboratories, and academic sequencing facilities as well as expert advisors, scientists, consultants, and contractors. Data management, sample sourcing and processing, and computational infrastructure were major challenges that had to be resolved in the midst of the pandemic chaos in order to conduct SARS-CoV-2 genomic surveillance. Data management, storage, and analytics needs were addressed with both conventional database applications and newer cloud-based data solutions, which also fulfilled computational requirements. Results Representative and randomly selected samples were sourced from state-sponsored community testing sites. Since March of 2021, California COVIDNet partners have contributed more than 450,000 SARS-CoV-2 genomes sequenced from remnant samples from both molecular and antigen tests. Combined with genomes from CDC-contracted WGS labs, there are currently nearly 800,000 genomes from all 61 local health jurisdictions (LHJs) in California in the COVIDNet sequence database. More than 5% of all reported positive tests in the state have been sequenced, with similar rates of sequencing across 5 major geographic regions in the state. Discussion Implementation of California COVIDNet revealed challenges and limitations in the public health system. These were overcome by engaging in novel partnerships that established a successful genomic surveillance program which provided valuable data to inform the COVID-19 public health response in California. Significantly, California COVIDNet has provided a foundational data framework and computational infrastructure needed to respond to future public health crises.
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Affiliation(s)
- Debra A. Wadford
- California Department of Public Health, Richmond, CA, United States
| | - Nikki Baumrind
- California Department of Public Health, Richmond, CA, United States
| | | | - John M. Bell
- California Department of Public Health, Richmond, CA, United States
| | | | - Megan Crumpler
- Orange County Public Health Laboratory, Santa Ana, CA, United States
| | - Eric M. Foote
- California Department of Public Health, Richmond, CA, United States
| | - Sabrina Gilliam
- California Department of Public Health, Richmond, CA, United States
| | - Carol A. Glaser
- California Department of Public Health, Richmond, CA, United States
| | - Jill K. Hacker
- California Department of Public Health, Richmond, CA, United States
| | - Katya Ledin
- California Department of Public Health, Richmond, CA, United States
| | | | | | | | | | | | - Joseph DeRisi
- University of California, San Francisco, San Francisco, CA, United States
- Chan Zuckerberg Biohub, San Francisco, CA, United States
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11
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Ma W, Shi L, Li M. A fast and accurate method for SARS-CoV-2 genomic tracing. Brief Bioinform 2023; 24:bbad339. [PMID: 37779249 DOI: 10.1093/bib/bbad339] [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: 07/08/2023] [Revised: 09/04/2023] [Accepted: 09/07/2023] [Indexed: 10/03/2023] Open
Abstract
To contain infectious diseases, it is crucial to determine the origin and transmission routes of the pathogen, as well as how the virus evolves. With the development of genome sequencing technology, genome epidemiology has emerged as a powerful approach for investigating the source and transmission of pathogens. In this study, we first presented the rationale for genomic tracing of SARS-CoV-2 and the challenges we currently face. Identifying the most genetically similar reference sequence to the query sequence is a critical step in genome tracing, typically achieved using either a phylogenetic tree or a sequence similarity search. However, these methods become inefficient or computationally prohibitive when dealing with tens of millions of sequences in the reference database, as we encountered during the COVID-19 pandemic. To address this challenge, we developed a novel genomic tracing algorithm capable of processing 6 million SARS-CoV-2 sequences in less than a minute. Instead of constructing a giant phylogenetic tree, we devised a weighted scoring system based on mutation characteristics to quantify sequences similarity. The developed method demonstrated superior performance compared to previous methods. Additionally, an online platform was developed to facilitate genomic tracing and visualization of the spatiotemporal distribution of sequences. The method will be a valuable addition to standard epidemiological investigations, enabling more efficient genomic tracing. Furthermore, the computational framework can be easily adapted to other pathogens, paving the way for routine genomic tracing of infectious diseases.
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Affiliation(s)
- Wentai Ma
- Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leisheng Shi
- Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingkun Li
- Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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12
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Misra G, Manzoor A, Chopra M, Upadhyay A, Katiyar A, Bhushan B, Anvikar A. Genomic epidemiology of SARS-CoV-2 from Uttar Pradesh, India. Sci Rep 2023; 13:14847. [PMID: 37684328 PMCID: PMC10491582 DOI: 10.1038/s41598-023-42065-6] [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: 05/12/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023] Open
Abstract
The various strains and mutations of SARS-CoV-2 have been tracked using several forms of genomic classification systems. The present study reports high-throughput sequencing and analysis of 99 SARS-CoV-2 specimens from Western Uttar Pradesh using sequences obtained from the GISAID database, followed by phylogeny and clade classification. Phylogenetic analysis revealed that Omicron lineages BA-2-like (55.55%) followed by Delta lineage-B.1.617.2 (45.5%) were predominantly circulating in this area Signature substitution at positions S: N501Y, S: D614G, S: T478K, S: K417N, S: E484A, S: P681H, and S: S477N were commonly detected in the Omicron variant-BA-2-like, however S: D614G, S: L452R, S: P681R and S: D950N were confined to Delta variant-B.1.617.2. We have also identified three escape variants in the S gene at codon position 19 (T19I/R), 484 (E484A/Q), and 681 (P681R/H) during the fourth and fifth waves in India. Based on the phylogenetic diversification studies and similar changes in other lineages, our analysis revealed indications of convergent evolution as the virus adjusts to the shifting immunological profile of its human host. To the best of our knowledge, this study is an approach to comprehensively map the circulating SARS-CoV-2 strains from Western Uttar Pradesh using an integrated approach of whole genome sequencing and phylogenetic analysis. These findings will be extremely valuable in developing a structured approach toward pandemic preparedness and evidence-based intervention plans in the future.
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Affiliation(s)
- Gauri Misra
- Molecular Diagnostics and COVID-19 Kit Testing Laboratory, National Institute of Biologicals (Ministry of Health and Family Welfare), A-32, Sector-62, Institutional Area, Noida, UP, 201309, India.
| | - Ashrat Manzoor
- Molecular Diagnostics and COVID-19 Kit Testing Laboratory, National Institute of Biologicals (Ministry of Health and Family Welfare), A-32, Sector-62, Institutional Area, Noida, UP, 201309, India
| | - Meenu Chopra
- National Dairy Research Institute, Karnal, Haryana, India
| | - Archana Upadhyay
- Molecular Diagnostics and COVID-19 Kit Testing Laboratory, National Institute of Biologicals (Ministry of Health and Family Welfare), A-32, Sector-62, Institutional Area, Noida, UP, 201309, India
| | - Amit Katiyar
- Bioinformatics Facility, Centralized Core Research Facility, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Brij Bhushan
- Molecular Diagnostics and COVID-19 Kit Testing Laboratory, National Institute of Biologicals (Ministry of Health and Family Welfare), A-32, Sector-62, Institutional Area, Noida, UP, 201309, India
| | - Anup Anvikar
- Molecular Diagnostics and COVID-19 Kit Testing Laboratory, National Institute of Biologicals (Ministry of Health and Family Welfare), A-32, Sector-62, Institutional Area, Noida, UP, 201309, India
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Rothstein AP, Jesser KJ, Feistel DJ, Konstantinidis KT, Trueba G, Levy K. Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2023; 113:105476. [PMID: 37392822 PMCID: PMC10599324 DOI: 10.1016/j.meegid.2023.105476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023]
Abstract
Human movement may be an important driver of transmission dynamics for enteric pathogens but has largely been underappreciated except for international 'travelers' diarrhea or cholera. Phylodynamic methods, which combine genomic and epidemiological data, are used to examine rates and dynamics of disease matching underlying evolutionary history and biogeographic distributions, but these methods often are not applied to enteric bacterial pathogens. We used phylodynamics to explore the phylogeographic and evolutionary patterns of diarrheagenic E. coli in northern Ecuador to investigate the role of human travel in the geographic distribution of strains across the country. Using whole genome sequences of diarrheagenic E. coli isolates, we built a core genome phylogeny, reconstructed discrete ancestral states across urban and rural sites, and estimated migration rates between E. coli populations. We found minimal structuring based on site locations, urban vs. rural locality, pathotype, or clinical status. Ancestral states of phylogenomic nodes and tips were inferred to have 51% urban ancestry and 49% rural ancestry. Lack of structuring by location or pathotype E. coli isolates imply highly connected communities and extensive sharing of genomic characteristics across isolates. Using an approximate structured coalescent model, we estimated rates of migration among circulating isolates were 6.7 times larger for urban towards rural populations compared to rural towards urban populations. This suggests increased inferred migration rates of diarrheagenic E. coli from urban populations towards rural populations. Our results indicate that investments in water and sanitation prevention in urban areas could limit the spread of enteric bacterial pathogens among rural populations.
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Affiliation(s)
- Andrew P. Rothstein
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
| | - Kelsey J. Jesser
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
| | - Dorian J. Feistel
- School of a Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Konstantinos T. Konstantinidis
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- School of a Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Gabriel Trueba
- Instituto de Microbiología, Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Quito, Pichincha, Ecuador
| | - Karen Levy
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA
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Martínez-Martínez FJ, Massinga AJ, De Jesus Á, Ernesto RM, Cano-Jiménez P, Chiner-Oms Á, Gómez-Navarro I, Guillot-Fernández M, Guinovart C, Sitoe A, Vubil D, Bila R, Gujamo R, Enosse S, Jiménez-Serrano S, Torres-Puente M, Comas I, Mandomando I, López MG, Mayor A. Tracking SARS-CoV-2 introductions in Mozambique using pandemic-scale phylogenies: a retrospective observational study. Lancet Glob Health 2023; 11:e933-e941. [PMID: 37202028 DOI: 10.1016/s2214-109x(23)00169-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/09/2023] [Accepted: 03/23/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND From the start of the SARS-CoV-2 outbreak, global sequencing efforts have generated an unprecedented amount of genomic data. Nonetheless, unequal sampling between high-income and low-income countries hinders the implementation of genomic surveillance systems at the global and local level. Filling the knowledge gaps of genomic information and understanding pandemic dynamics in low-income countries is essential for public health decision making and to prepare for future pandemics. In this context, we aimed to discover the timing and origin of SARS-CoV-2 variant introductions in Mozambique, taking advantage of pandemic-scale phylogenies. METHODS We did a retrospective, observational study in southern Mozambique. Patients from Manhiça presenting with respiratory symptoms were recruited, and those enrolled in clinical trials were excluded. Data were included from three sources: (1) a prospective hospital-based surveillance study (MozCOVID), recruiting patients living in Manhiça, attending the Manhiça district hospital, and fulfilling the criteria of suspected COVID-19 case according to WHO; (2) symptomatic and asymptomatic individuals with SARS-CoV-2 infection recruited by the National Surveillance system; and (3) sequences from SARS-CoV-2-infected Mozambican cases deposited on the Global Initiative on Sharing Avian Influenza Data database. Positive samples amenable for sequencing were analysed. We used Ultrafast Sample placement on Existing tRees to understand the dynamics of beta and delta waves, using available genomic data. This tool can reconstruct a phylogeny with millions of sequences by efficient sample placement in a tree. We reconstructed a phylogeny (~7·6 million sequences) adding new and publicly available beta and delta sequences. FINDINGS A total of 5793 patients were recruited between Nov 1, 2020, and Aug 31, 2021. During this time, 133 328 COVID-19 cases were reported in Mozambique. 280 good quality new SARS-CoV-2 sequences were obtained after the inclusion criteria were applied and an additional 652 beta (B.1.351) and delta (B.1.617.2) public sequences were included from Mozambique. We evaluated 373 beta and 559 delta sequences. We identified 187 beta introductions (including 295 sequences), divided in 42 transmission groups and 145 unique introductions, mostly from South Africa, between August, 2020 and July, 2021. For delta, we identified 220 introductions (including 494 sequences), with 49 transmission groups and 171 unique introductions, mostly from the UK, India, and South Africa, between April and November, 2021. INTERPRETATION The timing and origin of introductions suggests that movement restrictions effectively avoided introductions from non-African countries, but not from surrounding countries. Our results raise questions about the imbalance between the consequences of restrictions and health benefits. This new understanding of pandemic dynamics in Mozambique can be used to inform public health interventions to control the spread of new variants. FUNDING European and Developing Countries Clinical Trials, European Research Council, Bill & Melinda Gates Foundation, and Agència de Gestió d'Ajuts Universitaris i de Recerca.
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Affiliation(s)
- Francisco José Martínez-Martínez
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | | | - Áuria De Jesus
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Rita M Ernesto
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Pablo Cano-Jiménez
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | - Álvaro Chiner-Oms
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | - Inmaculada Gómez-Navarro
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | - Marina Guillot-Fernández
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | | | - António Sitoe
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Delfino Vubil
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Rubão Bila
- Hospital Distrital da Manhiça, Marracuene, Mozambique
| | | | - Sónia Enosse
- Instituto Nacional de Saúde, Marracuene, Mozambique
| | - Santiago Jiménez-Serrano
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | - Manuela Torres-Puente
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | - Iñaki Comas
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain; Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Inácio Mandomando
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique; Instituto Nacional de Saúde, Marracuene, Mozambique
| | - Mariana G López
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia, Spain.
| | - Alfredo Mayor
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique; ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Department of Physiologic Sciences, Faculty of Medicine, Universidade Eduardo Mondlane, Maputo, Mozambique
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15
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Salichos L, Warrell J, Cevasco H, Chung A, Gerstein M. Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for more efficient mitigation strategies. Sci Rep 2023; 13:8470. [PMID: 37231011 DOI: 10.1038/s41598-023-34959-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
For the COVID-19 pandemic, viral transmission has been documented in many historical and geographical contexts. Nevertheless, few studies have explicitly modeled the spatiotemporal flow based on genetic sequences, to develop mitigation strategies. Additionally, thousands of SARS-CoV-2 genomes have been sequenced with associated records, potentially providing a rich source for such spatiotemporal analysis, an unprecedented amount during a single outbreak. Here, in a case study of seven states, we model the first wave of the outbreak by determining regional connectivity from phylogenetic sequence information (i.e. "genetic connectivity"), in addition to traditional epidemiologic and demographic parameters. Our study shows nearly all of the initial outbreak can be traced to a few lineages, rather than disconnected outbreaks, indicative of a mostly continuous initial viral flow. While the geographic distance from hotspots is initially important in the modeling, genetic connectivity becomes increasingly significant later in the first wave. Moreover, our model predicts that isolated local strategies (e.g. relying on herd immunity) can negatively impact neighboring regions, suggesting more efficient mitigation is possible with unified, cross-border interventions. Finally, our results suggest that a few targeted interventions based on connectivity can have an effect similar to that of an overall lockdown. They also suggest that while successful lockdowns are very effective in mitigating an outbreak, less disciplined lockdowns quickly decrease in effectiveness. Our study provides a framework for combining phylodynamic and computational methods to identify targeted interventions.
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Affiliation(s)
- Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Biological and Chemical Sciences, New York Institute of Technology, Manhattan, NY, 10023, USA.
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Hannah Cevasco
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Alvin Chung
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA.
- Center for Biomedical Data Science, Yale University, New Haven, CT, 06520, USA.
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA.
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16
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Scotch M, Lauer K, Wieben ED, Cherukuri Y, Cunningham JM, Klee EW, Harrington JJ, Lau JS, McDonough SJ, Mutawe M, O’Horo JC, Rentmeester CE, Schlicher NR, White VT, Schneider SK, Vedell PT, Wang X, Yao JD, Pritt BS, Norgan AP. Genomic epidemiology reveals the dominance of Hennepin County in transmission of SARS-CoV-2 in Minnesota from 2020-2022. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2022.07.24.22277978. [PMID: 35923324 PMCID: PMC9347287 DOI: 10.1101/2022.07.24.22277978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
SARS-CoV-2 has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread, and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020-2022 and use Bayesian phylodynamics to describe county and regional spread in Minnesota. The earliest introduction into Minnesota was to Hennepin County from a domestic source around January 22, 2020; six weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota, and eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International (MSP) airport) totaling 45 (out of 107) over the two-year period. Southern Minnesota counties were most common for domestic introductions with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations (n=772) over the two-year period. We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases or major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have impact on virus diversity across each individual county.
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Affiliation(s)
- Matthew Scotch
- Research Affiliate, Mayo Clinic Arizona, Phoenix, AZ USA
- Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, AZ USA
- College of Health Solutions, Arizona State University, Phoenix, Arizona USA
| | - Kimberly Lauer
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, MN, USA
| | - Eric D. Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic Rochester, Rochester, MN, USA
| | | | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric W Klee
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, MN, USA
- Center for Individualized Medicine, Rochester, MN, USA
| | | | - Julie S Lau
- Center for Individualized Medicine, Rochester, MN, USA
| | | | - Mark Mutawe
- Center for Individualized Medicine, Rochester, MN, USA
| | - John C. O’Horo
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Chad E. Rentmeester
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Saint Mary’s University of Minnesota, Winona, MN, USA
| | - Nicole R Schlicher
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Valerie T White
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan K Schneider
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter T Vedell
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, MN, USA
| | - Xiong Wang
- Minnesota Department of Health, St. Paul, MN, USA
| | - Joseph D Yao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Bobbi S Pritt
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew P Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
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17
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Pillay S, San JE, Tshiabuila D, Naidoo Y, Pillay Y, Maharaj A, Anyaneji UJ, Wilkinson E, Tegally H, Lessells RJ, Baxter C, de Oliveira T, Giandhari J. Evaluation of miniaturized Illumina DNA preparation protocols for SARS-CoV-2 whole genome sequencing. PLoS One 2023; 18:e0283219. [PMID: 37099540 PMCID: PMC10132692 DOI: 10.1371/journal.pone.0283219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/03/2023] [Indexed: 04/27/2023] Open
Abstract
The global pandemic caused by SARS-CoV-2 has increased the demand for scalable sequencing and diagnostic methods, especially for genomic surveillance. Although next-generation sequencing has enabled large-scale genomic surveillance, the ability to sequence SARS-CoV-2 in some settings has been limited by the cost of sequencing kits and the time-consuming preparations of sequencing libraries. We compared the sequencing outcomes, cost and turn-around times obtained using the standard Illumina DNA Prep kit protocol to three modified protocols with fewer clean-up steps and different reagent volumes (full volume, half volume, one-tenth volume). We processed a single run of 47 samples under each protocol and compared the yield and mean sequence coverage. The sequencing success rate and quality for the four different reactions were as follows: the full reaction was 98.2%, the one-tenth reaction was 98.0%, the full rapid reaction was 97.5% and the half-reaction, was 97.1%. As a result, uniformity of sequence quality indicated that libraries were not affected by the change in protocol. The cost of sequencing was reduced approximately seven-fold and the time taken to prepare the library was reduced from 6.5 hours to 3 hours. The sequencing results obtained using the miniaturised volumes showed comparability to the results obtained using full volumes as described by the manufacturer. The adaptation of the protocol represents a lower-cost, streamlined approach for SARS-CoV-2 sequencing, which can be used to produce genomic data quickly and more affordably, especially in resource-constrained settings.
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Affiliation(s)
- Sureshnee Pillay
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - James Emmanuel San
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Derek Tshiabuila
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Yeshnee Naidoo
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Yusasha Pillay
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Akhil Maharaj
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Ugochukwu J. Anyaneji
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Eduan Wilkinson
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Richard J. Lessells
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Center for AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
| | - Cheryl Baxter
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- Center for AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- Center for AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
- Department of Global Health, University of Washington, Seattle, WA, United States of America
| | - Jennifer Giandhari
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
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18
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De Maio N, Kalaghatgi P, Turakhia Y, Corbett-Detig R, Minh BQ, Goldman N. Maximum likelihood pandemic-scale phylogenetics. Nat Genet 2023; 55:746-752. [PMID: 37038003 PMCID: PMC10181937 DOI: 10.1038/s41588-023-01368-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/07/2023] [Indexed: 04/12/2023]
Abstract
Phylogenetics has a crucial role in genomic epidemiology. Enabled by unparalleled volumes of genome sequence data generated to study and help contain the COVID-19 pandemic, phylogenetic analyses of SARS-CoV-2 genomes have shed light on the virus's origins, spread, and the emergence and reproductive success of new variants. However, most phylogenetic approaches, including maximum likelihood and Bayesian methods, cannot scale to the size of the datasets from the current pandemic. We present 'MAximum Parsimonious Likelihood Estimation' (MAPLE), an approach for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. MAPLE infers SARS-CoV-2 phylogenies more accurately than existing maximum likelihood approaches while running up to thousands of times faster, and requiring at least 100 times less memory on large datasets. This extends the reach of genomic epidemiology, allowing the continued use of accurate phylogenetic, phylogeographic and phylodynamic analyses on datasets of millions of genomes.
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Affiliation(s)
- Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
| | | | - Yatish Turakhia
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, USA
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Bui Quang Minh
- School of Computing, College of Engineering, Computing and Cybernetics, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Nick Goldman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
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19
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Jeong S, Kim JS, Lee SK, Cho EJ, Hyun J, Song W, Kim HS. Tracking the Genomic Evolution of SARS-CoV-2 for 29 Months in South Korea. Viruses 2023; 15:873. [PMID: 37112852 PMCID: PMC10142693 DOI: 10.3390/v15040873] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/29/2023] Open
Abstract
The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued, with the persistent emergence of variants of concern (VOCs). Therefore, this study aimed to track the genomic evolution of SARS-CoV-2 strains by sequencing the spike protein for 29 months, which accounted for the majority of the COVID-19 pandemic period. A total of 109 swabs from patients with confirmed coronavirus disease 2019 (COVID-19) infection were randomly collected between March 2020 and July 2022. After genomic sequencing, we analyzed the naming systems and phylogenetic trees. Five surge peaks of COVID-19 cases have been reported in South Korea, resulting in 14,000,000 cumulative confirmed cases and 17,000 deaths. Among the sequenced samples, 34 wild-type strains and 75 VOCs, including 4 Alpha, 33 Delta, 2 Epsilon, and 36 Omicron VOCs, were identified. Omicron strains were comprised of 8 BA.1.1 (21 K), 27 BA.2 (21 L), and 1 BA.2.12.1 (22C). Phylogenetic analysis of the identified isolates and representative sequences of SARS-CoV-2 strains revealed clusters that presented the WHO VOCs. Specific or unique mutations for each VOC waxed and waned according to the variant waves. Our findings allowed recognition of the overall trends of SARS-CoV-2 isolates, which implicated replication advantage, immune evasion, and disease management.
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Affiliation(s)
- Seri Jeong
- Department of Laboratory Medicine, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Jae-Seok Kim
- Department of Laboratory Medicine, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Su Kyung Lee
- Department of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Eun-Jung Cho
- Department of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Jungwon Hyun
- Department of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Wonkeun Song
- Department of Laboratory Medicine, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Hyun Soo Kim
- Department of Laboratory Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
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20
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Nawrocki EP. Faster SARS-CoV-2 sequence validation and annotation for GenBank using VADR. NAR Genom Bioinform 2023; 5:lqad002. [PMID: 36685728 PMCID: PMC9853093 DOI: 10.1093/nargab/lqad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/28/2022] [Accepted: 01/03/2023] [Indexed: 01/22/2023] Open
Abstract
In 2020 and 2021, >1.5 million SARS-CoV-2 sequences were submitted to GenBank. The initial version (v1.0) of the VADR (Viral Annotation DefineR) software package that GenBank uses to automatically validate and annotate incoming viral sequences is too slow and memory intensive to process many thousands of SARS-CoV-2 sequences in a reasonable amount of time. Additionally, long stretches of ambiguous N nucleotides, which are common in many SARS-CoV-2 sequences, prevent VADR from accurate validation and annotation. VADR has been updated to more accurately and rapidly annotate SARS-CoV-2 sequences. Stretches of consecutive Ns are now identified and temporarily replaced with expected nucleotides to facilitate processing, and the slowest steps have been overhauled using blastn and glsearch, increasing speed, reducing the memory requirement from 64Gb to 2Gb per thread, and allowing simple, coarse-grained parallelization on multiple processors per host. VADR is now nearly 1000 times faster than it was in early 2020 SARS-CoV-2 sequence processing. It has been used to screen and annotate more than 1.5 million SARS-CoV-2 sequences since June 2020, and it is now efficient enough to cope with the current rate of hundreds of thousands of submitted sequences per month.
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Affiliation(s)
- Eric P Nawrocki
- National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
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21
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Taylor MK, Williams EP, Xue Y, Jenjaroenpun P, Wongsurawat T, Smith AP, Smith AM, Parvathareddy J, Kong Y, Vogel P, Cao X, Reichard W, Spruill-Harrell B, Samarasinghe AE, Nookaew I, Fitzpatrick EA, Smith MD, Aranha M, Smith JC, Jonsson CB. Dissecting Phenotype from Genotype with Clinical Isolates of SARS-CoV-2 First Wave Variants. Viruses 2023; 15:611. [PMID: 36992320 PMCID: PMC10059853 DOI: 10.3390/v15030611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 02/25/2023] Open
Abstract
The emergence and availability of closely related clinical isolates of SARS-CoV-2 offers a unique opportunity to identify novel nonsynonymous mutations that may impact phenotype. Global sequencing efforts show that SARS-CoV-2 variants have emerged and then been replaced since the beginning of the pandemic, yet we have limited information regarding the breadth of variant-specific host responses. Using primary cell cultures and the K18-hACE2 mouse, we investigated the replication, innate immune response, and pathology of closely related, clinical variants circulating during the first wave of the pandemic. Mathematical modeling of the lung viral replication of four clinical isolates showed a dichotomy between two B.1. isolates with significantly faster and slower infected cell clearance rates, respectively. While isolates induced several common immune host responses to infection, one B.1 isolate was unique in the promotion of eosinophil-associated proteins IL-5 and CCL11. Moreover, its mortality rate was significantly slower. Lung microscopic histopathology suggested further phenotypic divergence among the five isolates showing three distinct sets of phenotypes: (i) consolidation, alveolar hemorrhage, and inflammation, (ii) interstitial inflammation/septal thickening and peribronchiolar/perivascular lymphoid cells, and (iii) consolidation, alveolar involvement, and endothelial hypertrophy/margination. Together these findings show divergence in the phenotypic outcomes of these clinical isolates and reveal the potential importance of nonsynonymous mutations in nsp2 and ORF8.
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Affiliation(s)
- Mariah K. Taylor
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Evan P. Williams
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Yi Xue
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Piroon Jenjaroenpun
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Thidathip Wongsurawat
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Amanda P. Smith
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
| | - Amber M. Smith
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Jyothi Parvathareddy
- Regional Biocontainment Laboratory, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Ying Kong
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Peter Vogel
- Veterinary Pathology Core Laboratory, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Xueyuan Cao
- Department of Health Promotion and Disease Prevention, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Walter Reichard
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Briana Spruill-Harrell
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Amali E. Samarasinghe
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
| | - Intawat Nookaew
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Elizabeth A. Fitzpatrick
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Micholas Dean Smith
- Center for Molecular Biophysics, University of Tennessee-Oak Ridge National Laboratory, Knoxville, TN 37996, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee- Knoxville, Knoxville, TN 37996, USA
| | - Michelle Aranha
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee- Knoxville, Knoxville, TN 37996, USA
| | - Jeremy C. Smith
- Center for Molecular Biophysics, University of Tennessee-Oak Ridge National Laboratory, Knoxville, TN 37996, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee- Knoxville, Knoxville, TN 37996, USA
| | - Colleen B. Jonsson
- Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Institute for the Study of Host-Pathogen Systems, University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Regional Biocontainment Laboratory, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
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22
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Rapid System to Detect Variants of SARS-CoV-2 in Nasopharyngeal Swabs. Viruses 2023; 15:v15020353. [PMID: 36851567 PMCID: PMC9966895 DOI: 10.3390/v15020353] [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: 09/24/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/28/2023] Open
Abstract
Currently, the reference method for identifying the presence of variants of SARS-CoV-2 is whole genome sequencing. Although it is less expensive than in the past, it is still time-consuming, and interpreting the results is difficult, requiring staff with specific skills who are not always available in diagnostic laboratories. The test presented in this study aimed to detect, using traditional real-time PCR, the presence of the main variants described for the spike protein of the SARS-CoV-2 genome. The primers and probes were designed to detect the main deletions that characterize the different variants. The amplification targets were deletions in the S gene: 25-27, 69-70, 241-243, and 157-158. In the ORF1a gene, the deletion 3675-3677 was chosen. Some of these mutations can be considered specific variants, while others can be identified by the simultaneous presence of one or more deletions. We avoided using point mutations in order to improve the speed of the test. Our test can help clinical and medical microbiologists quickly recognize the presence of variants in biological samples (particularly nasopharyngeal swabs). The test can also be used to identify variants of the virus that could potentially be more diffusive as well as not responsive to the vaccine.
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23
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Assato PA, Clemente LG, Giovanetti M, Ribeiro G, Lima ARJ, Palmieri M, de Moraes LN, Kashima S, Fukumasu H, Nogueira ML, Alcantara LCJ, Nicolodelli AL, Martins AJ, Petry B, Banho CA, Dos Santos Barros CR, Moncau-Gadbem CT, Moretti DB, De La Roque DGL, Marqueze EC, Mattos EC, Silva FEVD, Da Costa FADS, Cacherik G, De Souza Todao Bernardino J, Lesbon JCC, Sacchetto L, De Lima LPO, Caldeira LAV, Martininghi M, Moraes MM, Poleti MD, Cattony Neto PDQ, Cassano RDLRC, Brassaloti RA, Slavov SN, Viala VL, Coutinho LL, Grotto RMT, Neto RM, Covas DT, Sampaio SC, Elias MC, Souza-Neto JA. Retrospective Insights of the COVID-19 Epidemic in the Major Latin American City, São Paulo, Southeastern Brazil. Viruses 2023; 15:327. [PMID: 36851541 PMCID: PMC9965911 DOI: 10.3390/v15020327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/26/2023] Open
Abstract
São Paulo is the financial center of Brazil, with a population of over 12 million, that receives travelers from all over the world for business and tourism. It was the first city in Brazil to report a case of COVID-19 that rapidly spread across the city despite the implementation of the restriction measures. Despite many reports, much is still unknown regarding the genomic diversity and transmission dynamics of this virus in the city of São Paulo. Thus, in this study, we provide a retrospective overview of the COVID-19 epidemic in São Paulo City, Southeastern, Brazil, by generating a total of 9995 near-complete genome sequences from all the city's different macro-regions (North, West, Central, East, South, and Southeast). Our analysis revealed that multiple independent introduction events of different variants (mainly Gamma, Delta, and Omicron) occurred throughout time. Additionally, our estimates of viral movement within the different macro-regions further suggested that the East and the Southeast regions were the largest contributors to the Gamma and Delta viral exchanges to other regions. Meanwhile, the North region had a higher contribution to the dispersion of the Omicron variant. Together, our results reinforce the importance of increasing SARS-CoV-2 genomic monitoring within the city and the country to track the real-time evolution of the virus and to detect earlier any eventual emergency of new variants of concern that could undermine the fight against COVID-19 in Brazil and worldwide.
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Affiliation(s)
- Patricia Akemi Assato
- School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu 18610-034, Brazil
| | - Luan Gaspar Clemente
- Centro de Genômica Funcional da ESALQ, University of São Paulo, Piracicaba 13418-900, Brazil
| | - Marta Giovanetti
- Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21040-360, Brazil
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
- Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, 00128 Rome, Italy
| | | | | | - Melissa Palmieri
- Health Surveillance Coordination, Sao Paulo Municipal Health Department—Coordenadoria de Vigilância em Saúde—Secretaria Municipal de São Paulo, Sao Paulo 05579-000, Brazil
| | | | - Simone Kashima
- Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14051-140, Brazil
| | - Heidge Fukumasu
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga 13635-900, Brazil
| | | | - Luiz Carlos Junior Alcantara
- Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21040-360, Brazil
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Aline Lais Nicolodelli
- Primary Care Coordination/Sao Paulo Municipal Health Department—Coordenadoria de Atenção Básica—Secretaria Municipal de São Paulo, Sao Paulo 05579-000, Brazil
| | | | - Bruna Petry
- Centro de Genômica Funcional da ESALQ, University of São Paulo, Piracicaba 13418-900, Brazil
| | - Cecilia Artico Banho
- Faculdade de Medicina de São José do Rio Preto (FAMERP), São José do Rio Preto 15090-000, Brazil
| | | | | | | | - Debora Glenda Lima De La Roque
- Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14051-140, Brazil
| | | | - Elisangela Chicaroni Mattos
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga 13635-900, Brazil
| | - Fabiana Erica Vilanova Da Silva
- Primary Care Coordination/Sao Paulo Municipal Health Department—Coordenadoria de Atenção Básica—Secretaria Municipal de São Paulo, Sao Paulo 05579-000, Brazil
| | | | - Giselle Cacherik
- Primary Care Coordination/Sao Paulo Municipal Health Department—Coordenadoria de Atenção Básica—Secretaria Municipal de São Paulo, Sao Paulo 05579-000, Brazil
| | | | - Jessika Cristina Chagas Lesbon
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga 13635-900, Brazil
| | - Lívia Sacchetto
- Faculdade de Medicina de São José do Rio Preto (FAMERP), São José do Rio Preto 15090-000, Brazil
| | | | - Luiz Artur Vieira Caldeira
- Health Surveillance Coordination, Sao Paulo Municipal Health Department—Coordenadoria de Vigilância em Saúde—Secretaria Municipal de São Paulo, Sao Paulo 05579-000, Brazil
| | - Maiara Martininghi
- Health Surveillance Coordination, Sao Paulo Municipal Health Department—Coordenadoria de Vigilância em Saúde—Secretaria Municipal de São Paulo, Sao Paulo 05579-000, Brazil
| | - Marília Mazzi Moraes
- Faculdade de Medicina de São José do Rio Preto (FAMERP), São José do Rio Preto 15090-000, Brazil
| | - Mirele Daiana Poleti
- Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga 13635-900, Brazil
| | | | | | | | - Svetoslav Nanev Slavov
- Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14051-140, Brazil
| | | | - Luiz Lehmann Coutinho
- Centro de Genômica Funcional da ESALQ, University of São Paulo, Piracicaba 13418-900, Brazil
| | - Rejane Maria Tommasini Grotto
- School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu 18610-034, Brazil
- Genomic Surveillance Network, São Paulo State University (UNESP), Sao Paulo 01049-010, Brazil
| | | | - Dimas Tadeu Covas
- Butantan Institute, Sao Paulo 05508-040, Brazil
- Ribeirão Preto Medical School, Blood Center of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14051-140, Brazil
| | | | | | - Jayme A. Souza-Neto
- School of Agricultural Sciences, São Paulo State University (UNESP), Botucatu 18610-034, Brazil
- Genomic Surveillance Network, São Paulo State University (UNESP), Sao Paulo 01049-010, Brazil
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24
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Rocchigiani AM, Ferretti L, Ledda A, Di Nardo A, Floris M, Bonelli P, Loi F, Idda ML, Angioi PP, Zinellu S, Fiori MS, Bechere R, Capitta P, Coccollone A, Coradduzza E, Dettori MA, Fattaccio MC, Gallisai E, Maestrale C, Manunta D, Pedditzi A, Piredda I, Palmas B, Salza S, Sechi AM, Tanda B, Madrau MP, Sanna ML, Cherchi S, Ponti N, Masala G, Sirica R, Evangelista E, Oggiano A, Puggioni G, Ligios C, Dei Giudici S. Origin, Genetic Variation and Molecular Epidemiology of SARS-CoV-2 Strains Circulating in Sardinia (Italy) during the First and Second COVID-19 Epidemic Waves. Viruses 2023; 15:277. [PMID: 36851491 PMCID: PMC9961045 DOI: 10.3390/v15020277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
Understanding how geography and human mobility shape the patterns and spread of infectious diseases such as COVID-19 is key to control future epidemics. An interesting example is provided by the second wave of the COVID-19 epidemic in Europe, which was facilitated by the intense movement of tourists around the Mediterranean coast in summer 2020. The Italian island of Sardinia is a major tourist destination and is widely believed to be the origin of the second Italian wave. In this study, we characterize the genetic variation among SARS-CoV-2 strains circulating in northern Sardinia during the first and second Italian waves using both Illumina and Oxford Nanopore Technologies Next Generation Sequencing methods. Most viruses were placed into a single clade, implying that despite substantial virus inflow, most outbreaks did not spread widely. The second epidemic wave on the island was actually driven by local transmission of a single B.1.177 subclade. Phylogeographic analyses further suggest that those viral strains circulating on the island were not a relevant source for the second epidemic wave in Italy. This result, however, does not rule out the possibility of intense mixing and transmission of the virus among tourists as a major contributor to the second Italian wave.
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Affiliation(s)
| | - Luca Ferretti
- Pandemic Sciences Institute and Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department for Medicine, University of Oxford, Oxford OX1 2JD, UK
| | - Alice Ledda
- UK Health Security Agency, Colindale, London NW9 5EQ, UK
| | | | - Matteo Floris
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Piero Bonelli
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Federica Loi
- Osservatorio Epidemiologico Veterinario Regionale, Istituto Zooprofilattico Sperimentale della Sardegna, 09125 Cagliari, Italy
| | - Maria Laura Idda
- Institute for Genetic and Biomedical Research (IRGB), National Research Council (CNR), 07100 Sassari, Italy
| | - Pier Paolo Angioi
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Susanna Zinellu
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | | | - Roberto Bechere
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Paola Capitta
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | | | | | | | | | - Elena Gallisai
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Caterina Maestrale
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Daniela Manunta
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Aureliana Pedditzi
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Ivana Piredda
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Bruna Palmas
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Sara Salza
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Anna Maria Sechi
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Barbara Tanda
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Maria Paola Madrau
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Maria Luisa Sanna
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Simonetta Cherchi
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Nicoletta Ponti
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Giovanna Masala
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Roberto Sirica
- Ames Polydiagnostic Group Center SRL, 80013 Napoli, Italy
| | | | - Annalisa Oggiano
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | | | - Ciriaco Ligios
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
| | - Silvia Dei Giudici
- Istituto Zooprofilattico Sperimentale della Sardegna, 07100 Sassari, Italy
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Gu C, Ye Y, Zhang G, Yu M, Bai M, Ma Q, Shao W, Zhao Y, Hong S. The chloroplast genome of Cuphea hookeriana Walp. (Lythraceae), a Mexico ornamental plant. Mitochondrial DNA B Resour 2023; 8:522-526. [PMID: 37124996 PMCID: PMC10132219 DOI: 10.1080/23802359.2023.2203783] [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: 05/02/2023] Open
Abstract
Cuphea hookeriana Walp. is an ornamental plant belonging to the Lythraceae. In this study, we reported the complete chloroplast (cp) genome sequence here and analyzed the phylogenetic relationship among Lythraceae plants. The length of the cp genome was 158,999 bp, including a large single-copy (LSC, 89,311 bp) region and a small single-copy (SSC, 18,436 bp) region separated by a pair of inverted repeats (IRs, 25,626 bp). There were 72 unique protein-coding genes (PCGs), 30 transfer RNA (tRNA) genes, and four ribosomal RNA (rRNA) genes in the cp genome of C. hookeriana. A total of 223 simple sequence repeats (SSRs) and 34 long repeat sequences were identified. Phylogenetic analyses using maximum-likelihood (ML) revealed that C. hookeriana was close to C. hyssopifolia. In addition, the two Cuphea species were the sister group of Woodfordia fruticosa.
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Affiliation(s)
- Cuihua Gu
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
| | - Yacheng Ye
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
| | - Guozhe Zhang
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
| | - Mengxin Yu
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
| | - Mingzhu Bai
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
| | - Qingqing Ma
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
| | - Weili Shao
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
- Weili Shao School of Landscape and Architecture, Zhejiang A&F University, Hangzhou311300, China
| | - Yu Zhao
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
- Yu Zhao
| | - Sidan Hong
- College of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, Zhejiang A&F University, Hangzhou, China
- Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Zhejiang A&F University, Hangzhou, China
- CONTACT Sidan Hong
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Paredes MI, Perofsky AC, Frisbie L, Moncla LH, Roychoudhury P, Xie H, Mohamed Bakhash SA, Kong K, Arnould I, Nguyen TV, Wendm ST, Hajian P, Ellis S, Mathias PC, Greninger AL, Starita LM, Frazar CD, Ryke E, Zhong W, Gamboa L, Threlkeld M, Lee J, Stone J, McDermot E, Truong M, Shendure J, Oltean HN, Viboud C, Chu H, Müller NF, Bedford T. Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.12.15.22283536. [PMID: 36561171 PMCID: PMC9774227 DOI: 10.1101/2022.12.15.22283536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.
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Affiliation(s)
- Miguel I. Paredes
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Amanda C. Perofsky
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Lauren Frisbie
- Washington State Department of Health, Shoreline, WA USA
| | - Louise H. Moncla
- The University of Pennsylvania, Department of Pathobiology, Philadelphia, PA
| | - Pavitra Roychoudhury
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Hong Xie
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - Kevin Kong
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Isabel Arnould
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Tien V. Nguyen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Seffir T. Wendm
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Pooneh Hajian
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Sean Ellis
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Patrick C. Mathias
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Alexander L. Greninger
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Lea M. Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Chris D. Frazar
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Erica Ryke
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Weizhi Zhong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Luis Gamboa
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Machiko Threlkeld
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Jeremy Stone
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Evan McDermot
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Melissa Truong
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Helen Chu
- Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA
| | - Nicola F. Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Trevor Bedford
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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Alisoltani A, Jaroszewski L, Godzik A, Iranzadeh A, Simons LM, Dean TJ, Lorenzo-Redondo R, Hultquist JF, Ozer EA. ViralVar: A Web Tool for Multilevel Visualization of SARS-CoV-2 Genomes. Viruses 2022; 14:v14122714. [PMID: 36560718 PMCID: PMC9781208 DOI: 10.3390/v14122714] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/14/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
The unprecedented growth of publicly available SARS-CoV-2 genome sequence data has increased the demand for effective and accessible SARS-CoV-2 data analysis and visualization tools. The majority of the currently available tools either require computational expertise to deploy them or limit user input to preselected subsets of SARS-CoV-2 genomes. To address these limitations, we developed ViralVar, a publicly available, point-and-click webtool that gives users the freedom to investigate and visualize user-selected subsets of SARS-CoV-2 genomes obtained from the GISAID public database. ViralVar has two primary features that enable: (1) the visualization of the spatiotemporal dynamics of SARS-CoV-2 lineages and (2) a structural/functional analysis of genomic mutations. As proof-of-principle, ViralVar was used to explore the evolution of the SARS-CoV-2 pandemic in the USA in pediatric, adult, and elderly populations (n > 1.7 million genomes). Whereas the spatiotemporal dynamics of the variants did not differ between these age groups, several USA-specific sublineages arose relative to the rest of the world. Our development and utilization of ViralVar to provide insights on the evolution of SARS-CoV-2 in the USA demonstrates the importance of developing accessible tools to facilitate and accelerate the large-scale surveillance of circulating pathogens.
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Affiliation(s)
- Arghavan Alisoltani
- Department of Microbiology-Immunology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Department of Medicine, Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Correspondence: (A.A.); (E.A.O.)
| | - Lukasz Jaroszewski
- Biosciences Division, School of Medicine, University of California Riverside, Riverside, CA 92507, USA
| | - Adam Godzik
- Biosciences Division, School of Medicine, University of California Riverside, Riverside, CA 92507, USA
| | - Arash Iranzadeh
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Lacy M. Simons
- Department of Medicine, Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Taylor J. Dean
- Department of Medicine, Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Ramon Lorenzo-Redondo
- Department of Medicine, Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Judd F. Hultquist
- Department of Medicine, Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Egon A. Ozer
- Department of Medicine, Division of Infectious Diseases, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Correspondence: (A.A.); (E.A.O.)
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28
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Evaluation of co-circulating pathogens and microbiome from COVID-19 infections. PLoS One 2022; 17:e0278543. [PMID: 36455065 PMCID: PMC9714956 DOI: 10.1371/journal.pone.0278543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 11/18/2022] [Indexed: 12/05/2022] Open
Abstract
Co-infections or secondary infections with SARS-CoV-2 have the potential to affect disease severity and morbidity. Additionally, the potential influence of the nasal microbiome on COVID-19 illness is not well understood. In this study, we analyzed 203 residual samples, originally submitted for SARS-CoV-2 testing, for the presence of viral, bacterial, and fungal pathogens and non-pathogens using a comprehensive microarray technology, the Lawrence Livermore Microbial Detection Array (LLMDA). Eighty-seven percent of the samples were nasopharyngeal samples, and 23% of the samples were oral, nasal and oral pharyngeal swabs. We conducted bioinformatics analyses to examine differences in microbial populations of these samples, as a proxy for the nasal and oral microbiome, from SARS-CoV-2 positive and negative specimens. We found 91% concordance with the LLMDA relative to a diagnostic RT-qPCR assay for detection of SARS-CoV-2. Sixteen percent of all the samples (32/203) revealed the presence of an opportunistic bacterial or frank viral pathogen with the potential to cause co-infections. The two most detected bacteria, Streptococcus pyogenes and Streptococcus pneumoniae, were present in both SARS-CoV-2 positive and negative samples. Human metapneumovirus was the most prevalent viral pathogen in the SARS-CoV-2 negative samples. Sequence analysis of 16S rRNA was also conducted to evaluate bacterial diversity and confirm LLMDA results.
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29
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Tazerji SS, Nardini R, Safdar M, Shehata AA, Duarte PM. An Overview of Anthropogenic Actions as Drivers for Emerging and Re-Emerging Zoonotic Diseases. Pathogens 2022; 11:1376. [PMID: 36422627 PMCID: PMC9692567 DOI: 10.3390/pathogens11111376] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/05/2022] [Accepted: 11/15/2022] [Indexed: 08/05/2023] Open
Abstract
Population growth and industrialization have led to a race for greater food and supply productivity. As a result, the occupation and population of forest areas, contact with wildlife and their respective parasites and vectors, the trafficking and consumption of wildlife, the pollution of water sources, and the accumulation of waste occur more frequently. Concurrently, the agricultural and livestock production for human consumption has accelerated, often in a disorderly way, leading to the deforestation of areas that are essential for the planet's climatic and ecological balance. The effects of human actions on other ecosystems such as the marine ecosystem cause equally serious damage, such as the pollution of this habitat, and the reduction of the supply of fish and other animals, causing the coastal population to move to the continent. The sum of these factors leads to an increase in the demands such as housing, basic sanitation, and medical assistance, making these populations underserved and vulnerable to the effects of global warming and to the emergence of emerging and re-emerging diseases. In this article, we discuss the anthropic actions such as climate changes, urbanization, deforestation, the trafficking and eating of wild animals, as well as unsustainable agricultural intensification which are drivers for emerging and re-emerging of zoonotic pathogens such as viral (Ebola virus, hantaviruses, Hendravirus, Nipah virus, rabies, and severe acute respiratory syndrome coronavirus disease-2), bacterial (leptospirosis, Lyme borreliosis, and tuberculosis), parasitic (leishmaniasis) and fungal pathogens, which pose a substantial threat to the global community. Finally, we shed light on the urgent demand for the implementation of the One Health concept as a collaborative global approach to raise awareness and educate people about the science behind and the battle against zoonotic pathogens to mitigate the threat for both humans and animals.
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Affiliation(s)
- Sina Salajegheh Tazerji
- Department of Clinical Science, Faculty of Veterinary Medicine, Science and Research Branch, Islamic Azad University, Tehran P.O. Box. 1477893855, Iran
- Young Researchers and Elites Club Science and Research Branch, Islamic Azad University; Tehran P.O. Box. 1477893855, Iran
| | - Roberto Nardini
- Istituto Zooprofilattico Sperimentale del Lazio e della Toscana “M. Aleandri”, 00178 Rome, Italy
| | - Muhammad Safdar
- Department of Breeding and Genetics, Cholistan University of Veterinary & Animal Sciences, Bahawalpur 63100, Pakistan
| | - Awad A. Shehata
- Avian and Rabbit Diseases Department, Faculty of Veterinary Medicine, University of Sadat City, Sadat City 32897, Egypt
- Research and Development Section, PerNaturam GmbH, 56290 Gödenroth, Germany
- Prophy-Institute for Applied Prophylaxis, 59159 Bönen, Germany
| | - Phelipe Magalhães Duarte
- Postgraduate Program in Animal Bioscience, Federal Rural University of Pernambuco (UFRPE), Recife, Pernambuco 52171-900, Brazil
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30
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Brazer N, Morris MK, Servellita V, Anglin K, Saldhi P, Garcia-Knight M, Bethancourt S, Sotomayor-Gonzalez A, Wang B, Foresythe A, Nguyen J, Gliwa AS, Pineda-Ramirez J, Sanchez RD, Zhang Y, Ott M, Wadford DA, Andino R, Kelly JD, Hanson C, Chiu C. Neutralizing Immunity Induced Against the Omicron BA.1 and BA.2 Variants in Vaccine Breakthrough Infections. J Infect Dis 2022; 226:1688-1698. [PMID: 36134603 PMCID: PMC9619439 DOI: 10.1093/infdis/jiac384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/10/2022] [Accepted: 09/20/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND As of early 2022, the Omicron variants are the predominant circulating lineages globally. Understanding neutralizing antibody responses against Omicron BA.1 and BA.2 after vaccine breakthrough infections will provide insights into BA.2 infectivity and susceptibility to subsequent reinfection. METHODS Live virus neutralization assays were used to study immunity against Delta and Omicron BA.1 and BA.2 variants in samples from 86 individuals, 24 unvaccinated (27.9%) and 62 vaccinated (72.1%), who were infected with Delta (n = 42, 48.8%) or BA.1 (n = 44, 51.2%). Among the 62 vaccinated individuals, 39 were unboosted (62.9%), whereas 23 were boosted (37.1%). RESULTS In unvaccinated infections, neutralizing antibodies (nAbs) against the three variants were weak or undetectable, except against Delta for Delta-infected individuals. Both Delta and BA.1 breakthrough infections resulted in strong nAb responses against ancestral wild-type and Delta lineages, but moderate nAb responses against BA.1 and BA.2, with similar titers between unboosted and boosted individuals. Antibody titers against BA.2 were generally higher than those against BA.1 in breakthrough infections. CONCLUSIONS These results underscore the decreased immunogenicity of BA.1 compared to BA.2, insufficient neutralizing immunity against BA.2 in unvaccinated individuals, and moderate to strong neutralizing immunity induced against BA.2 in Delta and BA.1 breakthrough infections.
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Affiliation(s)
- Noah Brazer
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Mary Kate Morris
- Viral and Rickettsial Disease Laboratory, California Department of Public Health, Richmond, CA, USA
| | - Venice Servellita
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Khamal Anglin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Prachi Saldhi
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Miguel Garcia-Knight
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Sutana Bethancourt
- Viral and Rickettsial Disease Laboratory, California Department of Public Health, Richmond, CA, USA
| | | | - Baolin Wang
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Abiodun Foresythe
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jenny Nguyen
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Amelia S Gliwa
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jesus Pineda-Ramirez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Ruth Diaz Sanchez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Yueyuan Zhang
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Melanie Ott
- California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
- Innovative Genomics Institute, University of California Berkeley, CA, USA
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Debra A Wadford
- Viral and Rickettsial Disease Laboratory, California Department of Public Health, Richmond, CA, USA
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - J Daniel Kelly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Carl Hanson
- Viral and Rickettsial Disease Laboratory, California Department of Public Health, Richmond, CA, USA
| | - Charles Chiu
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- Innovative Genomics Institute, University of California Berkeley, CA, USA
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
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Hernandez MM, Banu R, Shrestha P, Gonzalez-Reiche AS, van de Guchte A, Farrugia K, Sebra R, Gitman MR, Nowak MD, Cordon-Cardo C, Simon V, van Bakel H, Sordillo EM, Luna N, Ramirez A, Castañeda SA, Patiño LH, Ballesteros N, Muñoz M, Ramírez JD, Paniz-Mondolfi AE. A Robust, Highly Multiplexed Mass Spectrometry Assay to Identify SARS-CoV-2 Variants. Microbiol Spectr 2022; 10:e0173622. [PMID: 36069609 PMCID: PMC9604185 DOI: 10.1128/spectrum.01736-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/12/2022] [Indexed: 12/31/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are characterized by differences in transmissibility and response to therapeutics. Therefore, discriminating among them is vital for surveillance, infection prevention, and patient care. While whole-genome sequencing (WGS) is the "gold standard" for variant identification, molecular variant panels have become increasingly available. Most, however, are based on limited targets and have not undergone comprehensive evaluation. We assessed the diagnostic performance of the highly multiplexed Agena MassARRAY SARS-CoV-2 Variant Panel v3 to identify variants in a diverse set of 391 SARS-CoV-2 clinical RNA specimens collected across our health systems in New York City, USA and Bogotá, Colombia (September 2, 2020 to March 2, 2022). We demonstrated almost perfect levels of interrater agreement between this assay and WGS for 9 of 11 variant calls (κ ≥ 0.856) and 25 of 30 targets (κ ≥ 0.820) tested on the panel. The assay had a high diagnostic sensitivity (≥93.67%) for contemporary variants (e.g., Iota, Alpha, Delta, and Omicron [BA.1 sublineage]) and a high diagnostic specificity for all 11 variants (≥96.15%) and all 30 targets (≥94.34%) tested. Moreover, we highlighted distinct target patterns that could be utilized to identify variants not yet defined on the panel, including the Omicron BA.2 and other sublineages. These findings exemplified the power of highly multiplexed diagnostic panels to accurately call variants and the potential for target result signatures to elucidate new ones. IMPORTANCE The continued circulation of SARS-CoV-2 amid limited surveillance efforts and inconsistent vaccination of populations has resulted in the emergence of variants that uniquely impact public health systems. Thus, in conjunction with functional and clinical studies, continuous detection and identification are quintessential to informing diagnostic and public health measures. Furthermore, until WGS becomes more accessible in the clinical microbiology laboratory, the ideal assay for identifying variants must be robust, provide high resolution, and be adaptable to the evolving nature of viruses like SARS-CoV-2. Here, we highlighted the diagnostic capabilities of a highly multiplexed commercial assay to identify diverse SARS-CoV-2 lineages that circulated from September 2, 2020 to March 2, 2022 among patients seeking care in our health systems. This assay demonstrated variant-specific signatures of nucleotide/amino acid polymorphisms and underscored its utility for the detection of contemporary and emerging SARS-CoV-2 variants of concern.
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Affiliation(s)
- Matthew M. Hernandez
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Radhika Banu
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Paras Shrestha
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ana S. Gonzalez-Reiche
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adriana van de Guchte
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Keith Farrugia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Sema4, a Mount Sinai venture, Stamford, Connecticut, USA
| | - Mount Sinai PSP Study Group
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VARPP), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Melissa R. Gitman
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Michael D. Nowak
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carlos Cordon-Cardo
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Viviana Simon
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VARPP), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Harm van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Emilia Mia Sordillo
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Nicolas Luna
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Angie Ramirez
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Sergio Andres Castañeda
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Luz Helena Patiño
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Nathalia Ballesteros
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Marina Muñoz
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Juan David Ramírez
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Alberto E. Paniz-Mondolfi
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Vázquez-Rodríguez EM, Vázquez-Rodríguez CF, Ortega-Betancourt NV, León-Hernández RC, de León-Escobedo R, Moctezuma-Paz A, Vázquez-Nava F. [Physical inactivity in young people during home confinement due to COVID-19]. REVISTA MEDICA DEL INSTITUTO MEXICANO DEL SEGURO SOCIAL 2022; 60:649-656. [PMID: 36283027 PMCID: PMC10396040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/06/2022] [Indexed: 06/16/2023]
Abstract
Background Physical inactivity is a highly prevalent condition in the world and has been associated with increased susceptibility to develop comorbidities and present with severe respiratory distress syndrome due to COVID-19. Objective To identify the factors present in the family environment and the personal reasons associated with physical inactivity in young people during confinement at home due to COVID-19. Material and methods A cross-sectional study analyzed data from 1,326 young people, ages 15 - 18. To collect information, a questionnaire was constructed using the Google Forms tool and distributed through the WhatsApp application and email to collect the information. Results The prevalence of physical inactivity was 43.4%. Approximately 24.4% were overweight, and 8.8% were obese. Near 43.0% of young people reported living in an environment with a dysfunctional family. The multivariate logistic regression analysis showed that suffering from obesity, does not have space at home, or devices to exercise and present a change in emotions, are related to the physical inactivity of young people during confinement at home due to the COVID-19 pandemic. Conclusions It is important to promote a harmonious environment within the family and the personal development of a healthy lifestyle, during the period of application of the contingency plan due to the presence of a pandemic, in order to maintain a better healthy physical and mental state.
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Affiliation(s)
- Eliza Mireya Vázquez-Rodríguez
- Universidad Veracruzana, Facultad de Medicina, Campus Minatitlán. Veracruz, Veracruz, MéxicoUniversidad VeracruzanaMéxico
| | - Carlos Francisco Vázquez-Rodríguez
- Instituto Mexicano del Seguro Social, Hospital Regional No. 1, Departamento de Medicina Comunitaria. Orizaba, Veracruz, MéxicoInstituto Mexicano del Seguro SocialMéxico
| | - Nancy Virginia Ortega-Betancourt
- Instituto Mexicano del Seguro Social, Hospital Regional No. 1, Departamento de Medicina Comunitaria. Orizaba, Veracruz, MéxicoInstituto Mexicano del Seguro SocialMéxico
| | - Rodrigo César León-Hernández
- Universidad Autónoma de Tamaulipas, Facultad de Enfermería. Tampico, Tamaulipas, MéxicoUniversidad Autónoma de TamaulipasMéxico
| | - Raúl de León-Escobedo
- Universidad Autónoma de Tamaulipas, Facultad de Medicina de Tampico, Departamento de Investigación. Tampico, Tamaulipas, MéxicoUniversidad Autónoma de TamaulipasMéxico
| | - Alejandro Moctezuma-Paz
- Instituto Mexicano del Seguro Social, Coordinación de Investigación en Salud, División de Investigación Clínica. Ciudad de México, MéxicoInstituto Mexicano del Seguro SocialMéxico
| | - Francisco Vázquez-Nava
- Universidad Autónoma de Tamaulipas, Facultad de Medicina de Tampico, Departamento de Investigación. Tampico, Tamaulipas, MéxicoUniversidad Autónoma de TamaulipasMéxico
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Jiménez-Báez MV, Sandoval-Jurado L, Santiago-Espinosa O, Ramírez-Aranda JM, Romero-Figueroa MDS, Montiel-Jarquín A, Prieto-Torres ME. [Epidemiological and clinical characteristics of the COVID-19 epidemic in Mexico: Quintana Roo case]. REVISTA MEDICA DEL INSTITUTO MEXICANO DEL SEGURO SOCIAL 2022; 60:657-665. [PMID: 36283034 PMCID: PMC10395888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 06/30/2022] [Indexed: 06/16/2023]
Abstract
Objective Identify risk factors for severe outcome in Mexican patients with COVID-19 in the population of Quintana Roo. Material and methods Study of 5,916 who met the criteria for suspected cases of COVID-19, 2,531 confirmed by qrTPCR-Sars-CoV-2 tests, of which 1,486 were positive, among which they were classified as hospitalized (severe COVID-19) and outpatients. Multivariate logistic regression analysis was performed to explore the factors associated with the severity of COVID-19 and death as clinical outcomes. The basic reproduction number (R0) was calculated Statistical analysis) Endorsement of the ethics committee 2301. Results SARS-CoV-2 positive patients presented a high prevalence of hypertension 29.1%, diabetes 23.5%, obesity 24%, and 48.5% have at least one chronic disease. There is a high risk of severity for COVID-19 in patients with diabetes OR=3.14, hypertension OR=1.88, obesity OR=1.68, kidney disease OR=3.2, older than 65 years OR=13.6 and men OR=1.7. These factors also increase the risk of death up to 7.7 times. The maximum R0 during the epidemic was 2.4. Conclusion Liver and kidney disease, diabetes, hypertension, and obesity are significantly associated with severe COVID-19 and death.
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Affiliation(s)
- María Valeria Jiménez-Báez
- Instituto Mexicano del Seguro Social, Órgano de Operación Administrativa Desconcentrada Estatal Quintana Roo, Coordinación de Planeación y Enlace Institucional. Cancún, Quintana Roo, MéxicoInstituto Mexicano del Seguro SocialMéxico
| | - Luis Sandoval-Jurado
- Instituto Mexicano del Seguro Social, Órgano de Operación Administrativa Desconcentrada Estatal Quintana Roo, Coordinación de Planeación y Enlace Institucional. Cancún, Quintana Roo, MéxicoInstituto Mexicano del Seguro SocialMéxico
| | - Oscar Santiago-Espinosa
- Instituto Mexicano del Seguro Social, Órgano de Operación Administrativa Desconcentrada Estatal Quintana Roo, Coordinación de Información y Análisis Estratégico. Cancún, Quintana Roo, MéxicoInstituto Mexicano del Seguro SocialMéxico
| | - José Manuel Ramírez-Aranda
- Universidad Autónoma de Nuevo León, Departamento de Medicina Familiar. Monterrey, Nuevo León, MéxicoUniversidad Autónoma de Nuevo LeónMéxico
| | | | - Alvaro Montiel-Jarquín
- Instituto Mexicano del Seguro Social, Hospital de Especialidades de Puebla, Puebla, Puebla, MéxicoInstituto Mexicano del Seguro SocialMéxico
| | - María Erhandi Prieto-Torres
- Instituto Mexicano del Seguro Social, Órgano de Operación Administrativa Desconcentrada Estatal Quintana Roo, Coordinación de Información y Análisis Estratégico. Cancún, Quintana Roo, MéxicoInstituto Mexicano del Seguro SocialMéxico
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Pei S, Kandula S, Cascante Vega J, Yang W, Foerster S, Thompson C, Baumgartner J, Ahuja SD, Blaney K, Varma JK, Long T, Shaman J. Contact tracing reveals community transmission of COVID-19 in New York City. Nat Commun 2022; 13:6307. [PMID: 36274183 PMCID: PMC9588776 DOI: 10.1038/s41467-022-34130-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/14/2022] [Indexed: 12/25/2022] Open
Abstract
Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that locations with higher vaccination coverage and lower numbers of visitors to points-of-interest had reduced within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Jaime Cascante Vega
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Wan Yang
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Steffen Foerster
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Corinne Thompson
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Jennifer Baumgartner
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Shama Desai Ahuja
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Kathleen Blaney
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Jay K Varma
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, 10065, USA
| | | | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
- Columbia Climate School, Columbia University, New York, NY, 10025, USA
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Li W, Bulekova K, Gregor B, White LF, Kolaczyk ED. Estimation of local time-varying reproduction numbers in noisy surveillance data. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210303. [PMID: 35965456 PMCID: PMC9376722 DOI: 10.1098/rsta.2021.0303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 04/11/2022] [Indexed: 05/04/2023]
Abstract
A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Wenrui Li
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Katia Bulekova
- Research Computing Services, Information Services and Technology Boston University, Boston, MA 02215, USA
| | - Brian Gregor
- Research Computing Services, Information Services and Technology Boston University, Boston, MA 02215, USA
| | - Laura F. White
- Department of Biostatistics, Boston University, Boston, MA 02215, USA
| | - Eric D. Kolaczyk
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
- Hariri Institute for Computing, Boston University, Boston,MA 02215, USA
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36
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Makau DN, Lycett S, Michalska-Smith M, Paploski IAD, Cheeran MCJ, Craft ME, Kao RR, Schroeder DC, Doeschl-Wilson A, VanderWaal K. Ecological and evolutionary dynamics of multi-strain RNA viruses. Nat Ecol Evol 2022; 6:1414-1422. [PMID: 36138206 DOI: 10.1038/s41559-022-01860-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 07/28/2022] [Indexed: 11/09/2022]
Abstract
Potential interactions among co-circulating viral strains in host populations are often overlooked in the study of virus transmission. However, these interactions probably shape transmission dynamics by influencing host immune responses or altering the relative fitness among co-circulating strains. In this Review, we describe multi-strain dynamics from ecological and evolutionary perspectives, outline scales in which multi-strain dynamics occur and summarize important immunological, phylogenetic and mathematical modelling approaches used to quantify interactions among strains. We also discuss how host-pathogen interactions influence the co-circulation of pathogens. Finally, we highlight outstanding questions and knowledge gaps in the current theory and study of ecological and evolutionary dynamics of multi-strain viruses.
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Affiliation(s)
- Dennis N Makau
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
| | | | | | - Igor A D Paploski
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
| | - Maxim C-J Cheeran
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
| | - Meggan E Craft
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, USA
| | - Rowland R Kao
- Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Declan C Schroeder
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA
- School of Biological Sciences, University of Reading, Reading, UK
| | | | - Kimberly VanderWaal
- Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA.
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Asghar A, Imran HM, Bano N, Maalik S, Mushtaq S, Hussain A, Varjani S, Aleya L, Iqbal HMN, Bilal M. SARS-COV-2/COVID-19: scenario, epidemiology, adaptive mutations, and environmental factors. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:69117-69136. [PMID: 35947257 PMCID: PMC9363873 DOI: 10.1007/s11356-022-22333-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
The coronavirus pandemic of 2019 has already exerted an enormous impact. For over a year, the worldwide pandemic has ravaged the whole globe, with approximately 250 million verified human infection cases and a mortality rate surpassing 4 million. While the genetic makeup of the related pathogen (SARS-CoV-2) was identified, many unknown facets remain a mystery, comprising the virus's origin and evolutionary trend. There were many rumors that SARS-CoV-2 was human-borne and its evolution was predicted many years ago, but scientific investigation proved them wrong and concluded that bats might be the origin of SARS-CoV-2 and pangolins act as intermediary species to transmit the virus from bats to humans. Airborne droplets were found to be the leading cause of human-to-human transmission of this virus, but later studies showed that contaminated surfaces and other environmental factors are also involved in its transmission. The evolution of different SARS-CoV-2 variants worsens the condition and has become a challenge to overcome this pandemic. The emergence of COVID-19 is still a mystery, and scientists are unable to explain the exact origin of SARS-CoV-2. This review sheds light on the possible origin of SARS-CoV-2, its transmission, and the key factors that worsen the situation.
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Affiliation(s)
- Asma Asghar
- Department of Biochemistry, University of Agriculture Faisalabad, Faisalabad, 38000, Pakistan
| | - Hafiz Muhammad Imran
- Department of Biochemistry, Government College University Faisalabad, Faisalabad, 38000, Pakistan
| | - Naheed Bano
- Department of Fisheries & Aquaculture, MNS-University of Agriculture, Multan, Pakistan
| | - Sadia Maalik
- Department of Zoology, Government College Women University, Sialkot, Pakistan
| | - Sajida Mushtaq
- Department of Zoology, Government College Women University, Sialkot, Pakistan
| | - Asim Hussain
- Department of Biochemistry, University of Agriculture Faisalabad, Faisalabad, 38000, Pakistan
| | - Sunita Varjani
- Gujarat Pollution Control Board, Gandhinagar, 382 010, Gujarat, India
| | - Lotfi Aleya
- Chrono-Environment Laboratory, UMR CNRS 6249, Bourgogne Franche-Comté University, Besançon, France
| | - Hafiz M N Iqbal
- Tecnologico de Monterrey, School of Engineering and Sciences, 64849, Monterrey, Mexico
| | - Muhammad Bilal
- School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai'an, 223003, China.
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Obeid D, Alnemari R, Al-Qahtani AA, Alsanea M, Alahideb B, Alsuwairi F, Abdulkarim M, Alhamlan FS. SARS-CoV-2 chronological genomic evolution and epidemiology in the Middle East and North Africa (MENA) region as affected by vaccination, conflict and socioeconomical disparities: a population-based cohort study. BMJ Open 2022; 12:e060775. [PMID: 36691215 PMCID: PMC9461085 DOI: 10.1136/bmjopen-2022-060775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/19/2022] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE To describe the chronological genomic evolution of SARS-CoV-2 and its impact on public health in the Middle East and North Africa (MENA) region. METHODS This study analysed all available SARS-CoV-2 genomic sequences, metadata and rates of COVID-19 infection from the MENA region retrieved from the Global Initiative on Sharing All Influenza Data database from January 2020 to August 2021. Inferential and descriptive statistics were conducted to describe the epidemiology of SARS-CoV-2. RESULTS Genomic surveillance of SARS-CoV-2 in the MENA region indicated that the variants in January 2020 predominately belonged to the G, GR, GH or O clades and that the most common variant of concern was Alpha. By August 2021, however, the GK clade dominated (57.4% of all sequenced genomes), followed by the G clade (18.7%) and the GR clade (11.6%). In August, the most commonly sequenced variants of concern were Delta in the Middle East region (91%); Alpha (44.3%) followed by Delta (29.7%) and Beta (25.3%) in the North Africa region; and Alpha (88.9%), followed by Delta (10%) in the fragile and conflict-affected regions of MENA. The mean proportion of the variants of concern among the total sequenced samples differed significantly by country (F=1.93, P=0.0112) but not by major MENA region (F=0.14, P=0.27) or by vaccination coverage (F=1.84, P=0.176). CONCLUSION This analysis of the genomic surveillance of SARS-CoV-2 provides an essential description the virus evolution and its impact on public health safety in the MENA region. As of August 2021, the Delta variant showed a genomic advantage in the MENA region. The MENA region includes several fragile and conflict-affected countries with extremely low levels of vaccination coverage and little genomic surveillance, which may soon exacerbate the existing health crisis within those countries and globally.
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Affiliation(s)
- Dalia Obeid
- Genome of Infectious Diseases Department, Public Health Authority, Riyadh, Saudi Arabia
- Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Rawan Alnemari
- Genome of Infectious Diseases Department, Public Health Authority, Riyadh, Saudi Arabia
| | - Ahmed A Al-Qahtani
- Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Madain Alsanea
- Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Basma Alahideb
- Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Feda Alsuwairi
- Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Maha Abdulkarim
- Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Fatimah S Alhamlan
- Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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39
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Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet 2022; 23:547-562. [PMID: 35459859 PMCID: PMC9028907 DOI: 10.1038/s41576-022-00483-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2022] [Indexed: 01/05/2023]
Abstract
Determining the transmissibility, prevalence and patterns of movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is central to our understanding of the impact of the pandemic and to the design of effective control strategies. Phylogenies (evolutionary trees) have provided key insights into the international spread of SARS-CoV-2 and enabled investigation of individual outbreaks and transmission chains in specific settings. Phylodynamic approaches combine evolutionary, demographic and epidemiological concepts and have helped track virus genetic changes, identify emerging variants and inform public health strategy. Here, we review and synthesize studies that illustrate how phylogenetic and phylodynamic techniques were applied during the first year of the pandemic, and summarize their contributions to our understanding of SARS-CoV-2 transmission and control.
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Affiliation(s)
- Stephen W Attwood
- Department of Zoology, University of Oxford, Oxford, UK.
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK.
| | - Sarah C Hill
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK
| | - David M Aanensen
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Thomas R Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, Cardiff University, Cardiff, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK.
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40
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McLaughlin A, Montoya V, Miller RL, Mordecai GJ, Worobey M, Poon AFY, Joy JB. Genomic epidemiology of the first two waves of SARS-CoV-2 in Canada. eLife 2022; 11:e73896. [PMID: 35916373 PMCID: PMC9345601 DOI: 10.7554/elife.73896] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 07/12/2022] [Indexed: 12/15/2022] Open
Abstract
Tracking the emergence and spread of SARS-CoV-2 lineages using phylogenetics has proven critical to inform the timing and stringency of COVID-19 public health interventions. We investigated the effectiveness of international travel restrictions at reducing SARS-CoV-2 importations and transmission in Canada in the first two waves of 2020 and early 2021. Maximum likelihood phylogenetic trees were used to infer viruses' geographic origins, enabling identification of 2263 (95% confidence interval: 2159-2366) introductions, including 680 (658-703) Canadian sublineages, which are international introductions resulting in sampled Canadian descendants, and 1582 (1501-1663) singletons, introductions with no sampled descendants. Of the sublineages seeded during the first wave, 49% (46-52%) originated from the USA and were primarily introduced into Quebec (39%) and Ontario (36%), while in the second wave, the USA was still the predominant source (43%), alongside a larger contribution from India (16%) and the UK (7%). Following implementation of restrictions on the entry of foreign nationals on 21 March 2020, importations declined from 58.5 (50.4-66.5) sublineages per week to 10.3-fold (8.3-15.0) lower within 4 weeks. Despite the drastic reduction in viral importations following travel restrictions, newly seeded sublineages in summer and fall 2020 contributed to the persistence of COVID-19 cases in the second wave, highlighting the importance of sustained interventions to reduce transmission. Importations rebounded further in November, bringing newly emergent variants of concern (VOCs). By the end of February 2021, there had been an estimated 30 (19-41) B.1.1.7 sublineages imported into Canada, which increasingly displaced previously circulating sublineages by the end of the second wave.Although viral importations are nearly inevitable when global prevalence is high, with fewer importations there are fewer opportunities for novel variants to spark outbreaks or outcompete previously circulating lineages.
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Affiliation(s)
- Angela McLaughlin
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
- Bioinformatics, University of British ColumbiaVancouverCanada
| | - Vincent Montoya
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
| | - Rachel L Miller
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
- Bioinformatics, University of British ColumbiaVancouverCanada
| | - Gideon J Mordecai
- Department of Medicine, University of British ColumbiaVancouverCanada
| | | | - Michael Worobey
- Department of Ecology and Evolution, University of ArizonaTucsonUnited States
| | - Art FY Poon
- Department of Pathology and Laboratory Medicine, Western UniversityLondonCanada
| | - Jeffrey B Joy
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
- Bioinformatics, University of British ColumbiaVancouverCanada
- Department of Medicine, University of British ColumbiaVancouverCanada
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41
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Ye C, Thornlow B, Hinrichs A, Kramer A, Mirchandani C, Torvi D, Lanfear R, Corbett-Detig R, Turakhia Y. matOptimize: a parallel tree optimization method enables online phylogenetics for SARS-CoV-2. Bioinformatics 2022; 38:3734-3740. [PMID: 35731204 PMCID: PMC9344837 DOI: 10.1093/bioinformatics/btac401] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/21/2022] [Accepted: 06/16/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Phylogenetic tree optimization is necessary for precise analysis of evolutionary and transmission dynamics, but existing tools are inadequate for handling the scale and pace of data produced during the coronavirus disease 2019 (COVID-19) pandemic. One transformative approach, online phylogenetics, aims to incrementally add samples to an ever-growing phylogeny, but there are no previously existing approaches that can efficiently optimize this vast phylogeny under the time constraints of the pandemic. RESULTS Here, we present matOptimize, a fast and memory-efficient phylogenetic tree optimization tool based on parsimony that can be parallelized across multiple CPU threads and nodes, and provides orders of magnitude improvement in runtime and peak memory usage compared to existing state-of-the-art methods. We have developed this method particularly to address the pressing need during the COVID-19 pandemic for daily maintenance and optimization of a comprehensive SARS-CoV-2 phylogeny. matOptimize is currently helping refine on a daily basis possibly the largest-ever phylogenetic tree, containing millions of SARS-CoV-2 sequences. AVAILABILITY AND IMPLEMENTATION The matOptimize code is freely available as part of the UShER package (https://github.com/yatisht/usher) and can also be installed via bioconda (https://bioconda.github.io/recipes/usher/README.html). All scripts we used to perform the experiments in this manuscript are available at https://github.com/yceh/matOptimize-experiments. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cheng Ye
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA 92093, USA
| | - Bryan Thornlow
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Angie Hinrichs
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alexander Kramer
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Cade Mirchandani
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Devika Torvi
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA
| | - Robert Lanfear
- Department of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Yatish Turakhia
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA 92093, USA
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Pei S, Kandula S, Vega JC, Yang W, Foerster S, Thompson C, Baumgartner J, Ahuja S, Blaney K, Varma J, Long T, Shaman J. Contact tracing reveals community transmission of COVID-19 in New York City. RESEARCH SQUARE 2022:rs.3.rs-1840065. [PMID: 35923312 PMCID: PMC9347284 DOI: 10.21203/rs.3.rs-1840065/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that higher vaccination coverage and reduced numbers of visitors to points-of-interest are associated with fewer within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.
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Affiliation(s)
| | | | | | | | | | | | | | - Shama Ahuja
- New York City Department of Health and Mental Hygiene
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43
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Abstract
Laboratory tests for the accurate and rapid identification of SARS-CoV-2 variants can potentially guide the treatment of COVID-19 patients and inform infection control and public health surveillance efforts. Here, we present the development and validation of a rapid COVID-19 variant DETECTR assay incorporating loop-mediated isothermal amplification (LAMP) followed by CRISPR-Cas12 based identification of single nucleotide polymorphism (SNP) mutations in the SARS-CoV-2 spike (S) gene. This assay targets the L452R, E484K/Q/A, and N501Y mutations, at least one of which is found in nearly all major variants. In a comparison of three different Cas12 enzymes, only the newly identified enzyme CasDx1 was able to accurately identify all targeted SNP mutations. An analysis pipeline for CRISPR-based SNP identification from 261 clinical samples yielded a SNP concordance of 97.3% and agreement of 98.9% (258 of 261) for SARS-CoV-2 lineage classification, using SARS-CoV-2 whole-genome sequencing and/or real-time RT-PCR as test comparators. We also showed that detection of the single E484A mutation was necessary and sufficient to accurately identify Omicron from other major circulating variants in patient samples. These findings demonstrate the utility of CRISPR-based DETECTR as a faster and simpler diagnostic method compared with sequencing for SARS-CoV-2 variant identification in clinical and public health laboratories.
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Shrestha L, Lin MJ, Xie H, Mills MG, Mohamed Bakhash SA, Gaur VP, Livingston RJ, Castor J, Bruce EA, Botten JW, Huang ML, Jerome KR, Greninger AL, Roychoudhury P. Clinical performance characteristics of the Swift Normalase Amplicon Panel for sensitive recovery of SARS-CoV-2 genomes. J Mol Diagn 2022; 24:963-976. [PMID: 35863699 PMCID: PMC9290336 DOI: 10.1016/j.jmoldx.2022.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 03/24/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
Amplicon-based sequencing methods are central in characterizing the diversity, transmission, and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but need to be rigorously assessed for clinical utility. Herein, we validated the Swift Biosciences' SARS-CoV-2 Swift Normalase Amplicon Panels using remnant clinical specimens. High-quality genomes meeting our established library and sequence quality criteria were recovered from positive specimens, with 95% limit of detection of 40.08 SARS-CoV-2 copies/PCR. Breadth of genome recovery was evaluated across a range of CT values (11.3 to 36.7; median, 21.6). Of 428 positive samples, 413 (96.5%) generated genomes with <10% unknown bases, with a mean genome coverage of 13,545× ± SD 8382×. No genomes were recovered from PCR-negative specimens (n = 30) or from specimens positive for non–SARS-CoV-2 respiratory viruses (n = 20). Compared with whole-genome shotgun metagenomic sequencing (n = 14) or Sanger sequencing for the spike gene (n = 11), pairwise identity between consensus sequences was 100% in all cases, with highly concordant allele frequencies (R2 = 0.99) between Swift and shotgun libraries. When samples from different clades were mixed at varying ratios, expected variants were detected even in 1:99 mixtures. When deployed as a clinical test, 268 tests were performed in the first 23 weeks, with a median turnaround time of 11 days, ordered primarily for outbreak investigations and infection control.
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Affiliation(s)
- Lasata Shrestha
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | - Michelle J Lin
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | - Hong Xie
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | - Margaret G Mills
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | | | - Vinod P Gaur
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | - Robert J Livingston
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | - Jared Castor
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | - Emily A Bruce
- Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT
| | - Jason W Botten
- Department of Medicine, University of Vermont, Burlington, VT
| | - Meei-Li Huang
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA
| | - Keith R Jerome
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Alexander L Greninger
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA.
| | - Pavitra Roychoudhury
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA.
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45
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Comparative Dynamics of Delta and Omicron SARS-CoV-2 Variants across and between California and Mexico. Viruses 2022; 14:v14071494. [PMID: 35891473 PMCID: PMC9317407 DOI: 10.3390/v14071494] [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: 06/07/2022] [Revised: 06/29/2022] [Accepted: 07/04/2022] [Indexed: 11/25/2022] Open
Abstract
Evolutionary analysis using viral sequence data can elucidate the epidemiology of transmission. Using publicly available SARS-CoV-2 sequence and epidemiological data, we developed discrete phylogeographic models to interrogate the emergence and dispersal of the Delta and Omicron variants in 2021 between and across California and Mexico. External introductions of Delta and Omicron in the region peaked in early July (2021-07-10 [95% CI: 2021-04-20, 2021-11-01]) and mid-December (2021-12-15 [95% CI: 2021-11-14, 2022-01-09]), respectively, 3 months and 2 weeks after first detection. These repeated introductions coincided with domestic migration events with no evidence of a unique transmission hub. The spread of Omicron was most consistent with gravity centric patterns within Mexico. While cross-border events accounted for only 5.1% [95% CI: 4.3-6] of all Delta migration events, they accounted for 20.6% [95% CI: 12.4-29] of Omicron movements, paralleling the increase in international travel observed in late 2021. Our investigations of the Delta and Omicron epidemics in the California/Mexico region illustrate the complex interplay and the multiplicity of viral and structural factors that need to be considered to limit viral spread, even as vaccination is reducing disease burden. Understanding viral transmission patterns may help intra-governmental responses to viral epidemics.
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Gupta AK, Kumar M. Benchmarking and Assessment of Eight De Novo Genome Assemblers on Viral Next-Generation Sequencing Data, Including the SARS-CoV-2. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:372-381. [PMID: 35759429 DOI: 10.1089/omi.2022.0042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Viral genomics has become crucial in clinical diagnostics and ecology, not to mention to stem the COVID-19 pandemic. Whole-genome sequencing (WGS) is pivotal in gaining an improved understanding of viral evolution, genomic epidemiology, infectious outbreaks, pathobiology, clinical management, and vaccine development. Genome assembly is one of the crucial steps in WGS data analyses. A series of different assemblers has been developed with the advent of high-throughput next-generation sequencing (NGS). Various studies have reported the evaluation of these assembly tools on distinct datasets; however, these lack data from viral origin. In this study, we performed a comparative evaluation and benchmarking of eight de novo assemblers: SOAPdenovo, Velvet, assembly by short sequences (ABySS), iterative De Bruijn graph assembler (IDBA), SPAdes, Edena, iterative virus assembler, and VICUNA on the viral NGS data from distinct Illumina (GAIIx, Hiseq, Miseq, and Nextseq) platforms. WGS data of diverse viruses, that is, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), dengue virus 3, human immunodeficiency virus 1, hepatitis B virus, human herpesvirus 8, human papillomavirus 16, rhinovirus A, and West Nile virus, were utilized to assess these assemblers. Performance metrics such as genome fraction recovery, assembly lengths, NG50, N50, contig length, contig numbers, mismatches, and misassemblies were analyzed. Overall, three assemblers, that is, SPAdes, IDBA, and ABySS, performed consistently well, including for genome assembly of SARS-CoV-2. These assembly methods should be considered and recommended for future studies of viruses. The study also suggests that implementing two or more assembly approaches should be considered in viral NGS studies, especially in clinical settings. Taken together, the benchmarking of eight de novo genome assemblers reported in this study can inform future public health and ecology research concerning the viruses, the COVID-19 pandemic, and viral outbreaks.
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Affiliation(s)
- Amit Kumar Gupta
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Chandigarh, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Chandigarh, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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47
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Quaglia F, Salladini E, Carraro M, Minervini G, Tosatto SCE, Le Mercier P. SARS-CoV-2 variants preferentially emerge at intrinsically disordered protein sites helping immune evasion. FEBS J 2022; 289:4240-4250. [PMID: 35108439 PMCID: PMC9542094 DOI: 10.1111/febs.16379] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/21/2022] [Accepted: 01/31/2022] [Indexed: 12/13/2022]
Abstract
The SARS‐CoV‐2 pandemic is maintained by the emergence of successive variants, highlighting the flexibility of the protein sequences of the virus. We show that experimentally determined intrinsically disordered regions (IDRs) are abundant in the SARS‐CoV‐2 viral proteins, making up to 28% of disorder content for the S1 subunit of spike and up to 51% for the nucleoprotein, with the vast majority of mutations occurring in the 13 major variants mapped to these IDRs. Strikingly, antigenic sites are enriched in IDRs, in the receptor‐binding domain (RBD) and in the N‐terminal domain (NTD), suggesting a key role of structural flexibility in the antigenicity of the SARS‐CoV‐2 protein surface. Mutations occurring in the S1 subunit and nucleoprotein (N) IDRs are critical for immune evasion and antibody escape, suggesting potential additional implications for vaccines and monoclonal therapeutic strategies. Overall, this suggests the presence of variable regions on S1 and N protein surfaces, which confer sequence and antigenic flexibility to the virus without altering its protein functions.
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Affiliation(s)
- Federica Quaglia
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy.,Department of Biomedical Sciences, University of Padova, Italy
| | | | - Marco Carraro
- Department of Biomedical Sciences, University of Padova, Italy
| | | | | | - Philippe Le Mercier
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
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48
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Hernandez MM, Banu R, Shrestha P, Gonzalez-Reiche AS, van de Guchte A, Farrugia K, Sebra R, Gitman MR, Nowak MD, Cordon-Cardo C, Simon V, van Bakel H, Sordillo EM, Luna N, Ramirez A, Castañeda SA, Patiño LH, Ballesteros N, Muñoz M, Ramírez JD, Paniz-Mondolfi AE. A robust, highly multiplexed mass spectrometry assay to identify SARS-CoV-2 variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.05.28.22275691. [PMID: 35665019 PMCID: PMC9164449 DOI: 10.1101/2022.05.28.22275691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are characterized by differences in transmissibility and response to therapeutics. Therefore, discriminating among them is vital for surveillance, infection prevention, and patient care. While whole viral genome sequencing (WGS) is the "gold standard" for variant identification, molecular variant panels have become increasingly available. Most, however, are based on limited targets and have not undergone comprehensive evaluation. We assessed the diagnostic performance of the highly multiplexed Agena MassARRAY ® SARS-CoV-2 Variant Panel v3 to identify variants in a diverse set of 391 SARS-CoV-2 clinical RNA specimens collected across our health systems in New York City, USA as well as in Bogotá, Colombia (September 2, 2020 - March 2, 2022). We demonstrate almost perfect levels of interrater agreement between this assay and WGS for 9 of 11 variant calls (κ ≥ 0.856) and 25 of 30 targets (κ ≥ 0.820) tested on the panel. The assay had a high diagnostic sensitivity (≥93.67%) for contemporary variants (e.g., Iota, Alpha, Delta, Omicron [BA.1 sublineage]) and a high diagnostic specificity for all 11 variants (≥96.15%) and all 30 targets (≥94.34%) tested. Moreover, we highlight distinct target patterns that can be utilized to identify variants not yet defined on the panel including the Omicron BA.2 and other sublineages. These findings exemplify the power of highly multiplexed diagnostic panels to accurately call variants and the potential for target result signatures to elucidate new ones. Importance The continued circulation of SARS-CoV-2 amidst limited surveillance efforts and inconsistent vaccination of populations has resulted in emergence of variants that uniquely impact public health systems. Thus, in conjunction with functional and clinical studies, continuous detection and identification are quintessential to inform diagnostic and public health measures. Furthermore, until WGS becomes more accessible in the clinical microbiology laboratory, the ideal assay for identifying variants must be robust, provide high resolution, and be adaptable to the evolving nature of viruses like SARS-CoV-2. Here, we highlight the diagnostic capabilities of a highly multiplexed commercial assay to identify diverse SARS-CoV-2 lineages that circulated at over September 2, 2020 - March 2, 2022 among patients seeking care at our health systems. This assay demonstrates variant-specific signatures of nucleotide/amino acid polymorphisms and underscores its utility for detection of contemporary and emerging SARS-CoV-2 variants of concern.
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Affiliation(s)
- Matthew M. Hernandez
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Radhika Banu
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paras Shrestha
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ana S. Gonzalez-Reiche
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adriana van de Guchte
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Keith Farrugia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Mount Sinai PSP Study Group
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VARPP), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Melissa R. Gitman
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael D. Nowak
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carlos Cordon-Cardo
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Viviana Simon
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Center for Vaccine Research and Pandemic Preparedness (C-VARPP), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Harm van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Emilia Mia Sordillo
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nicolas Luna
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Angie Ramirez
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Sergio Andres Castañeda
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Luz Helena Patiño
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Nathalia Ballesteros
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Marina Muñoz
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Juan David Ramírez
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Alberto E. Paniz-Mondolfi
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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49
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Saravanan KA, Panigrahi M, Kumar H, Rajawat D, Nayak SS, Bhushan B, Dutt T. Role of genomics in combating COVID-19 pandemic. Gene 2022; 823:146387. [PMID: 35248659 PMCID: PMC8894692 DOI: 10.1016/j.gene.2022.146387] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 12/20/2022]
Abstract
The coronavirus disease 2019 (COVID-19) quickly swept over the world, becoming one of the most devastating outbreaks in human history. Being the first pandemic in the post-genomic era, advancements in genomics contributed significantly to scientific understanding and public health response to COVID-19. Genomic technologies have been employed by researchers all over the world to better understand the biology of SARS-CoV-2 and its origin, genomic diversity, and evolution. Worldwide genomic resources have greatly aided in the investigation of the COVID-19 pandemic. The pandemic has ushered in a new era of genomic surveillance, wherein scientists are tracking the changes of the SARS-CoV-2 genome in real-time at the international and national levels. Availability of genomic and proteomic information enables the rapid development of molecular diagnostics and therapeutics. The advent of high-throughput sequencing and genome editing technologies led to the development of modern vaccines. We briefly discuss the impact of genomics in the ongoing COVID-19 pandemic in this review.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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50
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Siedlik JA, Watson CJ, Raine MA, Cheng AV, Goering RV, Stessman HAF, Belshan M. Epidemiologic and Genomic Analysis of the Severe Acute Respiratory Syndrome Coronavirus 2 Epidemic in the Nebraska Region of the United States, March 2020-2021. Front Microbiol 2022; 13:878342. [PMID: 35663859 PMCID: PMC9158493 DOI: 10.3389/fmicb.2022.878342] [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: 02/17/2022] [Accepted: 04/27/2022] [Indexed: 11/27/2022] Open
Abstract
COVID-19 emerged at varying intervals in different regions of the United States in 2020. This report details the epidemiologic and genetic evolution of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the first year of the epidemic in the state of Nebraska using data collected from the Creighton Catholic Health Initiatives (CHI) health system. Statistical modelling identified age, gender, and previous history of diabetes and/or stroke as significant risk factors associated with mortality in COVID-19 patients. In parallel, the viral genomes of over 1,000 samples were sequenced. The overall rate of viral variation in the population was 0.07 mutations/day. Genetically, the first 9 months of the outbreak, which include the initial outbreak, a small surge in August and a major outbreak in November 2020 were primarily characterized by B.1. lineage viruses. In early 2021, the United Kingdom variant (B.1.1.7 or alpha) quickly became the dominant variant. Notably, surveillance of non-consensus variants detected B.1.1.7 defining mutations months earlier in Fall 2020. This work provides insights into the regional variance and evolution of SARS-CoV-2 in the Nebraska region during the first year of the pandemic.
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Affiliation(s)
- Jacob A. Siedlik
- Department of Exercise Science and Pre-Health Professions, Creighton University, Omaha, NE, United States
| | - Cynthia J. Watson
- Department of Medical Microbiology and Immunology, Creighton University School of Medicine, Omaha, NE, United States
| | - Morgan A. Raine
- Department of Medical Microbiology and Immunology, Creighton University School of Medicine, Omaha, NE, United States
| | - Anne V. Cheng
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, United States
| | - Richard V. Goering
- Department of Medical Microbiology and Immunology, Creighton University School of Medicine, Omaha, NE, United States
| | - Holly A. F. Stessman
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, United States
| | - Michael Belshan
- Department of Medical Microbiology and Immunology, Creighton University School of Medicine, Omaha, NE, United States
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