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Tang CY, Gao C, Prasai K, Li T, Dash S, McElroy JA, Hang J, Wan XF. Prediction models for COVID-19 disease outcomes. Emerg Microbes Infect 2024; 13:2361791. [PMID: 38828796 PMCID: PMC11182058 DOI: 10.1080/22221751.2024.2361791] [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: 02/11/2024] [Accepted: 05/26/2024] [Indexed: 06/05/2024]
Abstract
SARS-CoV-2 has caused over 6.9 million deaths and continues to produce lasting health consequences. COVID-19 manifests broadly from no symptoms to death. In a retrospective cross-sectional study, we developed personalized risk assessment models that predict clinical outcomes for individuals with COVID-19 and inform targeted interventions. We sequenced viruses from SARS-CoV-2-positive nasopharyngeal swab samples between July 2020 and July 2022 from 4450 individuals in Missouri and retrieved associated disease courses, clinical history, and urban-rural classification. We integrated this data to develop machine learning-based predictive models to predict hospitalization, ICU admission, and long COVID.The mean age was 38.3 years (standard deviation = 21.4) with 55.2% (N = 2453) females and 44.8% (N = 1994) males (not reported, N = 4). Our analyses revealed a comprehensive set of predictors for each outcome, encompassing human, environment, and virus genome-wide genetic markers. Immunosuppression, cardiovascular disease, older age, cardiac, gastrointestinal, and constitutional symptoms, rural residence, and specific amino acid substitutions were associated with hospitalization. ICU admission was associated with acute respiratory distress syndrome, ventilation, bacterial co-infection, rural residence, and non-wild type SARS-CoV-2 variants. Finally, long COVID was associated with hospital admission, ventilation, and female sex.Overall, we developed risk assessment models that offer the capability to identify patients with COVID-19 necessitating enhanced monitoring or early interventions. Of importance, we demonstrate the value of including key elements of virus, host, and environmental factors to predict patient outcomes, serving as a valuable platform in the field of personalized medicine with the potential for adaptation to other infectious diseases.
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Affiliation(s)
- Cynthia Y. Tang
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Cheng Gao
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
| | - Kritika Prasai
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
| | - Tao Li
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Shreya Dash
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
| | - Jane A. McElroy
- Family and Community Medicine, University of Missouri, Columbia, Missouri, USA
| | - Jun Hang
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Xiu-Feng Wan
- Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA
- Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, USA
- Bond Life Sciences Center, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, USA
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2
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Griffiths EJ, Mendes I, Maguire F, Guthrie JL, Wee BA, Schmedes S, Holt K, Yadav C, Cameron R, Barclay C, Dooley D, MacCannell D, Chindelevitch L, Karsch-Mizrachi I, Waheed Z, Katz L, Petit Iii R, Dave M, Oluniyi P, Nasar MI, Raphenya A, Hsiao WWL, Timme RE. PHA4GE quality control contextual data tags: standardized annotations for sharing public health sequence datasets with known quality issues to facilitate testing and training. Microb Genom 2024; 10. [PMID: 38860884 DOI: 10.1099/mgen.0.001260] [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] [Indexed: 06/12/2024] Open
Abstract
As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower quality datasets be made available for analysis and comparison alongside those of higher quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability, and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies - community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The tags can also be applied to synthetic (lab created) data. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA's GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.
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Affiliation(s)
- Emma J Griffiths
- Centre for Infectious Disease Genomics and One Health, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Inês Mendes
- Theiagen Genomics, LLC, Highlands Ranch, Colorado, USA
| | - Finlay Maguire
- Department of Community Health & Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada, and Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jennifer L Guthrie
- Department of Microbiology & Immunology, Western University, London, Ontario, Canada
| | - Bryan A Wee
- The Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Sarah Schmedes
- National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Georgia, USA
| | - Kathryn Holt
- National Microbiology Laboratory, Public health Agency of Canada, Winnipeg, MB, Canada
| | - Chanchal Yadav
- National Microbiology Laboratory, Public health Agency of Canada, Winnipeg, MB, Canada
| | - Rhiannon Cameron
- Centre for Infectious Disease Genomics and One Health, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Charlotte Barclay
- Centre for Infectious Disease Genomics and One Health, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Damion Dooley
- Centre for Infectious Disease Genomics and One Health, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Duncan MacCannell
- National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Georgia, USA
| | - Leonid Chindelevitch
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, UK
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Ilene Karsch-Mizrachi
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Zahra Waheed
- European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Lee Katz
- Center for Food Safety, University of Georgia, Georgia, USA
| | | | - Mugdha Dave
- McMaster University, Hamilton, Ontario, Canada
| | | | - Muhammad Ibtisam Nasar
- Department of Biology, College of Science, United Arab Emirates University- AL Ain, Abu Dhabi, UAE
| | - Amogelang Raphenya
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - William W L Hsiao
- Centre for Infectious Disease Genomics and One Health, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Ruth E Timme
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland, USA
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Lira-Morales JD, López-Cuevas O, Medrano-Félix JA, González-Gómez JP, González-López I, Castro-Del Campo N, Gomez-Gil B, Chaidez C. Genomic Surveillance of SARS-CoV-2 in México: Three Years since Wuhan, China's First Reported Case. Viruses 2023; 15:2223. [PMID: 38005903 PMCID: PMC10674944 DOI: 10.3390/v15112223] [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: 10/21/2023] [Accepted: 10/24/2023] [Indexed: 11/26/2023] Open
Abstract
OBJECTIVE The aim of this work was to analyze the metadata of the SARS-CoV-2 sequences obtained from samples collected in Mexico from 2020 to 2022. MATERIALS AND METHODS Metadata of SARS-CoV-2 sequences from samples collected in Mexico up to 31 December 2022 was retrieved from GISAID and manually cured for interpretation. RESULTS As of December 2022, Mexican health authorities and the scientific community have sequenced up to 81,983 SARS-CoV-2 viral genomes deposited in GISAID, representing 1.1% of confirmed cases. The number of sequences obtained per state corresponded to the gross domestic product (GDP) of each state for the first (Mexico City) and the last (Tlaxcala). Approximately 25% of the sequences were obtained from CoViGen-Mex, an interdisciplinary initiative of health and scientific institutions to collect and sequence samples nationwide. The metadata showed a clear dominance of sequences retrieved by women. A similar variant distribution over time was found in Mexico and overseas, with the Omicron variant predominating. Finally, the age group with the highest representation in the sequences was adults aged 21 to 50 years, accounting for more than 50% of the total. CONCLUSIONS Mexico presents diverse sociodemographic and economic characteristics. The COVID-19 pandemic has been and continues to be a challenge for collaboration across the country and around the world.
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Affiliation(s)
- Juan Daniel Lira-Morales
- Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Culiacán, Culiacan 80110, Mexico; (J.D.L.-M.); (O.L.-C.); (J.P.G.-G.); (I.G.-L.); (N.C.-D.C.)
| | - Osvaldo López-Cuevas
- Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Culiacán, Culiacan 80110, Mexico; (J.D.L.-M.); (O.L.-C.); (J.P.G.-G.); (I.G.-L.); (N.C.-D.C.)
| | - José Andrés Medrano-Félix
- Investigadoras e Investigadores por México-Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Culiacán, Culiacan 80110, Mexico;
| | - Jean Pierre González-Gómez
- Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Culiacán, Culiacan 80110, Mexico; (J.D.L.-M.); (O.L.-C.); (J.P.G.-G.); (I.G.-L.); (N.C.-D.C.)
| | - Irvin González-López
- Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Culiacán, Culiacan 80110, Mexico; (J.D.L.-M.); (O.L.-C.); (J.P.G.-G.); (I.G.-L.); (N.C.-D.C.)
| | - Nohelia Castro-Del Campo
- Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Culiacán, Culiacan 80110, Mexico; (J.D.L.-M.); (O.L.-C.); (J.P.G.-G.); (I.G.-L.); (N.C.-D.C.)
| | - Bruno Gomez-Gil
- Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Mazatlán, Mazatlan 82112, Mexico;
| | - Cristóbal Chaidez
- Centro de Investigación en Alimentación y Desarrollo A.C., Coordinación Regional Culiacán, Culiacan 80110, Mexico; (J.D.L.-M.); (O.L.-C.); (J.P.G.-G.); (I.G.-L.); (N.C.-D.C.)
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4
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Maldonado-Cabrera A, Colin-Vilchis JA, Haque U, Velazquez C, Alvarez Villaseñor AS, Magdaleno-Márquez LE, Calleros-Muñoz CI, Figueroa-Enríquez KF, Angulo-Molina A, Gallego-Hernández AL. SARS-CoV-2 Variants of Concern and Clinical Severity in the Mexican Pediatric Population. Infect Dis Rep 2023; 15:535-548. [PMID: 37737000 PMCID: PMC10514801 DOI: 10.3390/idr15050053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/01/2023] [Accepted: 09/04/2023] [Indexed: 09/23/2023] Open
Abstract
The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern (VOCs) presents global heterogeneity, and their relative effect on pediatric severity is still limited. In this study, we associate VOCs with pediatric clinical severity outcomes in Mexico. Bioinformatics methods were used to characterize VOCs and single amino acid (aa) mutations in 75,348 SARS-CoV-2 genetic sequences from February 2020 to October 2022. High-predominance VOCs groups were calculated and subsequently associated with 372,989 COVID-19 clinical pediatric outcomes. We identified 21 high-frequency mutations related to Omicron lineages with an increased prevalence in pediatric sequences compared to adults. Alpha and the other lineages had a significant increase in case fatality rate (CFR), intensive critical unit (ICU) admission, and automated mechanical ventilation (AMV). Furthermore, a logistic model with age-adjusted variables estimated an increased risk of hospitalization, ICU/AMV, and death in Gamma and Alpha, in contrast to the other lineages. We found that, regardless of the VOCs lineage, infant patients presented the worst severity prognoses. Our findings improve the understanding of the impact of VOCs on pediatric patients across time, regions, and clinical outcomes. Enhanced understanding of the pediatric severity for VOCs would enable the development and improvement of public health strategies worldwide.
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Affiliation(s)
- Anahí Maldonado-Cabrera
- Department of Chemical Biological Sciences, University of Sonora, Hermosillo 83000, Mexico; (A.M.-C.); (C.V.)
- Department of Epidemiology, Family Medicine Unit No. 37, Mexican Social Security Institute (IMSS), Hermosillo 83260, Mexico
| | | | - Ubydul Haque
- Rutgers Global Health Institute, New Brunswick, NJ 08901, USA;
- Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University, Piscataway, NJ 08854, USA
| | - Carlos Velazquez
- Department of Chemical Biological Sciences, University of Sonora, Hermosillo 83000, Mexico; (A.M.-C.); (C.V.)
| | | | | | | | | | - Aracely Angulo-Molina
- Department of Chemical Biological Sciences, University of Sonora, Hermosillo 83000, Mexico; (A.M.-C.); (C.V.)
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland
| | - Ana Lucía Gallego-Hernández
- Department of Chemical Biological Sciences, University of Sonora, Hermosillo 83000, Mexico; (A.M.-C.); (C.V.)
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5
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Elkashlan M, Ahmad RM, Hajar M, Al Jasmi F, Corchado JM, Nasarudin NA, Mohamad MS. A review of SARS-CoV-2 drug repurposing: databases and machine learning models. Front Pharmacol 2023; 14:1182465. [PMID: 37601065 PMCID: PMC10436567 DOI: 10.3389/fphar.2023.1182465] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/06/2023] [Indexed: 08/22/2023] Open
Abstract
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.
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Affiliation(s)
- Marim Elkashlan
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Malak Hajar
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Fatma Al Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Juan Manuel Corchado
- Departamento de Informática y Automática, Facultad de Ciencias, Grupo de Investigación BISITE, Instituto de Investigación Biomédica de Salamanca, University of Salamanca, Salamanca, Spain
| | - Nurul Athirah Nasarudin
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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6
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Kim PY, Kim AY, Newman JJ, Cella E, Bishop TC, Huwe PJ, Uchakina ON, McKallip RJ, Mack VL, Hill MP, Ogungbe IV, Adeyinka O, Jones S, Ware G, Carroll J, Sawyer JF, Densmore KH, Foster M, Valmond L, Thomas J, Azarian T, Queen K, Kamil JP. A collaborative approach to improving representation in viral genomic surveillance. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001935. [PMID: 37467165 PMCID: PMC10355392 DOI: 10.1371/journal.pgph.0001935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/05/2023] [Indexed: 07/21/2023]
Abstract
The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building 'next generation' genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness.
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Affiliation(s)
- Paul Y. Kim
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - Audrey Y. Kim
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - Jamie J. Newman
- School of Biological Sciences, Louisiana Tech University, Ruston, LA, United States of America
| | - Eleonora Cella
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, United States of America
| | - Thomas C. Bishop
- Physics and Chemistry Programs, Louisiana Tech University, Ruston, LA, United States of America
| | - Peter J. Huwe
- Mercer University School of Medicine, Macon, GA, United States of America
| | - Olga N. Uchakina
- Mercer University School of Medicine, Macon, GA, United States of America
| | - Robert J. McKallip
- Mercer University School of Medicine, Macon, GA, United States of America
| | - Vance L. Mack
- Mercer Medicine, Macon, GA, United States of America
| | | | - Ifedayo Victor Ogungbe
- Department of Chemistry, Jackson State University, Jackson, MS, United States of America
| | - Olawale Adeyinka
- Department of Chemistry, Jackson State University, Jackson, MS, United States of America
| | - Samuel Jones
- Health Services Center, Jackson State University, Jackson, MS, United States of America
| | - Gregory Ware
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Jennifer Carroll
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Jarrod F. Sawyer
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Kenneth H. Densmore
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Michael Foster
- School of Biological Sciences, Louisiana Tech University, Ruston, LA, United States of America
| | - Lescia Valmond
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - John Thomas
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - Taj Azarian
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, United States of America
| | - Krista Queen
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Jeremy P. Kamil
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
- Department of Microbiology and Immunology, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
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7
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Mukherjee S, Stamatis D, Li C, Ovchinnikova G, Bertsch J, Sundaramurthi J, Kandimalla M, Nicolopoulos P, Favognano A, Chen IM, Kyrpides N, Reddy TBK. Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9. Nucleic Acids Res 2023; 51:D957-D963. [PMID: 36318257 PMCID: PMC9825498 DOI: 10.1093/nar/gkac974] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/05/2022] [Accepted: 10/16/2022] [Indexed: 01/09/2023] Open
Abstract
The Genomes OnLine Database (GOLD) (https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute (DOE-JGI) continues to maintain its role as one of the flagship genomic metadata repositories of the world. The ever-increasing number of projects and metadata are freely available to the user community world-wide. GOLD's metadata is consumed by scientists and remains an important source for large-scale comparative genomics analysis initiatives. Encouraged by this active user engagement and growth, GOLD has continued to add new components and capabilities. The new features such as a public Application Programming Interface (API) and Ecosystem landing page as well as the growth of different entities in this current GOLD v.9 edition are described in detail in this manuscript.
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Affiliation(s)
- Supratim Mukherjee
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Dimitri Stamatis
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Cindy Tianqing Li
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Galina Ovchinnikova
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jon Bertsch
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Mahathi Kandimalla
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Paul A Nicolopoulos
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Alessandro Favognano
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - I-Min A Chen
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Nikos C Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - T B K Reddy
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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8
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Gill IS, Griffiths EJ, Dooley D, Cameron R, Savić Kallesøe S, John NS, Sehar A, Gosal G, Alexander D, Chapel M, Croxen MA, Delisle B, Di Tullio R, Gaston D, Duggan A, Guthrie JL, Horsman M, Joshi E, Kearny L, Knox N, Lau L, LeBlanc JJ, Li V, Lyons P, MacKenzie K, McArthur AG, Panousis EM, Palmer J, Prystajecky N, Smith KN, Tanner J, Townend C, Tyler A, Van Domselaar G, Hsiao WWL. The DataHarmonizer: a tool for faster data harmonization, validation, aggregation and analysis of pathogen genomics contextual information. Microb Genom 2023; 9:mgen000908. [PMID: 36748616 PMCID: PMC9973856 DOI: 10.1099/mgen.0.000908] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Pathogen genomics is a critical tool for public health surveillance, infection control, outbreak investigations as well as research. In order to make use of pathogen genomics data, they must be interpreted using contextual data (metadata). Contextual data include sample metadata, laboratory methods, patient demographics, clinical outcomes and epidemiological information. However, the variability in how contextual information is captured by different authorities and how it is encoded in different databases poses challenges for data interpretation, integration and their use/re-use. The DataHarmonizer is a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. The tool's web browser-based JavaScript environment enables validation and its offline functionality and local installation increases data security. The DataHarmonizer was developed to address the data sharing needs that arose during the COVID-19 pandemic, and was used by members of the Canadian COVID Genomics Network (CanCOGeN) to harmonize SARS-CoV-2 contextual data for national surveillance and for public repository submission. In order to support coordination of international surveillance efforts, we have partnered with the Public Health Alliance for Genomic Epidemiology to also provide a template conforming to its SARS-CoV-2 contextual data specification for use worldwide. Templates are also being developed for One Health and foodborne pathogens. Overall, the DataHarmonizer tool improves the effectiveness and fidelity of contextual data capture as well as its subsequent usability. Harmonization of contextual information across authorities, platforms and systems globally improves interoperability and reusability of data for concerted public health and research initiatives to fight the current pandemic and future public health emergencies. While initially developed for the COVID-19 pandemic, its expansion to other data management applications and pathogens is already underway.
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Affiliation(s)
- Ivan S Gill
- University of British Columbia, Vancouver, BC, Canada
| | - Emma J Griffiths
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Damion Dooley
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Rhiannon Cameron
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | | | - Nithu Sara John
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Anoosha Sehar
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | - Gurinder Gosal
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
| | | | - Madison Chapel
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Matthew A Croxen
- Alberta Precision Labs, Edmonton, AB, Canada.,Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada
| | | | | | - Daniel Gaston
- Department of Pathology and Laboratory Medicine, Nova Scotia Health, Halifax, NS, Canada
| | - Ana Duggan
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | | | - Mark Horsman
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada.,Public Health Ontario Laboratory, Toronto, ON, Canada
| | - Esha Joshi
- Public Health Ontario Laboratory, Toronto, ON, Canada
| | - Levon Kearny
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Natalie Knox
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Lynette Lau
- The Hospital for Sick Children, Toronto, ON, Canada
| | - Jason J LeBlanc
- Department of Pathology and Laboratory Medicine, Nova Scotia Health, Halifax, NS, Canada
| | - Vincent Li
- Alberta Precision Labs, Edmonton, AB, Canada
| | - Pierre Lyons
- Public Health Agency of Canada, Moncton, NB, Canada
| | | | - Andrew G McArthur
- Michael G. DeGroote Institute for Infectious Disease Research & Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
| | - Emily M Panousis
- Michael G. DeGroote Institute for Infectious Disease Research & Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
| | - John Palmer
- Public Health Ontario Laboratory, Toronto, ON, Canada
| | - Natalie Prystajecky
- University of British Columbia, Vancouver, BC, Canada.,BCCDC Public Health Laboratory, Vancouver, BC, Canada
| | | | - Jennifer Tanner
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Christopher Townend
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Andrea Tyler
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Gary Van Domselaar
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - William W L Hsiao
- University of British Columbia, Vancouver, BC, Canada.,Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
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9
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Bernasconi A, Guizzardi G, Pastor O, Storey VC. Semantic interoperability: ontological unpacking of a viral conceptual model. BMC Bioinformatics 2022; 23:491. [PMID: 36396980 PMCID: PMC9672571 DOI: 10.1186/s12859-022-05022-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/29/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Genomics and virology are unquestionably important, but complex, domains being investigated by a large number of scientists. The need to facilitate and support work within these domains requires sharing of databases, although it is often difficult to do so because of the different ways in which data is represented across the databases. To foster semantic interoperability, models are needed that provide a deep understanding and interpretation of the concepts in a domain, so that the data can be consistently interpreted among researchers. RESULTS In this research, we propose the use of conceptual models to support semantic interoperability among databases and assess their ontological clarity to support their effective use. This modeling effort is illustrated by its application to the Viral Conceptual Model (VCM) that captures and represents the sequencing of viruses, inspired by the need to understand the genomic aspects of the virus responsible for COVID-19. For achieving semantic clarity on the VCM, we leverage the "ontological unpacking" method, a process of ontological analysis that reveals the ontological foundation of the information that is represented in a conceptual model. This is accomplished by applying the stereotypes of the OntoUML ontology-driven conceptual modeling language.As a result, we propose a new OntoVCM, an ontologically grounded model, based on the initial VCM, but with guaranteed interoperability among the data sources that employ it. CONCLUSIONS We propose and illustrate how the unpacking of the Viral Conceptual Model resolves several issues related to semantic interoperability, the importance of which is recognized by the "I" in FAIR principles. The research addresses conceptual uncertainty within the domain of SARS-CoV-2 data and knowledge.The method employed provides the basis for further analyses of complex models currently used in life science applications, but lacking ontological grounding, subsequently hindering the interoperability needed for scientists to progress their research.
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Affiliation(s)
- Anna Bernasconi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Valencia, Spain.
| | - Giancarlo Guizzardi
- Conceptual and Cognitive Modeling Research Group, Free University of Bozen-Bolzano, Bolzano, Italy
- Services and Cybersecurity Group, University of Twente, Enschede, The Netherlands
| | - Oscar Pastor
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Valencia, Spain
| | - Veda C Storey
- J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia, USA
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10
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Ling-Hu T, Rios-Guzman E, Lorenzo-Redondo R, Ozer EA, Hultquist JF. Challenges and Opportunities for Global Genomic Surveillance Strategies in the COVID-19 Era. Viruses 2022; 14:2532. [PMID: 36423141 PMCID: PMC9698389 DOI: 10.3390/v14112532] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
Global SARS-CoV-2 genomic surveillance efforts have provided critical data on the ongoing evolution of the virus to inform best practices in clinical care and public health throughout the pandemic. Impactful genomic surveillance strategies generally follow a multi-disciplinary pipeline involving clinical sample collection, viral genotyping, metadata linkage, data reporting, and public health responses. Unfortunately, current limitations in each of these steps have compromised the overall effectiveness of these strategies. Biases from convenience-based sampling methods can obfuscate the true distribution of circulating variants. The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Limitations and inconsistencies in clinical and demographic data collection and sharing can slow the compilation and limit the utility of comprehensive datasets. This likewise can complicate data reporting, restricting the availability of timely data. Finally, gaps and delays in the implementation of genomic surveillance data in the public health sphere can prevent officials from formulating effective mitigation strategies to prevent outbreaks. In this review, we outline current SARS-CoV-2 global genomic surveillance methods and assess roadblocks at each step of the pipeline to identify potential solutions. Evaluating the current obstacles that impede effective surveillance can improve both global coordination efforts and pandemic preparedness for future outbreaks.
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Affiliation(s)
- Ted Ling-Hu
- Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL 60611, USA
| | - Estefany Rios-Guzman
- Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL 60611, USA
| | - Ramon Lorenzo-Redondo
- Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL 60611, USA
| | - Egon A. Ozer
- Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL 60611, USA
| | - Judd F. Hultquist
- Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- Center for Pathogen Genomics and Microbial Evolution, Robert J. Havey, MD Institute for Global Health, Northwestern University, Chicago, IL 60611, USA
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11
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Kim PY, Kim AY, Newman JJ, Cella E, Bishop TC, Huwe PJ, Uchakina ON, McKallip RJ, Mack VL, Hill MP, Ogungbe IV, Adeyinka O, Jones S, Ware G, Carroll J, Sawyer JF, Densmore KH, Foster M, Valmond L, Thomas J, Azarian T, Queen K, Kamil JP. A collaborative approach to improve representation in viral genomic surveillance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.10.19.512816. [PMID: 36299431 PMCID: PMC9603817 DOI: 10.1101/2022.10.19.512816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building microbial genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness. Author summary Genomic surveillance involves decoding a pathogen’s genetic code to track its spread and evolution. During the pandemic, genomic surveillance programs around the world provided valuable data to scientists, doctors, and public health officials. Knowing the complete SARS-CoV-2 genome has helped detect the emergence of new variants, including ones that are more transmissible or cause more severe disease, and has supported the development of diagnostics, vaccines, and therapeutics. The impact of genomic surveillance on public health depends on representative sampling that accurately reflects the diversity and distribution of populations, as well as rapid turnaround time from sampling to data sharing. After a slow start, SARS-CoV-2 genomic surveillance in the United States grew exponentially. Despite this, many rural regions and ethnic minorities remain poorly represented, leaving significant gaps in the data that informs public health responses. To address this problem, we formed a network of universities and clinics in Louisiana, Georgia, and Mississippi with the goal of increasing SARS-CoV-2 sequencing volume, representation, and equity. Our results demonstrate the advantages of rapidly sequencing pathogens in the same communities where the cases occur and present a model that leverages existing academic and clinical infrastructure for a powerful decentralized genomic surveillance system.
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Affiliation(s)
- Paul Y. Kim
- Department of Biological Sciences, Grambling State University, Grambling, LA
| | - Audrey Y. Kim
- Department of Biological Sciences, Grambling State University, Grambling, LA
| | - Jamie J. Newman
- School of Biological Sciences, Louisiana Tech University, Ruston, LA
| | - Eleonora Cella
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL
| | - Thomas C. Bishop
- Physics and Chemistry Programs, Louisiana Tech University, Ruston, LA
| | | | | | | | | | | | | | | | - Samuel Jones
- Health Services Center, Jackson State University, Jackson, MS
| | - Gregory Ware
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA
| | - Jennifer Carroll
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA
| | - Jarrod F. Sawyer
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA
| | - Kenneth H. Densmore
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA
| | - Michael Foster
- School of Biological Sciences, Louisiana Tech University, Ruston, LA
| | - Lescia Valmond
- Department of Biological Sciences, Grambling State University, Grambling, LA
| | - John Thomas
- Department of Biological Sciences, Grambling State University, Grambling, LA
| | - Taj Azarian
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL
| | - Krista Queen
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA
| | - Jeremy P. Kamil
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA
- Department of Microbiology and Immunology, Louisiana State University Health Shreveport, Shreveport, LA
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12
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Zainulabid UA, Mat Yassim AS, Hussain M, Aslam A, Soffian SN, Mohd Ibrahim MS, Kamarudin N, Kamarulzaman MN, Hin HS, Ahmad HF. Whole genome sequence analysis showing unique SARS-CoV-2 lineages of B.1.524 and AU.2 in Malaysia. PLoS One 2022; 17:e0263678. [PMID: 35213571 PMCID: PMC8880882 DOI: 10.1371/journal.pone.0263678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/25/2022] [Indexed: 12/23/2022] Open
Abstract
SARS-CoV-2 has spread throughout the world since its discovery in China, and Malaysia is no exception. WGS has been a crucial approach in studying the evolution and genetic diversity of SARS-CoV-2 in the ongoing pandemic. Despite considerable number of SARS-CoV-2 genome sequences have been submitted to GISAID and NCBI databases, there is still scarcity of data from Malaysia. This study aims to report new Malaysian lineages of the virus, responsible for the sustained spikes in COVID-19 cases during the third wave of the pandemic. Patients with nasopharyngeal and/or oropharyngeal swabs confirmed COVID-19 positive by real-time RT-PCR with CT value < 25 were chosen for WGS. The selected SARS-CoV-2 isolates were then sequenced, characterized and analyzed along with 986 sequences of the dominant lineages of D614G variants currently circulating throughout Malaysia. The prevalence of clade GH and G formed strong ground for the presence of two Malaysian lineages of AU.2 and B.1.524 that has caused sustained spikes of cases in the country. Statistical analysis on the association of gender and age group with Malaysian lineages revealed a significant association (p <0.05). Phylogenetic analysis revealed dispersion of 41 lineages, of these, 22 lineages are still active. Mutational analysis showed presence of unique G1223C missense mutation in transmembrane domain of the spike protein. For better understanding of the SARS-CoV-2 evolution in Malaysia especially with reference to the reported lineages, large scale studies based on WGS are warranted.
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Affiliation(s)
- Ummu Afeera Zainulabid
- Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Gambang, Pahang, Malaysia
- Department of Internal Medicine, Kulliyyah of Medicine, International Islamic University of Malaysia, Kuantan, Pahang, Malaysia
| | | | - Mushtaq Hussain
- Bioinformatics and Molecular Medicine Research Group, Dow Research Institute of Biotechnology and Biomedical Sciences, Dow College of Biotechnology, Dow University of Health Sciences, Karachi, Pakistan
| | - Ayesha Aslam
- Bioinformatics and Molecular Medicine Research Group, Dow Research Institute of Biotechnology and Biomedical Sciences, Dow College of Biotechnology, Dow University of Health Sciences, Karachi, Pakistan
| | - Sharmeen Nellisa Soffian
- Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Gambang, Pahang, Malaysia
| | - Mohamad Shafiq Mohd Ibrahim
- Department of Paediatric and Dental Public Health, Kulliyyah of Dentistry, International Islamic University Malaysia, Kuantan, Pahang, Malaysia
| | - Norhidayah Kamarudin
- Department of Pathology and Laboratory Medicine, Kulliyyah of Medicine, International Islamic University of Malaysia, Kuantan, Pahang, Malaysia
| | - Mohd Nazli Kamarulzaman
- Department of Surgery, Kulliyyah of Medicine, International Islamic University of Malaysia, Kuantan, Pahang, Malaysia
| | - How Soon Hin
- Department of Internal Medicine, Kulliyyah of Medicine, International Islamic University of Malaysia, Kuantan, Pahang, Malaysia
| | - Hajar Fauzan Ahmad
- Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Gambang, Pahang, Malaysia
- Centre for Research in Advanced Tropical Bioscience (Biotropic Centre), Universiti Malaysia Pahang, Gambang, Pahang, Malaysia
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13
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Gómez-Carballa A, Pardo-Seco J, Bello X, Martinón-Torres F, Salas A. Superspreading in the emergence of COVID-19 variants. Trends Genet 2021; 37:1069-1080. [PMID: 34556337 PMCID: PMC8423994 DOI: 10.1016/j.tig.2021.09.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/02/2021] [Accepted: 09/03/2021] [Indexed: 11/25/2022]
Abstract
Superspreading and variants of concern (VOC) of the human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the main catalyzers of the coronavirus disease 2019 (COVID-19) pandemic. However, measuring their individual impact is challenging. By examining the largest database of SARS-CoV-2 genomes The Global Initiative on Sharing Avian Influenza Data [GISAID; n >1.2 million high-quality (HQ) sequences], we present evidence suggesting that superspreading has had a key role in the epidemiological predominance of VOC. There are clear signatures in the database compatible with large superspreading events (SSEs) coinciding chronologically with the worst epidemiological scenarios triggered by VOC. The data suggest that, without the randomness effect of the genetic drift facilitated by superspreading, new VOC of SARS-CoV-2 would have had more limited chance of success.
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Affiliation(s)
- Alberto Gómez-Carballa
- Genetics, Vaccines, and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain; Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias, Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain
| | - Jacobo Pardo-Seco
- Genetics, Vaccines, and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain; Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias, Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain
| | - Xabier Bello
- Genetics, Vaccines, and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain; Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias, Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain
| | - Federico Martinón-Torres
- Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias, Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain; Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
| | - Antonio Salas
- Genetics, Vaccines, and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain; Unidade de Xenética, Instituto de Ciencias Forenses (INCIFOR), Facultade de Medicina, Universidade de Santiago de Compostela, and GenPoB Research Group, Instituto de Investigaciones Sanitarias, Hospital Clínico Universitario de Santiago (SERGAS), Galicia, Spain.
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14
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Melendrez MC, Shaw S, Brown CT, Goodner BW, Kvaal C. Editorial: Curriculum Applications in Microbiology: Bioinformatics in the Classroom. Front Microbiol 2021; 12:705233. [PMID: 34276638 PMCID: PMC8281245 DOI: 10.3389/fmicb.2021.705233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 06/07/2021] [Indexed: 11/18/2022] Open
Affiliation(s)
| | - Sophie Shaw
- Centre for Genome Enabled Biology and Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - C Titus Brown
- Department of Population Health and Reproduction, University of California, Davis, Davis, CA, United States
| | | | - Christopher Kvaal
- Department of Biology, St. Cloud State University, St. Cloud, MN, United States
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