1
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Du H, Du Z, Wang L, Wang H, Jia M, Zhang C, Liu Y, Zhang C, Zhang Y, Zhang R, Zhang S, Zhang N, Ma Z, Chen C, Liu W, Zeng H, Gao GF, Hou X, Bi Y. Fulminant myocarditis induced by SARS-CoV-2 infection without severe lung involvement: insights into COVID-19 pathogenesis. J Genet Genomics 2024; 51:608-616. [PMID: 38447818 DOI: 10.1016/j.jgg.2024.02.007] [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: 11/17/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection often leads to pulmonary complications. Cardiovascular sequelae, including myocarditis and heart failure, have also been reported. Here, the study presents two fulminant myocarditis cases infected by SARS-CoV-2 exhibiting remarkable elevation of cardiac biomarkers without significant pulmonary injury, as determined by imaging examinations. Immunohistochemical staining reveals the viral antigen within cardiomyocytes, indicating that SARS-CoV-2 could directly infect the myocardium. The full viral genomes from respiratory, anal, and myocardial specimens are obtained via next-generation sequencing. Phylogenetic analyses of the whole genome and spike gene indicate that viruses in the myocardium/pericardial effusion and anal swabs are closely related and cluster together yet diverge from those in the respiratory samples. In addition, unique mutations are found in the anal/myocardial strains compared to the respiratory strains, suggesting tissue-specific virus mutation and adaptation. These findings indicate genetically distinct SARS-CoV-2 variants have infiltrated and disseminated within myocardial tissues, independent of pulmonary injury, and point to different infection routes between the myocardium and respiratory tract, with myocardial infections potentially arising from intestinal infection. These findings highlight the potential for systemic SARS-CoV-2 infection and the importance of a thorough multi-organ assessment in patients for a comprehensive understanding of the pathogenesis of COVID-19.
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Affiliation(s)
- Han Du
- College of Life Science and Technology, Xinjiang University, Urumchi, Xinjiang 830046, China; CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China
| | - Zhongtao Du
- Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - Liang Wang
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China
| | - Hong Wang
- Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China
| | - Mingjun Jia
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China; College of Veterinary Medicine, Shanxi Agricultural University, Taiyuan, Shanxi 030031, China
| | - Chunge Zhang
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yun Liu
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China; College of Veterinary Medicine, Shanxi Agricultural University, Taiyuan, Shanxi 030031, China
| | - Cheng Zhang
- College of Life Science and Technology, Xinjiang University, Urumchi, Xinjiang 830046, China; CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China
| | - Ya Zhang
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China
| | - Ruifeng Zhang
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China
| | - Shuang Zhang
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China
| | - Ning Zhang
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China
| | - Zhenghai Ma
- College of Life Science and Technology, Xinjiang University, Urumchi, Xinjiang 830046, China
| | - Chen Chen
- Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
| | - Wenjun Liu
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Zeng
- Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.
| | - George F Gao
- College of Life Science and Technology, Xinjiang University, Urumchi, Xinjiang 830046, China; CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xiaotong Hou
- Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
| | - Yuhai Bi
- College of Life Science and Technology, Xinjiang University, Urumchi, Xinjiang 830046, China; CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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2
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Martelloni G, Turchi A, Fallerini C, Degl’Innocenti A, Baldassarri M, Olmi S, Furini S, Renieri A. Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features. Front Genet 2024; 15:1362469. [PMID: 38841724 PMCID: PMC11150643 DOI: 10.3389/fgene.2024.1362469] [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: 12/28/2023] [Accepted: 04/09/2024] [Indexed: 06/07/2024] Open
Abstract
The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147-173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%-60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into "Boolean quantum features," inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGS p h 1 and IPGS p h 2 ). By applying a logistic regression with both IPGS, (IPGS p h 2 (or indifferently IPGS p h 1 ) and age as inputs, we reached an accuracy of 84%-86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147-173) by a factor of 10%.
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Affiliation(s)
| | - Alessio Turchi
- INAF Osservatorio Astrofisico di Arcetri, Florence, Italy
| | - Chiara Fallerini
- Medical Genetics, University of Siena, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, Siena, Italy
| | - Andrea Degl’Innocenti
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, Siena, Italy
| | - Margherita Baldassarri
- Medical Genetics, University of Siena, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, Siena, Italy
| | - Simona Olmi
- CNR-Consiglio Nazionale delle Ricerche—Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
| | - Simone Furini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Alessandra Renieri
- Medical Genetics, University of Siena, Siena, Italy
- Department of Medical Biotechnologies, Med Biotech Hub and Competence Center, University of Siena, Siena, Italy
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
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3
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Vaughan TG, Scire J, Nadeau SA, Stadler T. Estimates of early outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data. Proc Natl Acad Sci U S A 2024; 121:e2308125121. [PMID: 38175864 PMCID: PMC10786264 DOI: 10.1073/pnas.2308125121] [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: 05/15/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024] Open
Abstract
We estimate the basic reproductive number and case counts for 15 distinct Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, distributed across 11 populations (10 countries and one cruise ship), based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for 10 (out of 15) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provide information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative number of infections for each outbreak. For 7 out of 11 of the populations studied, the number of confirmed cases is much bigger than the cumulative number of infections estimated from the sequence data, a possible explanation being the presence of unsequenced outbreaks in these populations.
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Affiliation(s)
- Timothy G. Vaughan
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Jérémie Scire
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Sarah A. Nadeau
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, Eidgenössiche Technische Hochschule Zurich, Basel4058, Switzerland
- Computational Evolution Group, Swiss Institute of Bioinformatics, Lausanne1015, Switzerland
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Hyun SW, Han S, Son JW, Song MS, Kim DA, Ha SD. Development and efficacy assessment of hand sanitizers and polylactic acid films incorporating caffeic acid and vanillin for enhanced antiviral properties against HCoV-229E. Virol J 2023; 20:194. [PMID: 37641064 PMCID: PMC10463313 DOI: 10.1186/s12985-023-02159-z] [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: 04/30/2023] [Accepted: 08/14/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Although three years after the outbreak of SARS-CoV-2, the virus is still having a significant impact on human health and the global economy. Infection through respiratory droplets is the main transmission route, but the transmission of the virus by surface contact cannot be ignored. Hand sanitizers and antiviral films can be applied to control SARS-CoV-2, but sanitizers and films show drawbacks such as resistance of the virus against ethanol and environmental problems including the overuse of plastics. Therefore, this study suggested applying natural substrates to hand sanitizers and antiviral films made of biodegradable plastic (PLA). This approach is expected to provide advantages for the easy control of SARS-CoV-2 through the application of natural substances. METHODS Antiviral disinfectants and films were manufactured by adding caffeic acid and vanillin to ethanol, isopropyl alcohol, benzalkonium chloride, and PLA. Antiviral efficacies were evaluated with slightly modified international standard testing methods EN 14,476 and ISO 21,702. RESULTS In suspension, all the hand sanitizers evaluated in this study showed a reduction of more than 4 log within 2 min against HCoV-229E. After natural substances were added to the hand sanitizers, the time needed to reach the detection limit of the viral titer was shortened both in suspension and porcine skin. However, no difference in the time needed to reach the detection limit of the viral titer was observed in benzalkonium chloride. In the case of antiviral films, those made using both PLA and natural substances showed a 1 log reduction of HCoV-229E compared to the neat PLA film for all treatment groups. Furthermore, the influence of the organic load was evaluated according to the number of contacts of the antiviral products with porcine skin. Ten rubs on the skin resulted in slightly higher antiviral activity than 50 rubs. CONCLUSION This study revealed that caffeic acid and vanillin can be effectively used to control HCoV-229E for hand sanitizers and antiviral films. In addition, it is recommended to remove organic matter from the skin for maintaining the antiviral activity of hand sanitizer and antiviral film as the antiviral activity decreased as the organic load increased in this study.
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Affiliation(s)
- Seok-Woo Hyun
- Department of Food Science and Technology, Advanced Food Safety Research Group, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Sangha Han
- Department of Food Science and Technology, Advanced Food Safety Research Group, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Jeong Won Son
- Department of Food Science and Technology, Advanced Food Safety Research Group, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Min Su Song
- Department of Food Science and Technology, Advanced Food Safety Research Group, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Dan Ah Kim
- Department of Food Science and Technology, Advanced Food Safety Research Group, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
| | - Sang-Do Ha
- Department of Food Science and Technology, Advanced Food Safety Research Group, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea.
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5
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Scarpa F, Locci C, Azzena I, Casu M, Fiori PL, Ciccozzi A, Giovanetti M, Quaranta M, Ceccarelli G, Pascarella S, Ciccozzi M, Sanna D. SARS-CoV-2 Recombinants: Genomic Comparison between XBF and Its Parental Lineages. Microorganisms 2023; 11:1824. [PMID: 37512996 PMCID: PMC10383834 DOI: 10.3390/microorganisms11071824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/05/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Recombination events are very common and represent one of the primary drivers of RNA virus evolution. The XBF SARS-CoV-2 lineage is one of the most recently generated recombinants during the COVID-19 pandemic. It is a recombinant of BA.5.2.3 and BA.2.75.3, both descendants of lineages that caused many concerns (BA.5 and BA.2.75, respectively). Here, we performed a genomic survey focused on comparing the recombinant XBF with its parental lineages to provide a comprehensive assessment of the evolutionary potential, epidemiological trajectory, and potential risks. Genetic analyses indicated that although XBF initially showed the typical expansion depicted by a steep curve, causing several concerns, currently there is no indication of significant expansion potential or a contagion rate surpassing that of other currently active or previously prevalent lineages. BSP indicated that the peak has been reached around 19 October 2022 and then the genetic variability suffered slight oscillations until early 5 March 2023 when the population size reduced for the last time starting its last plateau that is still lasting. Structural analyses confirmed its reduced potential, also indicating that properties of NTDs and RBDs of XBF and its parental lineages present no significant difference. Of course, cautionary measures must still be taken and genome-based monitoring remains the best tool for detecting any important changes in viral genome composition.
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Affiliation(s)
- Fabio Scarpa
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Chiara Locci
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Ilenia Azzena
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Marco Casu
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Pier Luigi Fiori
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Azienza Ospedaliera Universitaria (AOU) Sassari, 07100 Sassari, Italy
| | - Alessandra Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
| | - Marta Giovanetti
- Sciences and Technologies for Sustainable Development and One Health, University of Campus Bio-Medico of Rome, 00128 Rome, Italy
- Rene Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-009, Minas Gerais, Brazil
| | - Miriana Quaranta
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza Università di Roma, 00185 Rome, Italy
| | - Giancarlo Ceccarelli
- Department of Public Health and Infectious Diseases, University Hospital Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza Università di Roma, 00185 Rome, Italy
| | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
- Campus Bio-Medico, Fondazione Policlinico Universitario, 00128 Rome, Italy
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
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6
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Scarpa F, Azzena I, Locci C, Casu M, Fiori PL, Ciccozzi A, Angeletti S, Imperia E, Giovanetti M, Maruotti A, Borsetti A, Cauda R, Cassone A, Via A, Pascarella S, Sanna D, Ciccozzi M. Molecular In-Depth on the Epidemiological Expansion of SARS-CoV-2 XBB.1.5. Microorganisms 2023; 11:microorganisms11040912. [PMID: 37110335 PMCID: PMC10142263 DOI: 10.3390/microorganisms11040912] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/25/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Since the beginning of the pandemic, the generation of new variants periodically recurs. The XBB.1.5 SARS-CoV-2 variant is one of the most recent. This research was aimed at verifying the potential hazard of this new subvariant. To achieve this objective, we performed a genome-based integrative approach, integrating results from genetic variability/phylodynamics with structural and immunoinformatic analyses to obtain as comprehensive a viewpoint as possible. The Bayesian Skyline Plot (BSP) shows that the viral population size reached the plateau phase on 24 November 2022, and the number of lineages peaked at the same time. The evolutionary rate is relatively low, amounting to 6.9 × 10−4 subs/sites/years. The NTD domain is identical for XBB.1 and XBB.1.5 whereas their RBDs only differ for the mutations at position 486, where the Phe (in the original Wuhan) is replaced by a Ser in XBB and XBB.1, and by a Pro in XBB.1.5. The variant XBB.1.5 seems to spread more slowly than sub-variants that have caused concerns in 2022. The multidisciplinary molecular in-depth analyses on XBB.1.5 performed here does not provide evidence for a particularly high risk of viral expansion. Results indicate that XBB.1.5 does not possess features to become a new, global, public health threat. As of now, in its current molecular make-up, XBB.1.5 does not represent the most dangerous variant.
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Affiliation(s)
- Fabio Scarpa
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Correspondence: (F.S.); (M.C.)
| | - Ilenia Azzena
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Chiara Locci
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Marco Casu
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Pier Luigi Fiori
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Azienza Ospedaliera Universitaria (AOU) Sassari, 07100 Sassari, Italy
| | - Alessandra Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
| | - Silvia Angeletti
- Unit of Clinical Laboratory Science, Department of Medicine and Surgery, University Campus Bio-Medico of Rome, 00128 Rome, Italy
- Research Unit of Laboratory, University Hospital Campus Bio-Medico, 00128 Rome, Italy
| | - Elena Imperia
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
- Unit of Gastroenterology, Department of Medicine, University Campus Bio-Medico of Rome, 00128 Rome, Italy
| | - Marta Giovanetti
- Instituto Rene Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-009, Minas Gerais, Brazil
- Science and Technology for Sustainable Development and One Health, University of Campus Bio-Medico of Rome, 00128 Rome, Italy
| | | | - Alessandra Borsetti
- National HIV/AIDS Research Center, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Roberto Cauda
- UOC Malattie Infettive, Infectious Disease Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | | | - Allegra Via
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza Università di Roma, 00185 Rome, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza Università di Roma, 00185 Rome, Italy
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
- Correspondence: (F.S.); (M.C.)
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7
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Scarpa F, Imperia E, Azzena I, Giovanetti M, Benvenuto D, Locci C, Casu M, Fiori PL, Maruotti A, Ceccarelli G, Borsetti A, Caruso A, Cauda R, Cassone A, Via A, Pascarella S, Sanna D, Ciccozzi M. Genetic and structural genome-based survey reveals the low potential for epidemiological expansion of the SARS-CoV-2 XBB.1.5 sublineage. J Infect 2023; 86:596-598. [PMID: 36863537 PMCID: PMC9974203 DOI: 10.1016/j.jinf.2023.02.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023]
Affiliation(s)
- Fabio Scarpa
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.
| | - Elena Imperia
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, Italy; Unit of Gastroenterology, Department of Medicine, University Campus Bio-Medico of Rome, 00128 Rome, Italy
| | - Ilenia Azzena
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy; Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | - Marta Giovanetti
- Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil; Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, Rome, Italy
| | - Domenico Benvenuto
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, Italy
| | - Chiara Locci
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy; Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | - Marco Casu
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | - Pier Luigi Fiori
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy; Azienza Ospedaliera Universitaria (AOU) Sassari, Sassari, Italy
| | | | - Giancarlo Ceccarelli
- Department of Public Health and Infectious Diseases, University Hospital Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Alessandra Borsetti
- National HIV/AIDS Research Center, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Arnaldo Caruso
- Department of Molecular and Translational Medicine, Section of Microbiology, University of Brescia, 25123 Brescia, Italy
| | - Roberto Cauda
- UOC Malattie Infettive, Infectious Disease Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | | | - Allegra Via
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza Università di Roma, 00185 Rome, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza Università di Roma, 00185 Rome, Italy
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, Italy.
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8
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González-Vázquez LD, Arenas M. Molecular Evolution of SARS-CoV-2 during the COVID-19 Pandemic. Genes (Basel) 2023; 14:407. [PMID: 36833334 PMCID: PMC9956206 DOI: 10.3390/genes14020407] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) produced diverse molecular variants during its recent expansion in humans that caused different transmissibility and severity of the associated disease as well as resistance to monoclonal antibodies and polyclonal sera, among other treatments. In order to understand the causes and consequences of the observed SARS-CoV-2 molecular diversity, a variety of recent studies investigated the molecular evolution of this virus during its expansion in humans. In general, this virus evolves with a moderate rate of evolution, in the order of 10-3-10-4 substitutions per site and per year, which presents continuous fluctuations over time. Despite its origin being frequently associated with recombination events between related coronaviruses, little evidence of recombination was detected, and it was mostly located in the spike coding region. Molecular adaptation is heterogeneous among SARS-CoV-2 genes. Although most of the genes evolved under purifying selection, several genes showed genetic signatures of diversifying selection, including a number of positively selected sites that affect proteins relevant for the virus replication. Here, we review current knowledge about the molecular evolution of SARS-CoV-2 in humans, including the emergence and establishment of variants of concern. We also clarify relationships between the nomenclatures of SARS-CoV-2 lineages. We conclude that the molecular evolution of this virus should be monitored over time for predicting relevant phenotypic consequences and designing future efficient treatments.
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Affiliation(s)
- Luis Daniel González-Vázquez
- Biomedical Research Center (CINBIO), University of Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, University of Vigo, 36310 Vigo, Spain
| | - Miguel Arenas
- Biomedical Research Center (CINBIO), University of Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, University of Vigo, 36310 Vigo, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310 Vigo, Spain
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9
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Perez-Zabaleta M, Archer A, Khatami K, Jafferali MH, Nandy P, Atasoy M, Birgersson M, Williams C, Cetecioglu Z. Long-term SARS-CoV-2 surveillance in the wastewater of Stockholm: What lessons can be learned from the Swedish perspective? THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:160023. [PMID: 36356735 PMCID: PMC9640212 DOI: 10.1016/j.scitotenv.2022.160023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/14/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Wastewater-based epidemiology (WBE) can be used to track the spread of SARS-CoV-2 in a population. This study presents the learning outcomes from over two-year long monitoring of SARS-CoV-2 in Stockholm, Sweden. The three main wastewater treatment plants in Stockholm, with a total of six inlets, were monitored from April 2020 until June 2022 (in total 600 samples). This spans five major SARS-CoV-2 waves, where WBE data provided early warning signals for each wave. Further, the measured SARS-CoV-2 content in the wastewater correlated significantly with the level of positive COVID-19 tests (r = 0.86; p << 0.0001) measured by widespread testing of the population. Moreover, as a proof-of-concept, six SARS-CoV-2 variants of concern were monitored using hpPCR assay, demonstrating that variants can be traced through wastewater monitoring. During this long-term surveillance, two sampling protocols, two RNA concentration/extraction methods, two calculation approaches, and normalization to the RNA virus Pepper mild mottle virus (PMMoV) were evaluated. In addition, a study of storage conditions was performed, demonstrating that the decay of viral RNA was significantly reduced upon the addition of glycerol to the wastewater before storage at -80 °C. Our results provide valuable information that can facilitate the incorporation of WBE as a prediction tool for possible future outbreaks of SARS-CoV-2 and preparations for future pandemics.
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Affiliation(s)
- Mariel Perez-Zabaleta
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Amena Archer
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Kasra Khatami
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Mohammed Hakim Jafferali
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Prachi Nandy
- Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Merve Atasoy
- Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Madeleine Birgersson
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Cecilia Williams
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Zeynep Cetecioglu
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden.
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10
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Bousali M, Pogka V, Vatsellas G, Loupis T, Athanasiadis EI, Zoi K, Thanos D, Paraskevis D, Mentis A, Karamitros T. Tracing the First Days of the SARS-CoV-2 Pandemic in Greece and the Role of the First Imported Group of Travelers. Microbiol Spectr 2022; 10:e0213422. [PMID: 36409093 PMCID: PMC9769540 DOI: 10.1128/spectrum.02134-22] [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: 06/07/2022] [Accepted: 10/31/2022] [Indexed: 11/23/2022] Open
Abstract
The first SARS-CoV-2 case in Greece was confirmed on February 26, 2020, and since then, multiple strains have circulated the country, leading to regional and country-wide outbreaks. Our aim is to enlighten the events that took place during the first days of the SARS-CoV-2 pandemic in Greece, focusing on the role of the first imported group of travelers. We used whole-genome SARS-CoV-2 sequences obtained from the infected travelers of the group as well as Greece-derived and globally subsampled sequences and applied dedicated phylogenetics and phylodynamics tools as well as in-house-developed bioinformatics pipelines. Our analyses reveal the genetic variants circulating in Greece during the first days of the pandemic and the role of the group's imported strains in the course of the first pandemic wave in Greece. The strain that dominated in Greece throughout the first wave, bearing the D614G mutation, was primarily imported from a certain group of travelers, while molecular and clinical data suggest that the infection of the travelers occurred in Egypt. Founder effects early in the pandemic are important for the success of certain strains, as those arriving early, several times, and to diverse locations lead to the formation of large transmission clusters that can be estimated using molecular epidemiology approaches and can be a useful surveillance tool for the prioritization of nonpharmaceutical interventions and combating present and future outbreaks. IMPORTANCE The strain that dominated in Greece during the first pandemic wave was primarily imported from a group of returning travelers in February 2020, while molecular and clinical data suggest that the origin of the transmission was Egypt. The observed molecular transmission clusters reflect the transmission dynamics of this particular strain bearing the D614G mutation while highlighting the necessity of their use as a surveillance tool for the prioritization of nonpharmaceutical interventions and combating present and future outbreaks.
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Affiliation(s)
- Maria Bousali
- Bioinformatics and Applied Genomics Unit, Department of Microbiology, Hellenic Pasteur Institute, Athens, Greece
| | - Vasiliki Pogka
- Bioinformatics and Applied Genomics Unit, Department of Microbiology, Hellenic Pasteur Institute, Athens, Greece
- Laboratory of Medical Microbiology, Department of Microbiology, Hellenic Pasteur Institute, Athens, Greece
| | - Giannis Vatsellas
- Greek Genome Center, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Theodoros Loupis
- Greek Genome Center, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
- Haematology Research Laboratory, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Emmanouil I. Athanasiadis
- Greek Genome Center, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Katerina Zoi
- Greek Genome Center, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
- Haematology Research Laboratory, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Dimitris Thanos
- Greek Genome Center, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Dimitrios Paraskevis
- Department of Hygiene Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Andreas Mentis
- Laboratory of Medical Microbiology, Department of Microbiology, Hellenic Pasteur Institute, Athens, Greece
| | - Timokratis Karamitros
- Bioinformatics and Applied Genomics Unit, Department of Microbiology, Hellenic Pasteur Institute, Athens, Greece
- Laboratory of Medical Microbiology, Department of Microbiology, Hellenic Pasteur Institute, Athens, Greece
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11
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Scarpa F, Sanna D, Benvenuto D, Borsetti A, Azzena I, Casu M, Fiori PL, Giovanetti M, Maruotti A, Ceccarelli G, Caruso A, Caccuri F, Cauda R, Cassone A, Pascarella S, Ciccozzi M. Genetic and Structural Data on the SARS-CoV-2 Omicron BQ.1 Variant Reveal Its Low Potential for Epidemiological Expansion. Int J Mol Sci 2022; 23:15264. [PMID: 36499592 PMCID: PMC9739521 DOI: 10.3390/ijms232315264] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 11/29/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
The BQ.1 SARS-CoV-2 variant, also known as Cerberus, is one of the most recent Omicron descendant lineages. Compared to its direct progenitor BA.5, BQ.1 has some additional spike mutations in some key antigenic sites, which confer further immune escape ability over other circulating lineages. In such a context, here, we perform a genome-based survey aimed at obtaining a complete-as-possible nuance of this rapidly evolving Omicron subvariant. Genetic data suggest that BQ.1 represents an evolutionary blind background, lacking the rapid diversification that is typical of a dangerous lineage. Indeed, the evolutionary rate of BQ.1 is very similar to that of BA.5 (7.6 × 10-4 and 7 × 10-4 subs/site/year, respectively), which has been circulating for several months. The Bayesian Skyline Plot reconstruction indicates a low level of genetic variability, suggesting that the peak was reached around 3 September 2022. Concerning the affinity for ACE2, structure analyses (also performed by comparing the properties of BQ.1 and BA.5 RBD) indicate that the impact of the BQ.1 mutations may be modest. Likewise, immunoinformatic analyses showed moderate differences between the BQ.1 and BA5 potential B-cell epitopes. In conclusion, genetic and structural analyses on SARS-CoV-2 BQ.1 suggest no evidence of a particularly dangerous or high expansion capability. Genome-based monitoring must continue uninterrupted for a better understanding of its descendants and all other lineages.
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Affiliation(s)
- Fabio Scarpa
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Domenico Benvenuto
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
| | - Alessandra Borsetti
- National HIV/AIDS Research Center, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Ilenia Azzena
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Marco Casu
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Pier Luigi Fiori
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
| | - Marta Giovanetti
- Flavivirus Laboratory, Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro 21040-900, Brazil
- Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, 00128 Rome, Italy
| | | | - Giancarlo Ceccarelli
- Department of Public Health and Infectious Diseases, University Hospital Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
| | - Arnaldo Caruso
- Department of Molecular and Translational Medicine, Section of Microbiology, University of Brescia, 25123 Brescia, Italy
| | - Francesca Caccuri
- Department of Molecular and Translational Medicine, Section of Microbiology, University of Brescia, 25123 Brescia, Italy
| | - Roberto Cauda
- UOC Malattie Infettive, Infectious Disease Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | | | - Stefano Pascarella
- Department of Biochemical Sciences “A Rossi Fanelli”, Sapienza Università di Roma, 00161 Rome, Italy
| | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, 00128 Rome, Italy
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12
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Structural analysis of a simplified model reproducing SARS-CoV-2 S RBD/ACE2 binding site. Heliyon 2022; 8:e11568. [PMID: 36406731 PMCID: PMC9663143 DOI: 10.1016/j.heliyon.2022.e11568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 09/07/2022] [Accepted: 10/07/2022] [Indexed: 11/16/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an RNA virus identified as the cause of the coronavirus outbreak in December 2019 (COVID-19). Like all the RNA viruses, SARS-CoV-2 constantly evolves through mutations in its genome, accumulating 1–2 nucleotide changes every month, giving the virus a selective advantage through enhanced transmissibility, greater pathogenicity, and the possibility of circumventing immunity previously acquired by an individual either by natural infection or by vaccination. Several SARS-CoV-2 variants of concern (VoC) have been identified, among which we find Alpha (Lineage B.1.1.7), Beta (Lineage B.1.351), and Gamma (Lineage P.1) variants. Most of the mutations occur in the spike (S) protein, a surface glycoprotein that plays a crucial role in viral infection; the S protein binds the host cell receptor, the angiotensin-converting enzyme of type 2 (ACE2) via the receptor binding domain (RBD) and catalyzes the fusion of the viral membrane with the host cell. In this work, we present the development of a simplified system that would afford to study the change in the SARS-CoV-2 S RBD/ACE2 binding related to the frequent mutations. In particular, we synthesized and studied the structure of short amino acid sequences, mimicking the two proteins’ critical portions. Variations in the residues were easily managed through the one-point alteration of the sequences. Nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopies provide insights into ACE2 and SARS-CoV-2 S RBD structure with its related three variants (Alpha, Beta, and Gamma). Spectroscopy data supported by molecular dynamics lead to the description of an ACE2/RBD binding model in which the effect of a single amino acid mutation in changing the binding of S protein to the ACE2 receptor is predictable.
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13
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González-Parra G, Díaz-Rodríguez M, Arenas AJ. Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela. Spat Spatiotemporal Epidemiol 2022; 43:100532. [PMID: 36460458 PMCID: PMC9420318 DOI: 10.1016/j.sste.2022.100532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 07/08/2022] [Accepted: 08/15/2022] [Indexed: 01/19/2023]
Abstract
We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R0 using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.
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Affiliation(s)
- Gilberto González-Parra
- New Mexico Institute of Mining and Technology, Department of Mathematics, New Mexico Tech, Socorro, NM, USA,Corresponding author
| | - Miguel Díaz-Rodríguez
- Grupo Matemática Multidisciplinar, Facultad de Ingeniería, Universidad de los Andes, Venezuela
| | - Abraham J. Arenas
- Universidad de Córdoba, Departamento de Matemáticas y Estadística, Montería, Colombia
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14
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In silico evaluation of Philippine Natural Products against SARS-CoV-2 Main Protease. J Mol Model 2022; 28:345. [PMID: 36205801 PMCID: PMC9540280 DOI: 10.1007/s00894-022-05334-1] [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: 01/21/2022] [Accepted: 09/28/2022] [Indexed: 10/25/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is a novel strain of coronavirus first reported in December 2019 which rapidly spread throughout the world and was subsequently declared a pandemic by the World Health Organization (WHO) in March 2020. Although vaccines, as well as treatments, have been rapidly developed and deployed, these are still spread thin, especially in the developing world. There is also a continuing threat of the emergence of mutated variants which may not be as responsive to available vaccines and drugs. Accessible and affordable sources of antiviral drugs against SARS-CoV-2 offer wider options for the clinical treatment of populations at risk for severe COVID-19. Using in silico methods, this study identified potential inhibitors against the SARS-CoV-2 main protease (Mpro), the protease directly responsible for the activation of the viral replication enzyme, from a consolidated database of 1516 Philippine natural products. Molecular docking experiments, along with in silico ADME predictions, determined top ligands from this database with the highest potential inhibitory effects against Mpro. Molecular dynamic trajectories of the apo and diosmetin-7-O-b-D-glucopyranoside (DG) in complex with the protein predicted potential mechanisms of action for the ligand-by separating the Cys145-His41 catalytic dyad and by influencing the protein network through key intra-signaling residues within the Mpro binding site. These findings show the inhibitory potential of DG against the SARS-CoV-2 Mpro, and further validation is recommended through in vitro or in vivo experimentation.
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15
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Estimation of the Serial Interval and the Effective Reproductive Number of COVID-19 Outbreak Using Contact Data in Burkina Faso, a Sub-Saharan African Country. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8239915. [PMID: 36199779 PMCID: PMC9527438 DOI: 10.1155/2022/8239915] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 09/07/2022] [Indexed: 11/18/2022]
Abstract
The COVID-19 outbreak has spread all around the world in less than four months. However, the pattern of the epidemic was different according to the countries. We propose this paper to describe the transmission network and to estimate the serial interval and the reproductive number of the novel coronavirus disease (COVID-19) in Burkina Faso, a Sub-Saharan African country. Data from the COVID-19 response team was analyzed. Information on the 804 first detected cases were pulled together. From contact tracing information, 126 infector-infectee pairs were built. The principal infection clusters with their index cases were observed, principally the two major identified indexes in Burkina. However, the generations of infections were usually short (less than four). The serial interval was estimated to follow a gamma distribution with a shape parameter 1.04 (95% credibility interval: 0.69–1.57) and a scale parameter of 5.69 (95% credibility interval: 3.76–9.11). The basic reproductive number was estimated at 2.36 (95% confidence interval: 1.46–3.26). However, the effective reproductive number decreases very quickly, reaching a minimum value of 0.20 (95% confidence interval: 0.06–0.34). Estimated parameters are made available to monitor the outbreak in Sub-Saharan African countries. These show serial intervals like in the other continents but less infectiousness.
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16
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Scarpa F, Sanna D, Azzena I, Giovanetti M, Benvenuto D, Angeletti S, Ceccarelli G, Pascarella S, Casu M, Fiori PL, Ciccozzi M. On the SARS-CoV-2 BA.2.75 variant: A genetic and structural point of view. J Med Virol 2022; 95:e28119. [PMID: 36059082 PMCID: PMC9538365 DOI: 10.1002/jmv.28119] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 08/27/2022] [Accepted: 08/31/2022] [Indexed: 01/11/2023]
Affiliation(s)
- Fabio Scarpa
- Department of Biomedical SciencesUniversity of SassariSassariItaly
| | - Daria Sanna
- Department of Biomedical SciencesUniversity of SassariSassariItaly
| | - Ilenia Azzena
- Department of Biomedical SciencesUniversity of SassariSassariItaly,Department of Veterinary MedicineUniversity of SassariSassariItaly
| | - Marta Giovanetti
- Laboratório de Flavivírus, Instituto Oswaldo CruzFundação Oswaldo CruzRio de JaneiroBrazil,Department of Science and Technology for Humans and the EnvironmentUniversity of Campus Bio‐Medico di RomaRomeItaly
| | - Domenico Benvenuto
- Unit of Medical Statistics and Molecular EpidemiologyUniversity Campus Bio‐Medico of RomeRomeItaly
| | - Silvia Angeletti
- Department of Medicine, Unit of Clinical Laboratory ScienceUniversity Campus Bio‐Medico of RomeRomeItaly
| | - Giancarlo Ceccarelli
- Department of Public Health and Infectious Diseases, University Hospital Policlinico Umberto ISapienza University of RomeRomeItaly
| | - Stefano Pascarella
- Department of Biochemical Sciences “A Rossi Fanelli”Sapienza Università di RomaRomeItaly
| | - Marco Casu
- Department of Veterinary MedicineUniversity of SassariSassariItaly
| | - Pier Luigi Fiori
- Department of Biomedical SciencesUniversity of SassariSassariItaly
| | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular EpidemiologyUniversity Campus Bio‐Medico of RomeRomeItaly
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17
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Vegvari C, Abbott S, Ball F, Brooks-Pollock E, Challen R, Collyer BS, Dangerfield C, Gog JR, Gostic KM, Heffernan JM, Hollingsworth TD, Isham V, Kenah E, Mollison D, Panovska-Griffiths J, Pellis L, Roberts MG, Scalia Tomba G, Thompson RN, Trapman P. Commentary on the use of the reproduction number R during the COVID-19 pandemic. Stat Methods Med Res 2022; 31:1675-1685. [PMID: 34569883 PMCID: PMC9277711 DOI: 10.1177/09622802211037079] [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] [Indexed: 11/16/2022]
Abstract
Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.
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Affiliation(s)
- Carolin Vegvari
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK
| | - Sam Abbott
- Center for the Mathematical Modelling of Infectious Diseases, 4906London School of Hygiene & Tropical Medicine, UK
| | - Frank Ball
- School of Mathematical Sciences, 6123University of Nottingham, UK
| | - Ellen Brooks-Pollock
- Bristol Veterinary School, 1980University of Bristol, UK.,NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol, UK
| | - Robert Challen
- EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK.,Somerset NHS Foundation Trust, UK
| | - Benjamin S Collyer
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK
| | | | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
| | - Katelyn M Gostic
- Department of Ecology and Evolution, 2462University of Chicago, USA
| | - Jane M Heffernan
- Centre for Disease Modelling, Mathematics & Statistics, 7991York University, Canada.,COVID Modelling Task-Force, The Fields Institute, Canada
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, 6396University of Oxford, UK
| | - Valerie Isham
- Department of Statistical Science, 4919University College London, UK
| | - Eben Kenah
- Division of Biostatistics, College of Public Health, 2647The Ohio State University, USA
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.,Wolfson Centre for Mathematical Biology, Mathematical Institute and The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, 5292The University of Manchester, UK.,The Alan Turing Institute, UK
| | - Michael G Roberts
- School of Natural and Computational Sciences and New Zealand Institute for Advanced Study, Massey University, New Zealand
| | | | - Robin N Thompson
- Mathematics Institute, 2707University of Warwick, Coventry, UK.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, 2707University of Warwick, Coventry, UK
| | - Pieter Trapman
- Department of Mathematics, 7675Stockholm University, Sweden
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18
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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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19
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Abstract
The ongoing global pandemic has sharply increased the amount of data available to researchers in epidemiology and public health. Unfortunately, few existing analysis tools are capable of exploiting all of the information contained in a pandemic-scale data set, resulting in missed opportunities for improved surveillance and contact tracing. In this paper, we develop the variational Bayesian skyline (VBSKY), a method for fitting Bayesian phylodynamic models to very large pathogen genetic data sets. By combining recent advances in phylodynamic modeling, scalable Bayesian inference and differentiable programming, along with a few tailored heuristics, VBSKY is capable of analyzing thousands of genomes in a few minutes, providing accurate estimates of epidemiologically relevant quantities such as the effective reproduction number and overall sampling effort through time. We illustrate the utility of our method by performing a rapid analysis of a large number of SARS-CoV-2 genomes, and demonstrate that the resulting estimates closely track those derived from alternative sources of public health data.
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Affiliation(s)
- Caleb Ki
- Department of Statistics, University of Michigan
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20
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Romano CM, de Oliveira CM, da Silva LS, Levi JE. Early Emergence and Dispersal of Delta SARS-CoV-2 Lineage AY.99.2 in Brazil. Front Med (Lausanne) 2022; 9:930380. [PMID: 35783651 PMCID: PMC9249134 DOI: 10.3389/fmed.2022.930380] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
The year of 2021 was marked by the emergence and dispersal of a number of SARS-CoV-2 lineages, resulting in the “third wave” of COVID-19 in several countries despite the level of vaccine coverage. Soon after the first confirmed cases of COVID-19 by the Delta variant in Brazil, at least seven Delta sub-lineages emerged, including the globally spread AY.101 and AY.99.2. In this study we performed a detailed analysis of the COVID-19 scenario in Brazil from April to December 2021 by using data collected by the largest private medical diagnostic company in Latin America (Dasa), and SARS-CoV-2 genomic sequences generated by its SARS-CoV-2 genomic surveillance project (GENOV). For phylogenetic and Bayesian analysis, SARS-CoV-2 genomes available at GISAID public database were also retrieved. We confirmed that the Brazilian AY.99.2 and AY.101 were the most prevalent lineages during this period, overpassing the Gamma variant in July/August. We also estimated that AY.99.2 likely emerged a few weeks after the entry of the B.1.617.2 in the country, at some point between late April and May and rapidly spread to other countries. Despite no increased fitness described for the AY.99.2 lineage, a rapid shift in the composition of Delta SARS-CoV-2 lineages prevalence in Brazil took place. Understanding the reasons leading the AY.99.2 to become the dominant lineage in the country is important to understand the process of lineage competitions that may inform future control measures.
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Affiliation(s)
- Camila Malta Romano
- Hospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, Brazil
- Instituto de Medicina Tropical de São Paulo, Moléstias Infecciosas, Universidade de São Paulo, São Paulo, Brazil
- *Correspondence: Camila Malta Romano
| | | | | | - José Eduardo Levi
- Instituto de Medicina Tropical de São Paulo, Moléstias Infecciosas, Universidade de São Paulo, São Paulo, Brazil
- Research & Development, Dasa Laboratories, Dasa, Brazil
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21
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Martínez-González B, Soria ME, Vázquez-Sirvent L, Ferrer-Orta C, Lobo-Vega R, Mínguez P, de la Fuente L, Llorens C, Soriano B, Ramos-Ruíz R, Cortón M, López-Rodríguez R, García-Crespo C, Somovilla P, Durán-Pastor A, Gallego I, de Ávila AI, Delgado S, Morán F, López-Galíndez C, Gómez J, Enjuanes L, Salar-Vidal L, Esteban-Muñoz M, Esteban J, Fernández-Roblas R, Gadea I, Ayuso C, Ruíz-Hornillos J, Verdaguer N, Domingo E, Perales C. SARS-CoV-2 Mutant Spectra at Different Depth Levels Reveal an Overwhelming Abundance of Low Frequency Mutations. Pathogens 2022; 11:662. [PMID: 35745516 PMCID: PMC9227345 DOI: 10.3390/pathogens11060662] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 12/23/2022] Open
Abstract
Populations of RNA viruses are composed of complex and dynamic mixtures of variant genomes that are termed mutant spectra or mutant clouds. This applies also to SARS-CoV-2, and mutations that are detected at low frequency in an infected individual can be dominant (represented in the consensus sequence) in subsequent variants of interest or variants of concern. Here we briefly review the main conclusions of our work on mutant spectrum characterization of hepatitis C virus (HCV) and SARS-CoV-2 at the nucleotide and amino acid levels and address the following two new questions derived from previous results: (i) how is the SARS-CoV-2 mutant and deletion spectrum composition in diagnostic samples, when examined at progressively lower cut-off mutant frequency values in ultra-deep sequencing; (ii) how the frequency distribution of minority amino acid substitutions in SARS-CoV-2 compares with that of HCV sampled also from infected patients. The main conclusions are the following: (i) the number of different mutations found at low frequency in SARS-CoV-2 mutant spectra increases dramatically (50- to 100-fold) as the cut-off frequency for mutation detection is lowered from 0.5% to 0.1%, and (ii) that, contrary to HCV, SARS-CoV-2 mutant spectra exhibit a deficit of intermediate frequency amino acid substitutions. The possible origin and implications of mutant spectrum differences among RNA viruses are discussed.
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Affiliation(s)
- Brenda Martínez-González
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain;
| | - María Eugenia Soria
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain; (C.G.-C.); (P.S.); (A.D.-P.); (I.G.); (A.I.d.Á.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Lucía Vázquez-Sirvent
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
| | - Cristina Ferrer-Orta
- Structural Biology Department, Institut de Biología Molecular de Barcelona CSIC, 08028 Barcelona, Spain; (C.F.-O.); (N.V.)
| | - Rebeca Lobo-Vega
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
| | - Pablo Mínguez
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (P.M.); (L.d.l.F.); (M.C.); (R.L.-R.); (C.A.)
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Bioinformatics Unit, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28040 Madrid, Spain
| | - Lorena de la Fuente
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (P.M.); (L.d.l.F.); (M.C.); (R.L.-R.); (C.A.)
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Bioinformatics Unit, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28040 Madrid, Spain
| | - Carlos Llorens
- Biotechvana, “Scientific Park”, Universidad de Valencia, 46980 Valencia, Spain; (C.L.); (B.S.)
| | - Beatriz Soriano
- Biotechvana, “Scientific Park”, Universidad de Valencia, 46980 Valencia, Spain; (C.L.); (B.S.)
| | - Ricardo Ramos-Ruíz
- Unidad de Genómica, “Scientific Park of Madrid”, Campus de Cantoblanco, 28049 Madrid, Spain;
| | - Marta Cortón
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (P.M.); (L.d.l.F.); (M.C.); (R.L.-R.); (C.A.)
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Rosario López-Rodríguez
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (P.M.); (L.d.l.F.); (M.C.); (R.L.-R.); (C.A.)
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Carlos García-Crespo
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain; (C.G.-C.); (P.S.); (A.D.-P.); (I.G.); (A.I.d.Á.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Pilar Somovilla
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain; (C.G.-C.); (P.S.); (A.D.-P.); (I.G.); (A.I.d.Á.)
- Departamento de Biología Molecular, Universidad Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain
| | - Antoni Durán-Pastor
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain; (C.G.-C.); (P.S.); (A.D.-P.); (I.G.); (A.I.d.Á.)
| | - Isabel Gallego
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain; (C.G.-C.); (P.S.); (A.D.-P.); (I.G.); (A.I.d.Á.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Ana Isabel de Ávila
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain; (C.G.-C.); (P.S.); (A.D.-P.); (I.G.); (A.I.d.Á.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Soledad Delgado
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos (ETSISI), Universidad Politécnica de Madrid, 28031 Madrid, Spain;
| | - Federico Morán
- Departamento de Bioquímica y Biología Molecular, Universidad Complutense de Madrid, 28005 Madrid, Spain;
| | - Cecilio López-Galíndez
- Unidad de Virología Molecular, Laboratorio de Referencia e Investigación en Retrovirus, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Majadahonda, 28222 Madrid, Spain;
| | - Jordi Gómez
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain;
- Instituto de Parasitología y Biomedicina ‘López-Neyra’ (CSIC), Parque Tecnológico Ciencias de la Salud, Armilla, 18016 Granada, Spain
| | - Luis Enjuanes
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain;
| | - Llanos Salar-Vidal
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
| | - Mario Esteban-Muñoz
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
| | - Jaime Esteban
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
| | - Ricardo Fernández-Roblas
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
| | - Ignacio Gadea
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
| | - Carmen Ayuso
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (P.M.); (L.d.l.F.); (M.C.); (R.L.-R.); (C.A.)
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Javier Ruíz-Hornillos
- Allergy Unit, Hospital Infanta Elena, Valdemoro, 28342 Madrid, Spain;
- Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain
- Faculty of Medicine, Universidad Francisco de Vitoria, 28223 Madrid, Spain
| | - Nuria Verdaguer
- Structural Biology Department, Institut de Biología Molecular de Barcelona CSIC, 08028 Barcelona, Spain; (C.F.-O.); (N.V.)
| | - Esteban Domingo
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain; (C.G.-C.); (P.S.); (A.D.-P.); (I.G.); (A.I.d.Á.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Celia Perales
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Av. Reyes Católicos 2, 28040 Madrid, Spain; (B.M.-G.); (M.E.S.); (L.V.-S.); (R.L.-V.); (L.S.-V.); (M.E.-M.); (J.E.); (R.F.-R.); (I.G.)
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Campus de Cantoblanco, 28049 Madrid, Spain;
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain;
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22
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Frutos R, Gavotte L, Devaux CA. Le virus SARS-CoV-2 n’a pas « d’origine ». Med Sci (Paris) 2022; 38:600-607. [DOI: 10.1051/medsci/2022079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Depuis le début de la pandémie de COVID-19, la question de l’origine de ce virus fait l’objet d’une vive polémique. C’est la question de « l’origine » qui est biaisée. Darwin a montré qu’il n’y a pas d’origine déterminée à aucune espèce animale ou végétale, simplement un processus évolutif et sélectif. Il en est de même pour les virus, il n’y a pas d’origine, mais un processus évolutif. Les virus circulent d’hôte à hôte, animaux ou humains. Les virus pandémiques circulent déjà chez l’homme et évoluent avant l’apparition d’une maladie. Ce processus évolutif se poursuit et donne naissance à des variants successifs. La solution n’est pas de cibler la maladie ou le possible agent causal, mais plutôt de cibler le processus d’émergence de la maladie lui-même.
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23
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Duncan NA, L'Her GF, Osborne AG, Sawyer SL, Deinert MR. Estimating the effect of non-pharmaceutical interventions on US SARS-CoV-2 infections in the first year of the pandemic. ROYAL SOCIETY OPEN SCIENCE 2022; 9:210875. [PMID: 35774134 PMCID: PMC9240671 DOI: 10.1098/rsos.210875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
SARS-CoV-2 emerged in late 2019 as a zoonotic infection of humans, and proceeded to cause a worldwide pandemic of historic magnitude. Here, we use a simple epidemiological model and consider the full range of initial estimates from published studies for infection and recovery rates, seasonality, changes in mobility, the effectiveness of masks and the fraction of people wearing them. Monte Carlo simulations are used to simulate the progression of possible pandemics and we show a match for the real progression of the pandemic during 2020 with an R 2 of 0.91. The results show that the combination of masks and changes in mobility avoided approximately 248.3 million (σ = 31.2 million) infections in the US before vaccinations became available.
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Affiliation(s)
- N. A. Duncan
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
| | - G. F. L'Her
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
| | - A. G. Osborne
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
| | - S. L. Sawyer
- Molecular Biology, University of Colorado at Boulder, Boulder, CO, USA
| | - M. R. Deinert
- Mechanical Engineering, The Colorado School of Mines, Golden, CO 10996, USA
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24
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Andréoletti J, Zwaans A, Warnock RCM, Aguirre-Fernández G, Barido-Sottani J, Gupta A, Stadler T, Manceau M. The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology. Syst Biol 2022; 71:1440-1452. [PMID: 35608305 PMCID: PMC9558841 DOI: 10.1093/sysbio/syac037] [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: 11/26/2021] [Revised: 05/02/2022] [Accepted: 05/06/2022] [Indexed: 11/28/2022] Open
Abstract
Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of “total-evidence” analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth–death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth–death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.]
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Affiliation(s)
- Jérémy Andréoletti
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Antoine Zwaans
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Rachel C M Warnock
- GeoZentrum Nordbayern,Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
| | | | - Joëlle Barido-Sottani
- Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, USA
| | - Ankit Gupta
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Marc Manceau
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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Abbasi BA, Saraf D, Sharma T, Sinha R, Singh S, Sood S, Gupta P, Gupta A, Mishra K, Kumari P, Rawal K. Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. PeerJ 2022; 10:e13380. [PMID: 35611169 PMCID: PMC9124463 DOI: 10.7717/peerj.13380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/13/2022] [Indexed: 01/13/2023] Open
Abstract
An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.
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Frutos R, Pliez O, Gavotte L, Devaux CA. There is no "origin" to SARS-CoV-2. ENVIRONMENTAL RESEARCH 2022; 207:112173. [PMID: 34626592 PMCID: PMC8493644 DOI: 10.1016/j.envres.2021.112173] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 05/04/2023]
Abstract
Since the beginning of the COVID-19 pandemic in 2020 caused by SARS-CoV-2, the question of the origin of this virus has been a highly debated issue. Debates have been, and are still, very disputed and often violent between the two main hypotheses: a natural origin through the "spillover" model or a laboratory-leak origin. Tenants of these two options are building arguments often based on the discrepancies of the other theory. The main problem is that it is the initial question of the origin itself which is biased. Charles Darwin demonstrated in 1859 that all species are appearing through a process of evolution, adaptation and selection. There is no determined origin to any animal or plant species, simply an evolutionary and selective process in which chance and environment play a key role. The very same is true for viruses. There is no determined origin to viruses, simply also an evolutionary and selective process in which chance and environment play a key role. However, in the case of viruses the process is slightly more complex because the "environment" is another living organism. Pandemic viruses already circulate in humans prior to the emergence of a disease. They are simply not capable of triggering an epidemic yet. They must evolve in-host, i.e. in-humans, for that. The evolutionary process which gave rise to SARS-CoV-2 is still ongoing with regular emergence of novel variants more adapted than the previous ones. The real relevant question is how these viruses can emerge as pandemic viruses and what the society can do to prevent the future emergence of pandemic viruses.
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Affiliation(s)
| | | | | | - Christian A Devaux
- MEPHI, Aix-Marseille Université, IRD, AP-HM, IHU-Méditerranée Infection, Marseille, France; CNRS, Marseille, France
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27
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Martínez-González B, Soria ME, Vázquez-Sirvent L, Ferrer-Orta C, Lobo-Vega R, Mínguez P, de la Fuente L, Llorens C, Soriano B, Ramos R, Cortón M, López-Rodríguez R, García-Crespo C, Gallego I, de Ávila AI, Gómez J, Enjuanes L, Salar-Vidal L, Esteban J, Fernandez-Roblas R, Gadea I, Ayuso C, Ruíz-Hornillos J, Verdaguer N, Domingo E, Perales C. SARS-CoV-2 Point Mutation and Deletion Spectra and Their Association with Different Disease Outcomes. Microbiol Spectr 2022; 10:e0022122. [PMID: 35348367 PMCID: PMC9045161 DOI: 10.1128/spectrum.00221-22] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/25/2022] [Indexed: 12/15/2022] Open
Abstract
Mutant spectra of RNA viruses are important to understand viral pathogenesis and response to selective pressures. There is a need to characterize the complexity of mutant spectra in coronaviruses sampled from infected patients. In particular, the possible relationship between SARS-CoV-2 mutant spectrum complexity and disease associations has not been established. In the present study, we report an ultradeep sequencing (UDS) analysis of the mutant spectrum of amplicons from the nsp12 (polymerase)- and spike (S)-coding regions of 30 nasopharyngeal isolates (diagnostic samples) of SARS-CoV-2 of the first COVID-19 pandemic wave (Madrid, Spain, April 2020) classified according to the severity of ensuing COVID-19. Low-frequency mutations and deletions, counted relative to the consensus sequence of the corresponding isolate, were overwhelmingly abundant. We show that the average number of different point mutations, mutations per haplotype, and several diversity indices was significantly higher in SARS-CoV-2 isolated from patients who developed mild disease than in those associated with moderate or severe disease (exitus). No such bias was observed with RNA deletions. Location of amino acid substitutions in the three-dimensional structures of nsp12 (polymerase) and S suggest significant structural or functional effects. Thus, patients who develop mild symptoms may be a richer source of genetic variants of SARS-CoV-2 than patients with moderate or severe COVID-19. IMPORTANCE The study shows that mutant spectra of SARS-CoV-2 from diagnostic samples differ in point mutation abundance and complexity and that significantly larger values were observed in virus from patients who developed mild COVID-19 symptoms. Mutant spectrum complexity is not a uniform trait among isolates. The nature and location of low-frequency amino acid substitutions present in mutant spectra anticipate great potential for phenotypic diversification of SARS-CoV-2.
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Affiliation(s)
- Brenda Martínez-González
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - María Eugenia Soria
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
| | - Lucía Vázquez-Sirvent
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Cristina Ferrer-Orta
- Structural Biology Department, Institut de Biología Molecular de Barcelona CSIC, Barcelona, Spain
| | - Rebeca Lobo-Vega
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Pablo Mínguez
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Bioinformatics Unit, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Lorena de la Fuente
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Bioinformatics Unit, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Carlos Llorens
- Biotechvana, “Scientific Park”, Universidad de Valencia, Valencia, Spain
| | - Beatriz Soriano
- Biotechvana, “Scientific Park”, Universidad de Valencia, Valencia, Spain
| | - Ricardo Ramos
- Unidad de Genómica, “Scientific Park of Madrid”, Campus de Cantoblanco, Madrid, Spain
| | - Marta Cortón
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Rosario López-Rodríguez
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Carlos García-Crespo
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
| | - Isabel Gallego
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Ana Isabel de Ávila
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
| | - Jordi Gómez
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Parasitología y Biomedicina ‘López-Neyra’ (CSIC), Parque Tecnológico Ciencias de la Salud, Granada, Spain
| | - Luis Enjuanes
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
| | - Llanos Salar-Vidal
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Jaime Esteban
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Ricardo Fernandez-Roblas
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Ignacio Gadea
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Carmen Ayuso
- Department of Genetics & Genomics, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Javier Ruíz-Hornillos
- Allergy Unit, Hospital Infanta Elena, Valdemoro, Madrid, Spain
- Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain
| | - Nuria Verdaguer
- Structural Biology Department, Institut de Biología Molecular de Barcelona CSIC, Barcelona, Spain
| | - Esteban Domingo
- Centro de Biología Molecular “Severo Ochoa” (CSIC-UAM), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Celia Perales
- Department of Clinical Microbiology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
- Department of Molecular and Cell Biology, Centro Nacional de Biotecnología (CNB-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
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Lai A, Bergna A, Toppo S, Morganti M, Menzo S, Ghisetti V, Bruzzone B, Codeluppi M, Fiore V, Rullo EV, Antonelli G, Sarmati L, Brindicci G, Callegaro A, Sagnelli C, Francisci D, Vicenti I, Miola A, Tonon G, Cirillo D, Menozzi I, Caucci S, Cerutti F, Orsi A, Schiavo R, Babudieri S, Nunnari G, Mastroianni CM, Andreoni M, Monno L, Guarneri D, Coppola N, Crisanti A, Galli M, Zehender G. Phylogeography and genomic epidemiology of SARS-CoV-2 in Italy and Europe with newly characterized Italian genomes between February-June 2020. Sci Rep 2022; 12:5736. [PMID: 35388091 PMCID: PMC8986836 DOI: 10.1038/s41598-022-09738-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 03/25/2022] [Indexed: 12/29/2022] Open
Abstract
The aims of this study were to characterize new SARS-CoV-2 genomes sampled all over Italy and to reconstruct the origin and the evolutionary dynamics in Italy and Europe between February and June 2020. The cluster analysis showed only small clusters including < 80 Italian isolates, while most of the Italian strains were intermixed in the whole tree. Pure Italian clusters were observed mainly after the lockdown and distancing measures were adopted. Lineage B and B.1 spread between late January and early February 2020, from China to Veneto and Lombardy, respectively. Lineage B.1.1 (20B) most probably evolved within Italy and spread from central to south Italian regions, and to European countries. The lineage B.1.1.1 (20D) developed most probably in other European countries entering Italy only in the second half of March and remained localized in Piedmont until June 2020. In conclusion, within the limitations of phylogeographical reconstruction, the estimated ancestral scenario suggests an important role of China and Italy in the widespread diffusion of the D614G variant in Europe in the early phase of the pandemic and more dispersed exchanges involving several European countries from the second half of March 2020.
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Affiliation(s)
- Alessia Lai
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy.,Pediatric Clinical Research Center Fondazione Romeo ed Enrica Invernizzi, University of Milan, Milan, Italy
| | - Annalisa Bergna
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy
| | - Stefano Toppo
- Department of Molecular Medicine, University of Padova, Padua, Italy.,CRIBI Biotech Center, University of Padova, Padua, Italy
| | - Marina Morganti
- Risk Analyses and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Parma, Italy
| | - Stefano Menzo
- Department of Biomedical Sciences and Public Health, Virology Unit, Polytechnic University of Marche, Ancona, Italy
| | - Valeria Ghisetti
- Laboratory of Microbiology and Virology, Amedeo di Savoia, ASL Città di Torino, Torino, Italy
| | | | - Mauro Codeluppi
- UOC of Infectious Diseases, Department of Oncology and Hematology, Guglielmo da Saliceto Hospital, AUSL Piacenza, Piacenza, Italy
| | - Vito Fiore
- Infectious and Tropical Disease Clinic, Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Emmanuele Venanzi Rullo
- Unit of Infectious Diseases, Department of Experimental and Clinical Medicine, University of Messina, Messina, Italy
| | - Guido Antonelli
- Department of Molecular Medicine, University Hospital Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | | | - Annapaola Callegaro
- Microbiology and Virology Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Caterina Sagnelli
- Department of Mental Health and Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Daniela Francisci
- Department of Medicine and Surgery, Clinic of Infectious Diseases, "Santa Maria della Misericordia" Hospital, University of Perugia, Perugia, Italy
| | - Ilaria Vicenti
- Department of Medical Biotechnologies, University of Siena, Siena, Italy
| | - Arianna Miola
- Intesa San Paolo Innovation Center-AI LAB, Turin, Italy
| | - Giovanni Tonon
- Center for Omics Sciences, IRCCS Ospedale San Raffaele, Milan, Italy.,Division of Experimental Oncology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Daniela Cirillo
- Division of Immunology, Transplantation and Infectious Disease, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ilaria Menozzi
- Risk Analyses and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna, Parma, Italy
| | - Sara Caucci
- Department of Biomedical Sciences and Public Health, Virology Unit, Polytechnic University of Marche, Ancona, Italy
| | - Francesco Cerutti
- Laboratory of Microbiology and Virology, Amedeo di Savoia, ASL Città di Torino, Torino, Italy
| | - Andrea Orsi
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Roberta Schiavo
- UOC of Microbiology, Department of Clinical Pathology, Guglielmo da Saliceto Hospital, AUSL Piacenza, Piacenza, Italy
| | - Sergio Babudieri
- Infectious and Tropical Disease Clinic, Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy
| | - Giuseppe Nunnari
- Unit of Infectious Diseases, Department of Experimental and Clinical Medicine, University of Messina, Messina, Italy
| | - Claudio M Mastroianni
- Department of Public Health and Infectious Diseases, University Hospital Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | | | - Laura Monno
- Infectious Diseases Unit, University of Bari, Bari, Italy
| | - Davide Guarneri
- Microbiology and Virology Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Nicola Coppola
- Department of Mental Health and Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Andrea Crisanti
- Microbiology and Virology Diagnostic Unit, Padua University Hospital, Padua, Italy.,Department of Life Science, Imperial College London, South Kensington Campus Imperial College Road, London, SW7 AZ, UK
| | - Massimo Galli
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy
| | - Gianguglielmo Zehender
- Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milan, Milan, Italy. .,Pediatric Clinical Research Center Fondazione Romeo ed Enrica Invernizzi, University of Milan, Milan, Italy. .,CRC-Coordinated Research Center "EpiSoMI", University of Milan, Milan, Italy.
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Analysis of Genomic Characteristics of SARS-CoV-2 in Italy, 29 January to 27 March 2020. Viruses 2022; 14:v14030472. [PMID: 35336879 PMCID: PMC8951147 DOI: 10.3390/v14030472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 02/01/2022] [Accepted: 02/09/2022] [Indexed: 12/14/2022] Open
Abstract
We performed next-generation sequencing (NGS), phylogenetic analysis, gene flows, and N- and O-glycosylation prediction on SARS-CoV-2 genomes collected from lab-confirmed cases from different Italian regions. To this end, a total of 111 SARS-CoV-2 genomes collected in Italy between 29 January and 27 March 2020 were investigated. The majority of the genomes belonged to lineage B.1, with some descendant lineages. The gene flow analysis showed that the spread occurred mainly from the north to the center and to the south of Italy, as confirmed by epidemiological data. The mean evolutionary rate estimated here was 8.731 × 10−4 (95% highest posterior density, HPD intervals 5.809 × 10−4 to 1.19 × 10−3), in line with values reported by other authors. The dated phylogeny suggested that SARS-CoV-2 lineage B.1 probably entered Italy between the end of January and early February 2020. Continuous molecular surveillance is needed to trace virus circulation and evolution.
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Qian Z, Li P, Tang X, Lu J. Evolutionary dynamics of the severe acute respiratory syndrome coronavirus 2 genomes. MEDICAL REVIEW (BERLIN, GERMANY) 2022; 2:3-22. [PMID: 35658106 PMCID: PMC9047652 DOI: 10.1515/mr-2021-0035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 01/23/2022] [Indexed: 12/27/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused immense losses in human lives and the global economy and posed significant challenges for global public health. As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, has evolved, thousands of single nucleotide variants (SNVs) have been identified across the viral genome. The roles of individual SNVs in the zoonotic origin, evolution, and transmission of SARS-CoV-2 have become the focus of many studies. This review summarizes recent comparative genomic analyses of SARS-CoV-2 and related coronaviruses (SC2r-CoVs) found in non-human animals, including delineation of SARS-CoV-2 lineages based on characteristic SNVs. We also discuss the current understanding of receptor-binding domain (RBD) evolution and characteristic mutations in variants of concern (VOCs) of SARS-CoV-2, as well as possible co-evolution between RBD and its receptor, angiotensin-converting enzyme 2 (ACE2). We propose that the interplay between SARS-CoV-2 and host RNA editing mechanisms might have partially resulted in the bias in nucleotide changes during SARS-CoV-2 evolution. Finally, we outline some current challenges, including difficulty in deciphering the complicated relationship between viral pathogenicity and infectivity of different variants, and monitoring transmission of SARS-CoV-2 between humans and animals as the pandemic progresses.
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Affiliation(s)
- Zhaohui Qian
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100871, China
| | - Pei Li
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100871, China
| | - Xiaolu Tang
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, 100176, China
| | - Jian Lu
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, 100176, China
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31
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Epidemiology and Genetic Analysis of SARS-CoV-2 in Myanmar during the Community Outbreaks in 2020. Viruses 2022; 14:v14020259. [PMID: 35215852 PMCID: PMC8875553 DOI: 10.3390/v14020259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/21/2022] [Accepted: 01/25/2022] [Indexed: 02/01/2023] Open
Abstract
We aimed to analyze the situation of the first two epidemic waves in Myanmar using the publicly available daily situation of COVID-19 and whole-genome sequencing data of SARS-CoV-2. From March 23 to December 31, 2020, there were 33,917 confirmed cases and 741 deaths in Myanmar (case fatality rate of 2.18%). The first wave in Myanmar from March to July was linked to overseas travel, and then a second wave started from Rakhine State, a western border state, leading to the second wave spreading countrywide in Myanmar from August to December 2020. The estimated effective reproductive number (Rt) nationwide reached 6–8 at the beginning of each wave and gradually decreased as the epidemic spread to the community. The whole-genome analysis of 10 Myanmar SARS-CoV-2 strains together with 31 previously registered strains showed that the first wave was caused by GISAID clade O or PANGOLIN lineage B.6 and the second wave was changed to clade GH or lineage B.1.36.16 with a close genetic relationship with other South Asian strains. Constant monitoring of epidemiological situations combined with SARS-CoV-2 genome analysis is important for adjusting public health measures to mitigate the community transmissions of COVID-19.
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32
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Brüningk SC, Klatt J, Stange M, Mari A, Brunner M, Roloff TC, Seth-Smith HMB, Schweitzer M, Leuzinger K, Søgaard KK, Albertos Torres D, Gensch A, Schlotterbeck AK, Nickel CH, Ritz N, Heininger U, Bielicki J, Rentsch K, Fuchs S, Bingisser R, Siegemund M, Pargger H, Ciardo D, Dubuis O, Buser A, Tschudin-Sutter S, Battegay M, Schneider-Sliwa R, Borgwardt KM, Hirsch HH, Egli A. OUP accepted manuscript. Virus Evol 2022; 8:veac002. [PMID: 35310621 PMCID: PMC8927799 DOI: 10.1093/ve/veac002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/07/2021] [Accepted: 02/16/2022] [Indexed: 11/21/2022] Open
Abstract
Transmission chains within small urban areas (accommodating ∼30 per cent of the European population) greatly contribute to case burden and economic impact during the ongoing coronavirus pandemic and should be a focus for preventive measures to achieve containment. Here, at very high spatio-temporal resolution, we analysed determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in a European urban area, Basel-City (Switzerland). We combined detailed epidemiological, intra-city mobility and socio-economic data sets with whole-genome sequencing during the first SARS-CoV-2 wave. For this, we succeeded in sequencing 44 per cent of all reported cases from Basel-City and performed phylogenetic clustering and compartmental modelling based on the dominating viral variant (B.1-C15324T; 60 per cent of cases) to identify drivers and patterns of transmission. Based on these results we simulated vaccination scenarios and corresponding healthcare system burden (intensive care unit (ICU) occupancy). Transmissions were driven by socio-economically weaker and highly mobile population groups with mostly cryptic transmissions which lacked genetic and identifiable epidemiological links. Amongst more senior population transmission was clustered. Simulated vaccination scenarios assuming 60–90 per cent transmission reduction and 70–90 per cent reduction of severe cases showed that prioritising mobile, socio-economically weaker populations for vaccination would effectively reduce case numbers. However, long-term ICU occupation would also be effectively reduced if senior population groups were prioritised, provided there were no changes in testing and prevention strategies. Reducing SARS-CoV-2 transmission through vaccination strongly depends on the efficacy of the deployed vaccine. A combined strategy of protecting risk groups by extensive testing coupled with vaccination of the drivers of transmission (i.e. highly mobile groups) would be most effective at reducing the spread of SARS-CoV-2 within an urban area.
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Montomoli E, Apolone G, Manenti A, Boeri M, Suatoni P, Sabia F, Marchianò A, Bollati V, Pastorino U, Sozzi G. Timeline of SARS-CoV-2 Spread in Italy: Results from an Independent Serological Retesting. Viruses 2021; 14:61. [PMID: 35062265 PMCID: PMC8778320 DOI: 10.3390/v14010061] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 12/25/2022] Open
Abstract
The massive emergence of COVID-19 cases in the first phase of pandemic within an extremely short period of time suggest that an undetected earlier circulation of SARS-CoV-2 might have occurred. Given the importance of this evidence, an independent evaluation was recommended by the World Health Organization (WHO) to test a subset of samples selected on the level of positivity in ELISA assays (positive, low positive, negative) detected in our previous study of prepandemic samples collected in Italy. SARS-CoV-2 antibodies were blindly retested by two independent centers in 29 blood samples collected in the prepandemic period in Italy, 29 samples collected one year before and 11 COVID-19 control samples. The methodologies used included IgG-RBD/IgM-RBD ELISA assays, a qualitative micro-neutralization CPE-based assay, a multiplex IgG protein array, an ELISA IgM kit (Wantai), and a plaque-reduction neutralization test. The results suggest the presence of SARS-CoV-2 antibodies in some samples collected in the prepandemic period, with the oldest samples found to be positive for IgM by both laboratories collected on 10 October 2019 (Lombardy), 11 November 2019 (Lombardy) and 5 February 2020 (Lazio), the latter with neutralizing antibodies. The detection of IgM and/or IgG binding and neutralizing antibodies was strongly dependent on the different serological assays and thresholds employed, and they were not detected in control samples collected one year before. These findings, although gathered in a small and selected set of samples, highlight the importance of harmonizing serological assays for testing the spread of the SARS-CoV-2 virus and may contribute to a better understanding of future virus dynamics.
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Affiliation(s)
- Emanuele Montomoli
- Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy;
- VisMederi S.r.l., 53200 Siena, Italy;
| | - Giovanni Apolone
- Scientific Direction, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy;
| | - Alessandro Manenti
- VisMederi S.r.l., 53200 Siena, Italy;
- VisMederi Research S.r.l., 53100 Siena, Italy
| | - Mattia Boeri
- Department of Research, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy;
| | - Paola Suatoni
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (P.S.); (F.S.)
| | - Federica Sabia
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (P.S.); (F.S.)
| | - Alfonso Marchianò
- Department of Radiology, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy;
| | - Valentina Bollati
- EPIGET-Epidemiology, Epigenetics and Toxicology Lab., University of Milan, 20100 Milan, Italy;
| | - Ugo Pastorino
- Department of Surgery, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy; (P.S.); (F.S.)
| | - Gabriella Sozzi
- Department of Research, Fondazione IRCCS Istituto Nazionale Tumori, 20133 Milan, Italy;
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Ye C, Thornlow B, Kramer A, McBroome J, Hinrichs A, Corbett-Detig R, Turakhia Y. Pandemic-scale phylogenetics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.12.03.470766. [PMID: 34927180 PMCID: PMC8679213 DOI: 10.1101/2021.12.03.470766] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Phylogenetics has been central to the genomic surveillance, epidemiology and contact tracing efforts during the COVD-19 pandemic. But the massive scale of genomic sequencing has rendered the pre-pandemic tools inadequate for comprehensive phylogenetic analyses. Here, we discuss the phylogenetic package that we developed to address the needs imposed by this pandemic. The package incorporates several pandemic-specific optimization and parallelization techniques and comprises four programs: UShER, matOptimize, RIPPLES and matUtils. Using high-performance computing, UShER and matOptimize maintain and refine daily a massive mutation-annotated phylogenetic tree consisting of all SARS-CoV-2 sequences available in online repositories. With UShER and RIPPLES, individual labs - even with modest compute resources - incorporate newly-sequenced SARS-CoV-2 genomes on this phylogeny and discover evidence for recombination in real-time. With matUtils, they rapidly query and visualize massive SARS-CoV-2 phylogenies. These tools have empowered scientists worldwide to study the SARS-CoV-2 evolution and transmission at an unprecedented scale, resolution and speed.
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Affiliation(s)
- Cheng Ye
- University of California, San Diego; San Diego, CA 92093, USA
| | - Bryan Thornlow
- University of California, Santa Cruz; Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
| | - Alexander Kramer
- University of California, Santa Cruz; Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
| | - Jakob McBroome
- 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
| | - Russell Corbett-Detig
- University of California, Santa Cruz; Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
| | - Yatish Turakhia
- University of California, San Diego; San Diego, CA 92093, USA
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Wolf JM, Kipper D, Borges GR, Streck AF, Lunge VR. Temporal spread and evolution of SARS-CoV-2 in the second pandemic wave in Brazil. J Med Virol 2021; 94:926-936. [PMID: 34596904 PMCID: PMC8661965 DOI: 10.1002/jmv.27371] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 12/11/2022]
Abstract
Severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) pandemic spread rapidly and this scenario is concerning in South America, mainly in Brazil that presented more than 21 million coronavirus disease 2019 cases and 590 000 deaths. The recent emergence of novel lineages carrying several mutations in the spike protein has raised additional public health concerns worldwide. The present study describes the temporal spreading and evolution of SARS‐CoV2 in the beginning of the second pandemic wave in Brazil, highlighting the fast dissemination of the two major concerning variants (P.1 and P.2). A total of 2507 SARS‐CoV‐2 whole‐genome sequences (WGSs) with available information from the country (Brazil) and sampling date (July 2020–February 2021), were obtained and the frequencies of the lineages were evaluated in the period of the growing second pandemic wave. The results demonstrated the increasing prevalence of P.1 and P.2 lineages in the period evaluated. P.2 lineage was first detected in the middle of 2020, but a high increase occurred only in the last trimester of this same year and the spreading to all Brazilian regions. P.1 lineage emerged even later, first in the North region in December 2020 and really fast dissemination to all other Brazilian regions in January and February 2021. All SARS‐CoV‐2 WGSs of P.1 and P.2 were further separately evaluated with a Bayesian approach. The rates of nucleotide and amino acid substitutions were statistically higher in P.1 than P.2 (p < 0.01). The phylodynamic analysis demonstrated that P.2 gradually spread in all the country from September 2020 to January 2021, while P.1 disseminated even faster from December 2020 to February 2021. Skyline plots of both lineages demonstrated a slight rise in the spreading for P.2 and exponential growth for P.1. In conclusion, these data demonstrated that the P.1 (recently renamed as Gamma) and P.2 lineages have predominated in the second pandemic wave due to the very high spreading across all geographic regions in Brazil at the end of 2020 and beginning of 2021. In Brazil, P.1 (Gamma) and P.2 lineages have predominated in the second pandemic wave. The Bayesian approach showed very high spreading for both lineages across all geographic regions at the end of 2020 and the beginning of 2021. P.2 increased only in the last trimester of 2020 and the spreading to all Brazilian regions. P.1 (Gamma) emerged even later with fast dissemination to all Brazilian regions in January and February 2021.
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Affiliation(s)
- Jonas M Wolf
- Laboratório de Diagnóstico Molecular, Programa de Pós-Graduação em Biologia Celular e Molecular Aplicada à Saúde, Universidade Luterana do Brasil, ULBRA, Canoas, Rio Grande do Sul, Brazil.,Laboratório de Diagnóstico Molecular, Universidade Luterana do Brasil, Canoas, Rio Grande do Sul, Brazil
| | - Diéssy Kipper
- Laboratório de Diagnóstico em Medicina Veterinária, Universidade de Caxias do Sul, UCS, Caxias do Sul, Rio Grande do Sul, Brazil
| | - Gabriela R Borges
- Laboratório de Diagnóstico Molecular, Universidade Luterana do Brasil, Canoas, Rio Grande do Sul, Brazil
| | - André F Streck
- Laboratório de Diagnóstico em Medicina Veterinária, Universidade de Caxias do Sul, UCS, Caxias do Sul, Rio Grande do Sul, Brazil
| | - Vagner R Lunge
- Laboratório de Diagnóstico Molecular, Programa de Pós-Graduação em Biologia Celular e Molecular Aplicada à Saúde, Universidade Luterana do Brasil, ULBRA, Canoas, Rio Grande do Sul, Brazil.,Laboratório de Diagnóstico Molecular, Universidade Luterana do Brasil, Canoas, Rio Grande do Sul, Brazil.,Simbios Biotecnologia, Cachoeirinha, Rio Grande do Sul, Brazil
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Early reports of epidemiological parameters of the COVID-19 pandemic. Western Pac Surveill Response J 2021; 12:65-81. [PMID: 34540315 PMCID: PMC8421745 DOI: 10.5365/wpsar.2020.11.3.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Background The emergence of a new pathogen requires a rapid assessment of its transmissibility, to inform appropriate public health interventions. Methods The peer-reviewed literature published between 1 January and 30 April 2020 on COVID-19 in PubMed was searched. Estimates of the incubation period, serial interval and reproduction number for COVID-19 were obtained and compared. Results A total of 86 studies met the inclusion criteria. Of these, 33 estimated the mean incubation period (4–7 days) and 15 included estimates of the serial interval (mean 4–8 days; median length 4–5 days). Fifty-two studies estimated the reproduction number. Although reproduction number estimates ranged from 0.3 to 14.8, in 33 studies (63%), they fell between 2 and 3. Discussion Studies calculating the incubation period and effective reproduction number were published from the beginning of the pandemic until the end of the study period (30 April 2020); however, most of the studies calculating the serial interval were published in April 2020. The calculated incubation period was similar over the study period and in different settings, whereas estimates of the serial interval and effective reproduction number were setting-specific. Estimates of the serial interval were shorter at the end of the study period as increasing evidence of pre-symptomatic transmission was documented and as jurisdictions enacted outbreak control measures. Estimates of the effective reproduction number varied with the setting and the underlying model assumptions. Early analysis of epidemic parameters provides vital information to inform the outbreak response.
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Zhu M, Kleepbua J, Guan Z, Chew SP, Tan JW, Shen J, Latthitham N, Hu J, Law JX, Li L. Early Spatiotemporal Patterns and Population Characteristics of the COVID-19 Pandemic in Southeast Asia. Healthcare (Basel) 2021; 9:1220. [PMID: 34574997 PMCID: PMC8466219 DOI: 10.3390/healthcare9091220] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/02/2021] [Accepted: 09/10/2021] [Indexed: 12/28/2022] Open
Abstract
This observational study aims to investigate the early disease patterns of coronavirus disease 2019 (COVID-19) in Southeast Asia, consequently providing historical experience for further interventions. Data were extracted from official websites of the WHO and health authorities of relevant countries. A total of 1346 confirmed cases of COVID-19, with 217 recoveries and 18 deaths, were reported in Southeast Asia as of 16 March 2020. The basic reproductive number (R0) of COVID-19 in the region was estimated as 2.51 (95% CI:2.31 to 2.73), and there were significant geographical variations at the subregional level. Early transmission dynamics were examined with an exponential regression model: y = 0.30e0.13x (p < 0.01, R2 = 0.96), which could help predict short-term incidence. Country-level disease burden was positively correlated with Human Development Index (r = 0.86, p < 0.01). A potential early shift in spatial diffusion patterns and a spatiotemporal cluster occurring in Malaysia and Singapore were detected. Demographic analyses of 925 confirmed cases indicated a median age of 44 years and a sex ratio (male/female) of 1.25. Age may play a significant role in both susceptibilities and outcomes. The COVID-19 situation in Southeast Asia is challenging and unevenly geographically distributed. Hence, enhanced real-time surveillance and more efficient resource allocation are urgently needed.
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Affiliation(s)
- Mingjian Zhu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; (M.Z.); (Z.G.); (J.S.)
| | - Jirapat Kleepbua
- Thammasat University Hospital, Pathum Thani 12120, Thailand; (J.K.); (N.L.)
| | - Zhou Guan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; (M.Z.); (Z.G.); (J.S.)
| | - Sien Ping Chew
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China;
| | - Joanna Weihui Tan
- Faculty of Arts and Social Sciences, National University of Singapore, Singapore 117570, Singapore;
| | - Jian Shen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; (M.Z.); (Z.G.); (J.S.)
| | | | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China;
| | - Jia Xian Law
- Tuanku Ja’afar Hospital, Seremban 70300, Malaysia;
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; (M.Z.); (Z.G.); (J.S.)
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Nemira A, Adeniyi AE, Gasich EL, Bulda KY, Valentovich LN, Krasko AG, Glebova O, Kirpich A, Skums P. SARS-CoV-2 transmission dynamics in Belarus in 2020 revealed by genomic and incidence data analysis. COMMUNICATIONS MEDICINE 2021; 1:31. [PMID: 35602211 PMCID: PMC9053244 DOI: 10.1038/s43856-021-00031-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/25/2021] [Indexed: 12/15/2022] Open
Abstract
Background Non-pharmaceutical interventions (NPIs) have been implemented worldwide to curb COVID-19 spread. Belarus is a rare case of a country with a relatively modern healthcare system, where highly limited NPIs have been enacted. Thus, investigation of Belarusian COVID-19 dynamics is essential for the local and global assessment of the impact of NPI strategies. Methods We integrate genomic epidemiology and surveillance methods to investigate the spread of SARS-CoV-2 in Belarus in 2020. We utilize phylodynamics, phylogeography, and probabilistic bias inference to study the virus import and export routes, the dynamics of the effective reproduction number, and the incidence of SARS-CoV-2 infection. Results Here we show that the estimated cumulative number of infections by June 2020 exceeds the confirmed case number by a factor of ~4 (95% confidence interval (2; 9)). Intra-country SARS-CoV-2 genomic diversity originates from at least 18 introductions from different regions, with a high proportion of regional transmissions. Phylodynamic analysis indicates a moderate reduction of the effective reproductive number after the introduction of limited NPIs, but its magnitude is lower than for developed countries with large-scale NPIs. On the other hand, the effective reproduction number estimate is comparable with that for the neighboring Ukraine, where NPIs were broader. Conclusions The example of Belarus demonstrates how countries with relatively low outward population mobility continue to be integral parts of the global epidemiological environment. Comparison of the effective reproduction number dynamics for Belarus and other countries reveals the effect of different NPI strategies but also emphasizes the role of regional Eastern European sociodemographic factors in the virus spread. Belarus is one of few European countries that has enacted limited measures to contain SARS-CoV-2, the virus that causes COVID-19. We study the genetic sequences of the SARS-CoV-2 virus circulating in Belarus and other countries in 2020 to investigate how it might have been imported into the country and spread there. We show that the virus was repeatedly imported from and exported to different regions, including a large portion of regional transmissions that occurred despite stricter measures implemented by Belarus’ neighbors. There was a moderate reduction of the virus reproductive number—a measure of virus transmission speed—after April 2020, but its magnitude was lower than for developed countries with more stringent epidemiological interventions. These findings shed light on the COVID-19 spread in Eastern Europe and highlight the impact of public health policies and of regional factors on this spread. Nemira et al. study the genomic epidemiology and phylodynamics of SARS-CoV-2 in Belarus. They identify potential introduction routes of the virus from other countries, determine that during the first wave of the pandemic the number of infections was likely several times higher than reported case numbers, and estimate the impact of early non-pharmaceutical interventions on SARS-CoV-2 transmission.
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Xylogiannopoulos KF, Karampelas P, Alhajj R. COVID-19 pandemic spread against countries' non-pharmaceutical interventions responses: a data-mining driven comparative study. BMC Public Health 2021; 21:1607. [PMID: 34470630 PMCID: PMC8409702 DOI: 10.1186/s12889-021-11251-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 06/10/2021] [Indexed: 12/24/2022] Open
Abstract
Background The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020. Methods Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans. Results The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease. Conclusions The conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-11251-4.
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Affiliation(s)
| | | | - Reda Alhajj
- Department of Computer Science, University of Calgary, Calgary, Alberta, Canada. .,Department of Health Informatics, University of Southern Denmark, Odense, Denmark.
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Louca S, McLaughlin A, MacPherson A, Joy JB, Pennell MW. Fundamental Identifiability Limits in Molecular Epidemiology. Mol Biol Evol 2021; 38:4010-4024. [PMID: 34009339 PMCID: PMC8382926 DOI: 10.1093/molbev/msab149] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Viral phylogenies provide crucial information on the spread of infectious diseases, and many studies fit mathematical models to phylogenetic data to estimate epidemiological parameters such as the effective reproduction ratio (Re) over time. Such phylodynamic inferences often complement or even substitute for conventional surveillance data, particularly when sampling is poor or delayed. It remains generally unknown, however, how robust phylodynamic epidemiological inferences are, especially when there is uncertainty regarding pathogen prevalence and sampling intensity. Here, we use recently developed mathematical techniques to fully characterize the information that can possibly be extracted from serially collected viral phylogenetic data, in the context of the commonly used birth-death-sampling model. We show that for any candidate epidemiological scenario, there exists a myriad of alternative, markedly different, and yet plausible "congruent" scenarios that cannot be distinguished using phylogenetic data alone, no matter how large the data set. In the absence of strong constraints or rate priors across the entire study period, neither maximum-likelihood fitting nor Bayesian inference can reliably reconstruct the true epidemiological dynamics from phylogenetic data alone; rather, estimators can only converge to the "congruence class" of the true dynamics. We propose concrete and feasible strategies for making more robust epidemiological inferences from viral phylogenetic data.
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Affiliation(s)
- Stilianos Louca
- Department of Biology, University of Oregon, Eugene, OR, USA
- Institute of Ecology and Evolution, University of Oregon, Eugene, OR, USA
| | - Angela McLaughlin
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
- Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Ailene MacPherson
- Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
- Department of Zoology, University of British Columbia, Vancouver, BC, Canada
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada
| | - Jeffrey B Joy
- British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
- Bioinformatics, University of British Columbia, Vancouver, BC, Canada
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Matthew W Pennell
- Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
- Department of Zoology, University of British Columbia, Vancouver, BC, Canada
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Kumar S, Tao Q, Weaver S, Sanderford M, Caraballo-Ortiz MA, Sharma S, Pond SLK, Miura S. An Evolutionary Portrait of the Progenitor SARS-CoV-2 and Its Dominant Offshoots in COVID-19 Pandemic. Mol Biol Evol 2021; 38:3046-3059. [PMID: 33942847 PMCID: PMC8135569 DOI: 10.1093/molbev/msab118] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Global sequencing of genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to reveal new genetic variants that are the key to unraveling its early evolutionary history and tracking its global spread over time. Here we present the heretofore cryptic mutational history and spatiotemporal dynamics of SARS-CoV-2 from an analysis of thousands of high-quality genomes. We report the likely most recent common ancestor of SARS-CoV-2, reconstructed through a novel application and advancement of computational methods initially developed to infer the mutational history of tumor cells in a patient. This progenitor genome differs from genomes of the first coronaviruses sampled in China by three variants, implying that none of the earliest patients represent the index case or gave rise to all the human infections. However, multiple coronavirus infections in China and the United States harbored the progenitor genetic fingerprint in January 2020 and later, suggesting that the progenitor was spreading worldwide months before and after the first reported cases of COVID-19 in China. Mutations of the progenitor and its offshoots have produced many dominant coronavirus strains that have spread episodically over time. Fingerprinting based on common mutations reveals that the same coronavirus lineage has dominated North America for most of the pandemic in 2020. There have been multiple replacements of predominant coronavirus strains in Europe and Asia as well as continued presence of multiple high-frequency strains in Asia and North America. We have developed a continually updating dashboard of global evolution and spatiotemporal trends of SARS-CoV-2 spread (http://sars2evo.datamonkey.org/).
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Affiliation(s)
- Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
- Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Qiqing Tao
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Steven Weaver
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Marcos A Caraballo-Ortiz
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Sudip Sharma
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Sergei L K Pond
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA
- Department of Biology, Temple University, Philadelphia, PA, USA
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Abstract
The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.
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Affiliation(s)
- Gilberto Gonzalez-Parra
- Department of Mathematics, New Mexico Tech, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
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Crellen T, Pi L, Davis EL, Pollington TM, Lucas TCD, Ayabina D, Borlase A, Toor J, Prem K, Medley GF, Klepac P, Déirdre Hollingsworth T. Dynamics of SARS-CoV-2 with waning immunity in the UK population. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200274. [PMID: 34053264 PMCID: PMC8165597 DOI: 10.1098/rstb.2020.0274] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2021] [Indexed: 12/15/2022] Open
Abstract
The dynamics of immunity are crucial to understanding the long-term patterns of the SARS-CoV-2 pandemic. Several cases of reinfection with SARS-CoV-2 have been documented 48-142 days after the initial infection and immunity to seasonal circulating coronaviruses is estimated to be shorter than 1 year. Using an age-structured, deterministic model, we explore potential immunity dynamics using contact data from the UK population. In the scenario where immunity to SARS-CoV-2 lasts an average of three months for non-hospitalized individuals, a year for hospitalized individuals, and the effective reproduction number after lockdown ends is 1.2 (our worst-case scenario), we find that the secondary peak occurs in winter 2020 with a daily maximum of 387 000 infectious individuals and 125 000 daily new cases; threefold greater than in a scenario with permanent immunity. Our models suggest that longitudinal serological surveys to determine if immunity in the population is waning will be most informative when sampling takes place from the end of the lockdown in June until autumn 2020. After this period, the proportion of the population with antibodies to SARS-CoV-2 is expected to increase due to the secondary wave. Overall, our analysis presents considerations for policy makers on the longer-term dynamics of SARS-CoV-2 in the UK and suggests that strategies designed to achieve herd immunity may lead to repeated waves of infection as immunity to reinfection is not permanent. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Thomas Crellen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Li Pi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Emma L. Davis
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Timothy M. Pollington
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
- MathSys CDT, University of Warwick, Coventry CV4 7AL, UK
| | - Tim C. D. Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Diepreye Ayabina
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Anna Borlase
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
| | - Kiesha Prem
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Graham F. Medley
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Petra Klepac
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - T. Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK
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44
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Serwin K, Ossowski A, Szargut M, Cytacka S, Urbańska A, Majchrzak A, Niedźwiedź A, Czerska E, Pawińska-Matecka A, Gołąb J, Parczewski M. Molecular Evolution and Epidemiological Characteristics of SARS COV-2 in (Northwestern) Poland. Viruses 2021; 13:1295. [PMID: 34372500 PMCID: PMC8310356 DOI: 10.3390/v13071295] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/23/2021] [Accepted: 06/29/2021] [Indexed: 12/30/2022] Open
Abstract
The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) evolved into a worldwide outbreak, with the first Polish cases in February/March 2020. This study aimed to investigate the molecular epidemiology of the circulating virus lineages between March 2020 and February 2021. We performed variant identification, spike mutation pattern analysis, and phylogenetic and evolutionary analyses for 1106 high-coverage whole-genome sequences, implementing maximum likelihood, multiple continuous-time Markov chain, and Bayesian birth-death skyline models. For time trends, logistic regression was used. In the dataset, virus B.1.221 lineage was predominant (15.37%), followed by B.1.258 (15.01%) and B.1.1.29 (11.48%) strains. Three clades were identified, being responsible for 74.41% of infections over the analyzed period. Expansion in variant diversity was observed since September 2020 with increasing frequency of the number in spike substitutions, mainly H69V70 deletion, P681H, N439K, and S98F. In population dynamics inferences, three periods with exponential increase in infection were observed, beginning in March, July, and September 2020, respectively, and were driven by different virus clades. Additionally, a notable increase in infections caused by the B.1.1.7 lineage since February 2021 was noted. Over time, the virus accumulated mutations related to optimized transmissibility; therefore, faster dissemination is reflected by the second wave of epidemics in Poland.
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Affiliation(s)
- Karol Serwin
- Department of Infectious, Tropical Diseases and Immune Deficiency, Pomeranian Medical University in Szczecin, 71-455 Szczecin, Poland; (K.S.); (A.U.)
| | - Andrzej Ossowski
- Department of Forensic Medicine, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland; (A.O.); (M.S.); (S.C.)
| | - Maria Szargut
- Department of Forensic Medicine, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland; (A.O.); (M.S.); (S.C.)
| | - Sandra Cytacka
- Department of Forensic Medicine, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland; (A.O.); (M.S.); (S.C.)
| | - Anna Urbańska
- Department of Infectious, Tropical Diseases and Immune Deficiency, Pomeranian Medical University in Szczecin, 71-455 Szczecin, Poland; (K.S.); (A.U.)
| | - Adam Majchrzak
- Independent Public Regional Hospital in Szczecin, 71-455 Szczecin, Poland; (A.M.); (A.N.); (E.C.); (A.P.-M.); (J.G.)
| | - Anna Niedźwiedź
- Independent Public Regional Hospital in Szczecin, 71-455 Szczecin, Poland; (A.M.); (A.N.); (E.C.); (A.P.-M.); (J.G.)
| | - Ewa Czerska
- Independent Public Regional Hospital in Szczecin, 71-455 Szczecin, Poland; (A.M.); (A.N.); (E.C.); (A.P.-M.); (J.G.)
| | - Anna Pawińska-Matecka
- Independent Public Regional Hospital in Szczecin, 71-455 Szczecin, Poland; (A.M.); (A.N.); (E.C.); (A.P.-M.); (J.G.)
| | - Joanna Gołąb
- Independent Public Regional Hospital in Szczecin, 71-455 Szczecin, Poland; (A.M.); (A.N.); (E.C.); (A.P.-M.); (J.G.)
| | - Miłosz Parczewski
- Department of Infectious, Tropical Diseases and Immune Deficiency, Pomeranian Medical University in Szczecin, 71-455 Szczecin, Poland; (K.S.); (A.U.)
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45
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Milano M, Zucco C, Cannataro M. COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data. ACTA ACUST UNITED AC 2021; 10:46. [PMID: 34249598 PMCID: PMC8253246 DOI: 10.1007/s13721-021-00323-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/21/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022]
Abstract
Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D.
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Affiliation(s)
- Marianna Milano
- Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.,Data Analytics Research Center, University of Catanzaro, Catanzaro, Catanzaro, 88100 Italy
| | - Chiara Zucco
- Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.,Data Analytics Research Center, University of Catanzaro, Catanzaro, Catanzaro, 88100 Italy
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.,Data Analytics Research Center, University of Catanzaro, Catanzaro, Catanzaro, 88100 Italy
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46
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Dhama K, Patel SK, Kumar R, Masand R, Rana J, Yatoo MI, Tiwari R, Sharun K, Mohapatra RK, Natesan S, Dhawan M, Ahmad T, Emran TB, Malik YS, Harapan H. The role of disinfectants and sanitizers during COVID-19 pandemic: advantages and deleterious effects on humans and the environment. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:34211-34228. [PMID: 33991301 PMCID: PMC8122186 DOI: 10.1007/s11356-021-14429-w] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/11/2021] [Indexed: 04/16/2023]
Abstract
Disinfectants and sanitizers are essential preventive agents against the coronavirus disease 2019 (COVID-19) pandemic; however, the pandemic crisis was marred by undue hype, which led to the indiscriminate use of disinfectants and sanitizers. Despite demonstrating a beneficial role in the control and prevention of COVID-19, there are crucial concerns regarding the large-scale use of disinfectants and sanitizers, including the side effects on human and animal health along with harmful impacts exerted on the environment and ecological balance. This article discusses the roles of disinfectants and sanitizers in the control and prevention of the current pandemic and highlights updated disinfection techniques against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This article provides evidence of the deleterious effects of disinfectants and sanitizers exerted on humans, animals, and the environment as well as suggests mitigation strategies to reduce these effects. Additionally, potential technologies and approaches for the reduction of these effects and the development of safe, affordable, and effective disinfectants are discussed, particularly, eco-friendly technologies using nanotechnology and nanomedicine.
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Affiliation(s)
- Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243122, India.
| | - Shailesh Kumar Patel
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243122, India
| | - Rakesh Kumar
- Department of Veterinary Pathology, Dr. G.C Negi College of Veterinary and Animal Sciences, CSK Himachal Pradesh Agricultural University, Palampur, Himachal Pradesh, 176062, India
| | - Rupali Masand
- Department of Veterinary Pathology, Dr. G.C Negi College of Veterinary and Animal Sciences, CSK Himachal Pradesh Agricultural University, Palampur, Himachal Pradesh, 176062, India
| | - Jigyasa Rana
- Department of Veterinary Anatomy, Faculty of Veterinary and Animal Sciences, Rajeev Gandhi South Campus, Banaras Hindu University, Barkachha, Mirzapur, Uttar Pradesh, 231001, India
| | - Mohd Iqbal Yatoo
- Division of Veterinary Clinical Complex, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, Alusteng Srinagar, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir, 190006, India
| | - Ruchi Tiwari
- Department of Veterinary Microbiology and Immunology, College of Veterinary Sciences, Uttar Pradesh Pandit Deen Dayal Upadhyaya Pashu Chikitsa Vigyan Vishwavidyalaya Evam Go Anusandhan Sansthan (DUVASU), Mathura, 281001, India
| | - Khan Sharun
- Division of Surgery, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243122, India
| | - Ranjan K Mohapatra
- Department of Chemistry, Government College of Engineering, Keonjhar, Odisha, 758002, India
| | - Senthilkumar Natesan
- Indian Institute of Public Health Gandhinagar, Lekawada, Gandhinagar, Gujarat, 382042, India
| | - Manish Dhawan
- Department of Microbiology, Punjab Agricultural University, Ludhiana, 141004, India
- The Trafford Group of Colleges, Manchester, WA14 5PQ, UK
| | - Tauseef Ahmad
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, 210009, China
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China
| | - Talha Bin Emran
- Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, 4381, Bangladesh
| | - Yashpal Singh Malik
- Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243122, India
| | - Harapan Harapan
- Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.
- Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.
- Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.
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47
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Douglas J, Mendes FK, Bouckaert R, Xie D, Jiménez-Silva CL, Swanepoel C, de Ligt J, Ren X, Storey M, Hadfield J, Simpson CR, Geoghegan JL, Drummond AJ, Welch D. Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations. Virus Evol 2021; 7:veab052. [PMID: 34527282 PMCID: PMC8344840 DOI: 10.1093/ve/veab052] [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: 04/12/2021] [Revised: 05/14/2021] [Accepted: 06/07/2021] [Indexed: 01/07/2023] Open
Abstract
New Zealand, Australia, Iceland, and Taiwan all saw success in controlling their first waves of Coronavirus Disease 2019 (COVID-19). As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. We employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of Severe acute respiratory syndrome coronavirus 2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contact tracing strategies. We estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country and that introductions slowed down markedly following the reduction of international travel in mid-March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. We have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics and for contact tracing.
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Affiliation(s)
| | | | - Remco Bouckaert
- Centre for Computational Evolution, The University of Auckland, Auckland 1010, New Zealand,School of Computer Science, The University of Auckland, Auckland 1010, New Zealand
| | - Dong Xie
- Centre for Computational Evolution, The University of Auckland, Auckland 1010, New Zealand,School of Computer Science, The University of Auckland, Auckland 1010, New Zealand
| | - Cinthy L Jiménez-Silva
- Centre for Computational Evolution, The University of Auckland, Auckland 1010, New Zealand,School of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand
| | - Christiaan Swanepoel
- Centre for Computational Evolution, The University of Auckland, Auckland 1010, New Zealand,School of Computer Science, The University of Auckland, Auckland 1010, New Zealand
| | - Joep de Ligt
- Institute of Environmental Science and Research Limited (ESR), Poriua 5420, New Zealand
| | - Xiaoyun Ren
- Institute of Environmental Science and Research Limited (ESR), Poriua 5420, New Zealand
| | - Matt Storey
- Institute of Environmental Science and Research Limited (ESR), Poriua 5420, New Zealand
| | - James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington WA 98109-1024, USA
| | - Colin R Simpson
- School of Health, Victoria University of Wellington, Wellington 6012, New Zealand
| | | | - Alexei J Drummond
- Centre for Computational Evolution, The University of Auckland, Auckland 1010, New Zealand,School of Computer Science, The University of Auckland, Auckland 1010, New Zealand,School of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand
| | - David Welch
- Centre for Computational Evolution, The University of Auckland, Auckland 1010, New Zealand,School of Computer Science, The University of Auckland, Auckland 1010, New Zealand
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48
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Martínez-Rodríguez D, Gonzalez-Parra G, Villanueva RJ. Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach. EPIDEMIOLOGIA 2021; 2:140-161. [PMID: 35141702 PMCID: PMC8824484 DOI: 10.3390/epidemiologia2020012] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/25/2021] [Indexed: 02/07/2023] Open
Abstract
The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.
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Affiliation(s)
- David Martínez-Rodríguez
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
| | | | - Rafael-J. Villanueva
- Insituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain; (D.M.-R.); (R.-J.V.)
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49
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ZIZZA ANTONELLA, RECCHIA VIRGINIA, ALOISI ALESSANDRA, GUIDO MARCELLO. Clinical features of COVID-19 and SARS epidemics. A literature review. JOURNAL OF PREVENTIVE MEDICINE AND HYGIENE 2021; 62:E13-E24. [PMID: 34322612 PMCID: PMC8283653 DOI: 10.15167/2421-4248/jpmh2021.62.1.1680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 02/03/2021] [Indexed: 01/08/2023]
Abstract
SARS-CoV-2, responsible for the current pandemic, is a novel strain of the Coronaviridae family, which has infected humans as a result of the leap to a new species. It causes an atypical pneumonia similar to that caused by SARS-CoV in 2003. SARS-CoV-2 has currently infected more than 9,200,000 people and caused almost 480,000 deaths worldwide. Although SARS-CoV-2 and SARS-CoV have similar phylogenetic and pathogenetic characteristics, they show important differences in clinical manifestations. We have reviewed the recent literature comparing the characteristics of the two epidemics and highlight their peculiar aspects. An analysis of all signs and symptoms of 3,365 SARS patients and 23,280 COVID-19 patients as well as of the comorbidities has been carried out. A total of 17 and 75 studies regarding patients with SARS and COVID-19, respectively, were included in the analysis. The analysis revealed an overlap of some symptoms between the two infections. Unlike SARS patients, COVID-19 patients have developed respiratory, neurological and gastrointestinal symptoms, and, in a limited number of subjects, symptoms involving organs such as skin and subcutaneous tissue, kidneys, cardiovascular system, liver and eyes. This analysis was conducted in order to direct towards an early identification of the infection, a suitable diagnostic procedure and the adoption of appropriate containment measures.
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Affiliation(s)
- ANTONELLA ZIZZA
- Institute of Clinical Physiology (IFC), National Research Council (CNR), Lecce, Italy
- Correspondence: Antonella Zizza, National Research Council, Institute of Clinical Physiology, Campus Ecotekne, via Monteroni, Lecce, Italy - E-mail:
| | - VIRGINIA RECCHIA
- Institute of Clinical Physiology (IFC), National Research Council (CNR), Lecce, Italy
| | - ALESSANDRA ALOISI
- Institute for Microelectronics and Microsystems (IMM), National Research Council (CNR), Lecce, Italy
| | - MARCELLO GUIDO
- Laboratory of Hygiene, Department of Biological and Environmental Sciences and Technologies, Faculty of Sciences, University of Salento, Lecce, Italy - Inter-University Centre of Research on Influenza and other Transmissible Infections (CIRI-IT), Genoa, Italy
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50
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Nemira A, Adeniyi AE, Gasich EL, Bulda KY, Valentovich LN, Krasko AG, Glebova O, Kirpich A, Skums P. SARS-CoV-2 transmission dynamics in Belarus revealed by genomic and incidence data analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.04.13.21255404. [PMID: 33907756 PMCID: PMC8077579 DOI: 10.1101/2021.04.13.21255404] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Since the emergence of COVID-19, a series of non-pharmaceutical interventions (NPIs) has been implemented by governments and public health authorities world-wide to control and curb the ongoing pandemic spread. From that perspective, Belarus is one of a few countries with a relatively modern healthcare system, where much narrower NPIs have been put in place. Given the uniqueness of this Belarusian experience, the understanding its COVID-19 epidemiological dynamics is essential not only for the local assessment, but also for a better insight into the impact of different NPI strategies globally. In this work, we integrate genomic epidemiology and surveillance methods to investigate the emergence and spread of SARS-CoV-2 in the country. The observed Belarusian SARS-CoV-2 genetic diversity originated from at least eighteen separate introductions, at least five of which resulted in on-going domestic transmissions. The introduction sources represent a wide variety of regions, although the proportion of regional virus introductions and exports from/to geographical neighbors appears to be higher than for other European countries. Phylodynamic analysis indicates a moderate reduction in the effective reproductive number ℛ e after the introduction of limited NPIs, with the reduction magnitude generally being lower than for countries with large-scale NPIs. On the other hand, the estimate of the Belarusian ℛ e at the early epidemic stage is comparable with this number for the neighboring ex-USSR country of Ukraine, where much broader NPIs have been implemented. The actual number of cases by the end of May, 2020 was predicted to be 2-9 times higher than the detected number of cases.
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Affiliation(s)
- Alina Nemira
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | | | - Elena L. Gasich
- Republican Research and Practical Center for Epidemiology and Microbiology, Minsk, Belarus
| | - Kirill Y. Bulda
- Republican Research and Practical Center for Epidemiology and Microbiology, Minsk, Belarus
| | | | - Anatoly G. Krasko
- Republican Research and Practical Center for Epidemiology and Microbiology, Minsk, Belarus
| | - Olga Glebova
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Alexander Kirpich
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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