1
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Smith TP, Mishra S, Dorigatti I, Dixit MK, Tristem M, Pearse WD. Differential responses of SARS-CoV-2 variants to environmental drivers during their selective sweeps. Sci Rep 2024; 14:13326. [PMID: 38858479 PMCID: PMC11164892 DOI: 10.1038/s41598-024-64044-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 06/04/2024] [Indexed: 06/12/2024] Open
Abstract
Previous work has shown that environmental variables affect SARS-CoV-2 transmission, but it is unclear whether different strains show similar environmental responses. Here we leverage genetic data on the transmission of three (Alpha, Delta and Omicron BA.1) variants of SARS-CoV-2 throughout England, to unpick the roles that climate and public-health interventions play in the circulation of this virus. We find evidence for enhanced transmission of the virus in colder conditions in the first variant selective sweep (of Alpha, in winter), but limited evidence of an impact of climate in either the second (of Delta, in the summer, when vaccines were prevalent) or third sweep (of Omicron, in the winter, during a successful booster-vaccination campaign). We argue that the results for Alpha are to be expected if the impact of climate is non-linear: we find evidence of an asymptotic impact of temperature on the alpha variant transmission rate. That is, at lower temperatures, the influence of temperature on transmission is much higher than at warmer temperatures. As with the initial spread of SARS-CoV-2, however, the overwhelming majority of variation in disease transmission is explained by the intrinsic biology of the virus and public-health mitigation measures. Specifically, when vaccination rates are high, a major driver of the spread of a new variant is it's ability to evade immunity, and any climate effects are secondary (as evidenced for Delta and Omicron). Climate alone cannot describe the transmission dynamics of emerging SARS-CoV-2 variants.
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Affiliation(s)
- Thomas P Smith
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK.
| | - Swapnil Mishra
- Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore and National University Health System, 12 Science Dr 2, Singapore, 117549, Singapore
| | - Ilaria Dorigatti
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, 90 Wood Lane, London, W12 OBZ, UK
| | - Mahika K Dixit
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
| | - Michael Tristem
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
| | - William D Pearse
- Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK
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2
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Khurana MP, Scheidwasser-Clow N, Penn MJ, Bhatt S, Duchêne DA. The Limits of the Constant-rate Birth-Death Prior for Phylogenetic Tree Topology Inference. Syst Biol 2024; 73:235-246. [PMID: 38153910 PMCID: PMC11129600 DOI: 10.1093/sysbio/syad075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/20/2023] [Accepted: 12/27/2023] [Indexed: 12/30/2023] Open
Abstract
Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under the constant-rate birth-death (crBD) model tend to differ from empirical trees, for example, with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between the crBD model and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which the crBD model differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD model trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used Bayesian methods and crBD priors with those that used other non-crBD priors and non-Bayesian methods (i.e., maximum likelihood methods), we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using the crBD model in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios but also highlight that empirically observed phylogenetic imbalance is highly improbable under the crBD model, leading to systematic bias in data sets with limited information content.
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Affiliation(s)
- Mark P Khurana
- Section of Epidemiology, Department of Public Health, University of Copenhagen, 1352 Copenhagen, Denmark
| | - Neil Scheidwasser-Clow
- Section of Epidemiology, Department of Public Health, University of Copenhagen, 1352 Copenhagen, Denmark
| | - Matthew J Penn
- Department of Statistics, University of Oxford, OX1 3LB, Oxford, UK
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, 1352 Copenhagen, Denmark
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, SW7 2AZ, London, UK
| | - David A Duchêne
- Centre for Evolutionary Hologenomics, University of Copenhagen, 1352 Copenhagen, Denmark
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3
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Yi B, Patrasová E, Šimůnková L, Rost F, Winkler S, Laubner A, Reinhardt S, Dahl A, Dalpke AH. Investigating the cause of a 2021 winter wave of COVID-19 in a border region in eastern Germany: a mixed-methods study, August to November 2021. Epidemiol Infect 2024; 152:e87. [PMID: 38751220 PMCID: PMC11149030 DOI: 10.1017/s0950268824000761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/15/2024] [Accepted: 04/24/2024] [Indexed: 05/31/2024] Open
Abstract
It is so far unclear how the COVID-19 winter waves started and what should be done to prevent possible future waves. In this study, we deciphered the dynamic course of a winter wave in 2021 in Saxony, a state in Eastern Germany neighbouring the Czech Republic and Poland. The study was carried out through the integration of multiple virus genomic epidemiology approaches to track transmission chains, identify emerging variants and investigate dynamic changes in transmission clusters. For identified local variants of interest, functional evaluations were performed. Multiple long-lasting community transmission clusters have been identified acting as driving force for the winter wave 2021. Analysis of the dynamic courses of two representative clusters indicated a similar transmission pattern. However, the transmission cluster caused by a locally occurring new Delta variant AY.36.1 showed a distinct transmission pattern, and functional analyses revealed a replication advantage of it. This study indicated that long-lasting community transmission clusters starting since early autumn caused by imported or locally occurring variants all contributed to the development of the 2021 winter wave. The information we achieved might help future pandemic prevention.
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Affiliation(s)
- Buqing Yi
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Eva Patrasová
- Department of Epidemiology, Regional Public Health Authority for Ustecky Kraj, Ústí nad Labem, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Lenka Šimůnková
- Department of Epidemiology, Regional Public Health Authority for Ustecky Kraj, Ústí nad Labem, Czech Republic
| | - Fabian Rost
- DRESDEN-Concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
- Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany
| | - Sylke Winkler
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- DRESDEN-Concept Genome Center, Technische Universität Dresden, Dresden, Germany
| | - Alexa Laubner
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Susanne Reinhardt
- DRESDEN-Concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Andreas Dahl
- DRESDEN-Concept Genome Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Alexander H. Dalpke
- Institute of Medical Microbiology and Virology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Department of Infectious Diseases, Medical Microbiology and Hygiene, University of Heidelberg, Heidelberg, Germany
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4
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Shao Y, Magee AF, Vasylyeva TI, Suchard MA. Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models. PLoS Comput Biol 2024; 20:e1011640. [PMID: 38551979 PMCID: PMC11006205 DOI: 10.1371/journal.pcbi.1011640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/10/2024] [Accepted: 03/10/2024] [Indexed: 04/09/2024] Open
Abstract
Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.
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Affiliation(s)
- Yucai Shao
- Department of Biostatistics, University of California, Los Angeles, California, United States of America
| | - Andrew F. Magee
- Department of Biomathematics, University of California, Los Angeles, California, United States of America
| | - Tetyana I. Vasylyeva
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Department of Population Health and Disease Prevention, University of California Irvine, Irvine, California, United States of America
| | - Marc A. Suchard
- Department of Biostatistics, University of California, Los Angeles, California, United States of America
- Department of Biomathematics, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, Universtiy of California, Los Angeles, California, United States of America
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5
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Luo C, Li X, Li Y. Application of the Peroxidase‒like Activity of Nanomaterials for the Detection of Pathogenic Bacteria and Viruses. Int J Nanomedicine 2024; 19:441-452. [PMID: 38250191 PMCID: PMC10799623 DOI: 10.2147/ijn.s442335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024] Open
Abstract
Infectious diseases caused by pathogenic bacteria and viruses pose a significant threat to human life and well-being. The prompt identification of these pathogens, characterized by speed, accuracy, and efficiency, not only aids in the timely screening of infected individuals and the prevention of further transmission, but also facilitates the precise diagnosis and treatment of patients. Direct smear microscopy, microbial culture, nucleic acid-based polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA) based on microbial surface antigens or human serum antibodies, have made substantial contributions to the prevention and management of infectious diseases. Due to its shorter processing time, simple equipment requirements, and no need for professional and technical personnel, ELISA has inherent advantages over other methods for detecting pathogenic bacteria and viruses. Horseradish peroxidase mediated catalysis of substrate coloration is the key for the detection of target substances in ELISA. However, the variability, high cost, and environmental susceptibility of natural peroxidase greatly limit the application of ELISA in pathogen detection. Compared with natural enzymes, nanomaterials with enzyme-mimicking activity are inexpensive, highly environmentally stable, easy to store and mass producing, etc. Based on their peroxidase-like activities and unique physicochemical properties, nanomaterials can greatly improve the efficiency and ease of use of ELISA-like detection methods for pathogenic bacteria and viruses. This review introduces recent advances in the application of nanomaterials with peroxidase-like activity for the detection of pathogenic bacteria (both gram-negative bacteria and gram-positive bacteria) and viruses (both RNA viruses and DNA viruses). The emphasis is on the detection principle and the evaluation of effectiveness. The limitations and prospects for future translations are also discussed.
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Affiliation(s)
- Cheng Luo
- School of Medicine, Yichun University, Yichun, 336000, People’s Republic of China
| | - Xianglong Li
- Medical and Radiation Oncology, Department of the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
| | - Yan Li
- School of Medicine, Yichun University, Yichun, 336000, People’s Republic of China
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6
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Isabel S, Eshaghi A, Duvvuri VR, Gubbay JB, Cronin K, Li A, Hasso M, Clark ST, Hopkins JP, Patel SN, Braukmann TWA. Targeted amplification-based whole genome sequencing of Monkeypox virus in clinical specimens. Microbiol Spectr 2024; 12:e0297923. [PMID: 38047694 PMCID: PMC10783113 DOI: 10.1128/spectrum.02979-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/29/2023] [Indexed: 12/05/2023] Open
Abstract
IMPORTANCE We present a protocol to efficiently sequence genomes of the MPXV-causing mpox. This enables researchers and public health agencies to acquire high-quality genomic data using a rapid and cost-effective approach. Genomic data can be used to conduct surveillance and investigate mpox outbreaks. We present 91 mpox genomes that show the diversity of the 2022 mpox outbreak in Ontario, Canada.
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Affiliation(s)
- S. Isabel
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
| | - A. Eshaghi
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
| | - V. R. Duvvuri
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - J. B. Gubbay
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - K. Cronin
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
| | - Aimin Li
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
| | - M. Hasso
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
| | - S. T. Clark
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
| | - J. P. Hopkins
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - S. N. Patel
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - T. W. A. Braukmann
- Public Health Ontario Laboratory, Public Health Ontario, Toronto, Ontario, Canada
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7
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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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8
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Chen Z, Lemey P, Yu H. Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data. THE LANCET. MICROBE 2024; 5:e81-e92. [PMID: 38042165 DOI: 10.1016/s2666-5247(23)00296-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 12/04/2023]
Abstract
Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.
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Affiliation(s)
- Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary Virology, KU Leuven, Leuven, Belgium
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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9
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Penn MJ, Scheidwasser N, Penn J, Donnelly CA, Duchêne DA, Bhatt S. Leaping through Tree Space: Continuous Phylogenetic Inference for Rooted and Unrooted Trees. Genome Biol Evol 2023; 15:evad213. [PMID: 38085949 PMCID: PMC10745275 DOI: 10.1093/gbe/evad213] [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] [Accepted: 11/16/2023] [Indexed: 12/24/2023] Open
Abstract
Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains challenging. To address this problem, for the first time, we perform both tree exploration and inference in a continuous space where the computation of gradients is possible. This continuous relaxation allows for major leaps across tree space in both rooted and unrooted trees, and is less susceptible to convergence to local minima. Our approach outperforms the current best methods for inference on unrooted trees and, in simulation, accurately infers the tree and root in ultrametric cases. The approach is effective in cases of empirical data with negligible amounts of data, which we demonstrate on the phylogeny of jawed vertebrates. Indeed, only a few genes with an ultrametric signal were generally sufficient for resolving the major lineages of vertebrates. Optimization is possible via automatic differentiation and our method presents an effective way forward for exploring the most difficult, data-deficient phylogenetic questions.
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Affiliation(s)
- Matthew J Penn
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Neil Scheidwasser
- Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Penn
- Department of Physics, University of Oxford, Oxford, United Kingdom
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - David A Duchêne
- Center for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Samir Bhatt
- Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom
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10
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Weber A, Översti S, Kühnert D. Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021. Virus Evol 2023; 9:vead070. [PMID: 38107332 PMCID: PMC10725310 DOI: 10.1093/ve/vead070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023] Open
Abstract
Phylodynamic methods have lately played a key role in understanding the spread of infectious diseases. During the coronavirus disease (COVID-19) pandemic, large scale genomic surveillance has further increased the potential of dynamic inference from viral genomes. With the continual emergence of novel severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants, explicitly allowing transmission rate differences between simultaneously circulating variants in phylodynamic inference is crucial. In this study, we present and empirically validate an extension to the BEAST2 package birth-death skyline model (BDSKY), BDSKY[Formula: see text], which introduces a scaling factor for the transmission rate between independent, jointly inferred trees. In an extensive simulation study, we show that BDSKY[Formula: see text] robustly infers the relative transmission rates under different epidemic scenarios. Using publicly available genome data of SARS-CoV-2, we apply BDSKY[Formula: see text] to quantify the transmission advantage of the Omicron over the Delta variant in Berlin, Germany. We find the overall transmission rate of Omicron to be scaled by a factor of two with pronounced variation between the individual clusters of each variant. These results quantify the transmission advantage of Omicron over the previously circulating Delta variant, in a crucial period of pre-established non-pharmaceutical interventions. By inferring variant- as well as cluster-specific transmission rate scaling factors, we show the differences in transmission dynamics for each variant. This highlights the importance of incorporating lineage-specific transmission differences in phylodynamic inference.
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Affiliation(s)
- Ariane Weber
- Transmission, Infection, Diversification & Evolution Group (tide), Max Planck Institute of Geoanthropology, Kahlaische Strasse 10, Jena, Thuringia 07745, Germany
- Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig, Saxony 04103, Germany
| | | | - Denise Kühnert
- Transmission, Infection, Diversification & Evolution Group (tide), Max Planck Institute of Geoanthropology, Kahlaische Strasse 10, Jena, Thuringia 07745, Germany
- Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig, Saxony 04103, Germany
- Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Ludwig-Witthöft-Straße 14, Wildau, Brandenburg 15745, Germany
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11
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Bergna A, Lai A, Ventura CD, Bruzzone B, Weisz A, d'Avenia M, Testa S, Torti C, Sagnelli C, Menchise A, Brindicci G, Francisci D, Vicenti I, Clementi N, Callegaro A, Rullo EV, Caucci S, De Pace V, Orsi A, Brusa S, Greco F, Letizia V, Vaccaro E, Franci G, Rizzo F, Sagradi F, Lanfranchi L, Coppola N, Saracino A, Sampaolo M, Ronchiadin S, Galli M, Riva A, Zehender G. Genomic epidemiology of the main SARS-CoV-2 variants in Italy between summer 2020 and winter 2021. J Med Virol 2023; 95:e29193. [PMID: 37927140 DOI: 10.1002/jmv.29193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023]
Abstract
Since the beginning of the pandemic, SARS-CoV-2 has shown a great genomic variability, resulting in the continuous emergence of new variants that has made their global monitoring and study a priority. This work aimed to study the genomic heterogeneity, the temporal origin, the rate of viral evolution and the population dynamics of the main circulating variants (20E.EU1, Alpha and Delta) in Italy, in August 2020-January 2022 period. For phylogenetic analyses, three datasets were set up, each for a different main lineage/variant circulating in Italy in that time including other Italian and International sequences of the same lineage/variant, available in GISAID sampled in the same times. The international dataset showed 26 (23% Italians, 23% singleton, 54% mixed), 40 (60% mixed, 37.5% Italians, 1 singleton) and 42 (85.7% mixed, 9.5% singleton, 4.8% Italians) clusters with at least one Italian sequence, in 20E.EU1 clade, Alpha and Delta variants, respectively. The estimation of tMRCAs in the Italian clusters (including >70% of genomes from Italy) showed that in all the lineage/variant, the earliest clusters were the largest in size and the most persistent in time and frequently mixed. Isolates from the major Italian Islands tended to segregate in clusters more frequently than those from other part of Italy. The study of infection dynamics showed a positive correlation between the trend in the effective number of infections estimated by BSP model and the Re curves estimated by birth-death skyline plot. The present work highlighted different evolutionary dynamics of studied lineages with high concordance between epidemiological parameters estimation and phylodynamic trends suggesting that the mechanism of replacement of the SARS-CoV-2 variants must be related to a complex of factors involving the transmissibility, as well as the implementation of control measures, and the level of cross-immunization within the population.
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Affiliation(s)
- Annalisa Bergna
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Alessia Lai
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Carla Della Ventura
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | | | - Alessandro Weisz
- Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno and Genome Research Center for Health, Baronissi, Italy
| | - Morena d'Avenia
- UOSVD of Cytopathology and Screening, Department of Laboratory Medicines, Ospedale di Venere, Asl Bari, Bari, Italy
| | - Sophie Testa
- Unit of Infectious Diseases, Azienda Socio Sanitaria Territoriale Cremona, Cremona, Italy
| | - Carlo Torti
- Infectious and Tropical Disease Unit, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Caterina Sagnelli
- Department of Mental Health and Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Angela Menchise
- Microbiology and Virology Laboratory, A.O.R. San Carlo Potenza, Potenza, 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
| | - Nicola Clementi
- Laboratory of Microbiology and Virology, Università "Vita-Salute" San Raffaele, Milan, Italy
- Laboratory of Microbiology and Virology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Emmanuele Venanzi Rullo
- Unit of Infectious Diseases, Department of Experimental and Clinical Medicine, University of Messina, Messina, Italy
| | - Sara Caucci
- Department of Biomedical Sciences and Public Health, Virology Unit, Polytechnic University of Marche, Ancona, Italy
| | | | - Andrea Orsi
- Hygiene Unit, IRCCS AOU San Martino-IST, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Stefano Brusa
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | | | - Vittoria Letizia
- UOSD Genetic and Molecular Biology, AORN Sant'Anna and San Sebastiano di Caserta, Caserta, Italy
| | - Emilia Vaccaro
- Molecular Biology Units, AOU 'S. Giovanni di Dio e Ruggi d'Aragona' Università di Salerno, Salerno, Italy
| | - Gianluigi Franci
- Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno and Genome Research Center for Health, Baronissi, Italy
| | - Francesca Rizzo
- Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno and Genome Research Center for Health, Baronissi, Italy
| | - Fabio Sagradi
- Unit of Infectious Diseases, Azienda Socio Sanitaria Territoriale Cremona, Cremona, Italy
| | - Leonardo Lanfranchi
- Unit of Infectious Diseases, Azienda Socio Sanitaria Territoriale Cremona, Cremona, Italy
| | - Nicola Coppola
- Department of Mental Health and Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Michela Sampaolo
- Laboratory of Microbiology and Virology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Ronchiadin
- Artificial Intelligence Laboratory, Intesa Sanpaolo Innovation Center, Turin, Italy
| | - Massimo Galli
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Agostino Riva
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
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12
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Carnegie L, Raghwani J, Fournié G, Hill SC. Phylodynamic approaches to studying avian influenza virus. Avian Pathol 2023; 52:289-308. [PMID: 37565466 DOI: 10.1080/03079457.2023.2236568] [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: 02/02/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 08/12/2023]
Abstract
Avian influenza viruses can cause severe disease in domestic and wild birds and are a pandemic threat. Phylodynamics is the study of how epidemiological, evolutionary, and immunological processes can interact to shape viral phylogenies. This review summarizes how phylodynamic methods have and could contribute to the study of avian influenza viruses. Specifically, we assess how phylodynamics can be used to examine viral spread within and between wild or domestic bird populations at various geographical scales, identify factors associated with virus dispersal, and determine the order and timing of virus lineage movement between geographic regions or poultry production systems. We discuss factors that can complicate the interpretation of phylodynamic results and identify how future methodological developments could contribute to improved control of the virus.
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Affiliation(s)
- L Carnegie
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, UK
| | - J Raghwani
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, UK
| | - G Fournié
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, UK
- Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, Marcy l'Etoile, France
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, Saint Genes Champanelle, France
| | - S C Hill
- Department of Pathobiology and Population Sciences, Royal Veterinary College (RVC), Hatfield, UK
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13
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Volz E. Fitness, growth and transmissibility of SARS-CoV-2 genetic variants. Nat Rev Genet 2023; 24:724-734. [PMID: 37328556 DOI: 10.1038/s41576-023-00610-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2023] [Indexed: 06/18/2023]
Abstract
The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.
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Affiliation(s)
- Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
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14
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Layan M, Dacheux L, Lemey P, Brunker K, Ma L, Troupin C, Dussart P, Chevalier V, Wood JLN, Ly S, Duong V, Bourhy H, Dellicour S. Uncovering the endemic circulation of rabies in Cambodia. Mol Ecol 2023; 32:5140-5155. [PMID: 37540190 DOI: 10.1111/mec.17087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/18/2023] [Indexed: 08/05/2023]
Abstract
In epidemiology, endemicity characterizes sustained pathogen circulation in a geographical area, which involves a circulation that is not being maintained by external introductions. Because it could potentially shape the design of public health interventions, there is an interest in fully uncovering the endemic pattern of a disease. Here, we use a phylogeographic approach to investigate the endemic signature of rabies virus (RABV) circulation in Cambodia. Cambodia is located in one of the most affected regions by rabies in the world, but RABV circulation between and within Southeast Asian countries remains understudied. Our analyses are based on a new comprehensive data set of 199 RABV genomes collected between 2014 and 2017 as well as previously published Southeast Asian RABV sequences. We show that most Cambodian sequences belong to a distinct clade that has been circulating almost exclusively in Cambodia. Our results thus point towards rabies circulation in Cambodia that does not rely on external introductions. We further characterize within-Cambodia RABV circulation by estimating lineage dispersal metrics that appear to be similar to other settings, and by performing landscape phylogeographic analyses to investigate environmental factors impacting the dispersal dynamic of viral lineages. The latter analyses do not lead to the identification of environmental variables that would be associated with the heterogeneity of viral lineage dispersal velocities, which calls for a better understanding of local dog ecology and further investigations of the potential drivers of RABV spread in the region. Overall, our study illustrates how phylogeographic investigations can be performed to assess and characterize viral endemicity in a context of relatively limited data.
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Affiliation(s)
- Maylis Layan
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000, CNRS, Paris, France
- Collège Doctoral, Sorbonne Université, Paris, France
| | - Laurent Dacheux
- Lyssavirus Epidemiology and Neuropathology Unit, Institut Pasteur, Université Paris Cité, Paris, France
- WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, Université Paris Cité, Paris, France
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
| | - Kirstyn Brunker
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Laurence Ma
- Biomics, Center for Technological Resources and Research (C2RT), Institut Pasteur, Université Paris Cité, Paris, France
| | - Cécile Troupin
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Philippe Dussart
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Véronique Chevalier
- CIRAD, UMR ASTRE, Montpellier, France
- ASTRE, Univ. Montpellier CIRAD, INRAE, Montpellier, France
- Epidemiology and Clinical Research, Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | - James L N Wood
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Sowath Ly
- Epidemiology and Public Health, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Veasna Duong
- Virology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Hervé Bourhy
- Lyssavirus Epidemiology and Neuropathology Unit, Institut Pasteur, Université Paris Cité, Paris, France
- WHO Collaborating Centre for Reference and Research on Rabies, Institut Pasteur, Université Paris Cité, Paris, France
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
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15
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El Mazouri S, Essabbar A, Aanniz T, Eljaoudi R, Belyamani L, Ibrahimi A, Ouadghiri M. Genetic diversity and evolutionary dynamics of the Omicron variant of SARS-CoV-2 in Morocco. Pathog Glob Health 2023:1-12. [PMID: 37635364 DOI: 10.1080/20477724.2023.2250942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023] Open
Abstract
Among the numerous variants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that have been reported worldwide, the emergence of the Omicron variant has drastically changed the landscape of the coronavirus disease (COVID-19) pandemic. Here, we analyzed the genetic diversity of Moroccan SARS-CoV-2 genomes with a focus on Omicron variant after one year of its detection in Morocco in order to understand its genomic dynamics, features and its potential introduction sources. From 937 Omicron genomes, we identified a total of 999 non-unique mutations distributed across 92 Omicron lineages, of which 13 were specific to the country. Our findings suggest multiple introductory sources of the Omicron variant to Morocco. In addition, we found that four Omicron clades are more infectious in comparison to other Omicron clades. Remarkably, a clade of Omicron is particularly more transmissible and has become the dominant variant worldwide. Moreover, our assessment of Receptor-Binding Domain (RBD) mutations showed that the Spike K444T and N460K mutations enabled a clade higher ability of immune vaccine escape. In conclusion, our analysis highlights the unique genetic diversity of the Omicron variant in Moroccan SARS-CoV-2 genomes, with multiple introductory sources and the emergence of highly transmissible clades. The distinctiveness of the Moroccan strains compared to global ones underscores the importance of ongoing surveillance and understanding of local genomic dynamics for effective response strategies in the evolving COVID-19 pandemic.
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Affiliation(s)
- Safae El Mazouri
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
| | - Abdelmounim Essabbar
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
| | - Tarik Aanniz
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
| | - Rachid Eljaoudi
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
- Mohammed VI Center for Research & Innovation, Mohammed VI University of Health Sciences, Casablanca, Morocco
| | - Lahcen Belyamani
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
- Mohammed VI Center for Research & Innovation, Mohammed VI University of Health Sciences, Casablanca, Morocco
- Emergency Department, Military Hospital Mohammed V, Rabat, Morocco
| | - Azeddine Ibrahimi
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
- Mohammed VI Center for Research & Innovation, Mohammed VI University of Health Sciences, Casablanca, Morocco
| | - Mouna Ouadghiri
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
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16
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Friedman SR, Perlman DC, Paraskevis D, Feldman J. Sociopolitical Diagnostic Tools to Understand National and Local Response Capabilities and Vulnerabilities to Epidemics and Guide Research into How to Improve the Global Response to Pathogens. Pathogens 2023; 12:1023. [PMID: 37623983 PMCID: PMC10457759 DOI: 10.3390/pathogens12081023] [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: 06/23/2023] [Revised: 08/04/2023] [Accepted: 08/05/2023] [Indexed: 08/26/2023] Open
Abstract
The AIDS and COVID-19 pandemics demonstrated that nations at similar economic development levels varied widely in their capacity to protect the health of their residents. For AIDS, Britain and Australia brought gay representatives into official counsels and adopted harm reduction far more rapidly than the United States or Spain, and East African countries responded more effectively than South Africa or the Democratic Republic of the Congo. National responses to COVID-19 varied widely, with New Zealand, China, and Vietnam more effective than Italy, Brazil, or the United States. Further, as phylogenetic research has demonstrated, these pandemics spread from one country to another, with those that responded poorly acting as sources for mutations and potentially sources of transmission to countries with more effective responses. Many observers expressed surprise at the poor responses of the United States to COVID-19, but in retrospect the cutbacks in public health funding at state and national levels made it clear that this was a predictable weakness even in addition to the political vacillations that crippled the US and Brazilian responses. In a time of global sociopolitical and climate instability, it is important to measure and conduct research into spatial and time variations in 1. public health and medical funding, 2. social influence networks, social cohesion and trust, and stigmatization, 3. income inequality, 4. social conflict, and 5. other factors that affect responsiveness to pandemics.
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Affiliation(s)
| | - David C. Perlman
- Infectious Diseases, Mount Sinai Beth Israel, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Dimitrios Paraskevis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Justin Feldman
- Visiting Scientist, Harvard FXB Center for Health and Human Rights, Cambridge, MA 02138, USA;
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17
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Porter AF, Featherstone L, Lane CR, Sherry NL, Nolan ML, Lister D, Seemann T, Duchene S, Howden BP. The importance of utilizing travel history metadata for informative phylogeographical inferences: a case study of early SARS-CoV-2 introductions into Australia. Microb Genom 2023; 9:mgen001099. [PMID: 37650865 PMCID: PMC10483412 DOI: 10.1099/mgen.0.001099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/08/2023] [Indexed: 09/01/2023] Open
Abstract
Inferring the spatiotemporal spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via Bayesian phylogeography has been complicated by the overwhelming sampling bias present in the global genomic dataset. Previous work has demonstrated the utility of metadata in addressing this bias. Specifically, the inclusion of recent travel history of SARS-CoV-2-positive individuals into extended phylogeographical models has demonstrated increased accuracy of estimates, along with proposing alternative hypotheses that were not apparent using only genomic and geographical data. However, as the availability of comprehensive epidemiological metadata is limited, many of the current estimates rely on sequence data and basic metadata (i.e. sample date and location). As the bias within the SARS-CoV-2 sequence dataset is extensive, the degree to which we can rely on results drawn from standard phylogeographical models (i.e. discrete trait analysis) that lack integrated metadata is of great concern. This is particularly important when estimates influence and inform public health policy. We compared results generated from the same dataset, using two discrete phylogeographical models: one including travel history metadata and one without. We utilized sequences from Victoria, Australia, in this case study for two unique properties. Firstly, the high proportion of cases sequenced throughout 2020 within Victoria and the rest of Australia. Secondly, individual travel history was collected from returning travellers in Victoria during the first wave (January to May) of the coronavirus disease 2019 (COVID-19) pandemic. We found that the implementation of individual travel history was essential for the estimation of SARS-CoV-2 movement via discrete phylogeography models. Without the additional information provided by the travel history metadata, the discrete trait analysis could not be fit to the data due to numerical instability. We also suggest that during the first wave of the COVID-19 pandemic in Australia, the primary driving force behind the spread of SARS-CoV-2 was viral importation from international locations. This case study demonstrates the necessity of robust genomic datasets supplemented with epidemiological metadata for generating accurate estimates from phylogeographical models in datasets that have significant sampling bias. For future work, we recommend the collection of metadata in conjunction with genomic data. Furthermore, we highlight the risk of applying phylogeographical models to biased datasets without incorporating appropriate metadata, especially when estimates influence public health policy decision making.
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Affiliation(s)
- Ashleigh F. Porter
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Leo Featherstone
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Courtney R. Lane
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Norelle L. Sherry
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia
| | | | | | - Torsten Seemann
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Centre for Pathogen Genomics, The University of Melbourne, Melbourne, VIC, Australia
| | - Sebastian Duchene
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Benjamin P. Howden
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia
- Centre for Pathogen Genomics, The University of Melbourne, Melbourne, VIC, Australia
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18
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Tian L, Liang C, Huang X, Liu Z, Su J, Guo C, Zhu G, Sun J. Genomic epidemiology of dengue in Shantou, China, 2019. Front Public Health 2023; 11:1035060. [PMID: 37522010 PMCID: PMC10374217 DOI: 10.3389/fpubh.2023.1035060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 06/23/2023] [Indexed: 08/01/2023] Open
Abstract
Objectives Dengue has been endemic in Southeast Asian countries for decades. There are few reports tracing the dynamics of dengue in real time. In this study, we generated hundreds of pathogen genomes to understand the genomic epidemiology of an outbreak in a hyper-endemic area of dengue. Methods We leveraged whole-genome short-read sequencing (PE150) to generate genomes of the dengue virus and investigated the genomic epidemiology of a dengue virus transmission in a mesoscale outbreak in Shantou, China, in 2019. Results The outbreak was sustained from July to December 2019. The total accumulated number of laboratory-confirmed cases was 944. No gender bias or fatalities were recorded. Cambodia and Singapore were the main sources of imported dengue cases (74.07%, n = 20). A total of 284 dengue virus strains were isolated, including 259 DENV-1, 24 DENV-2, and 1 DENV-3 isolates. We generated the entire genome of 252 DENV isolates (229 DENV-1, 22 DENV-2, and 1 DENV-3), which represented 26.7% of the total cases. Combined epidemiological and phylogenetic analyses indicated multiple independent introductions. The internal transmission evaluations and transmission network reconstruction supported the inference of phylodynamic analysis, with high Bayes factor support in BSSVS analysis. Two expansion founders and transmission chains were detected in CCH and LG of Shantou. Conclusions We observed the instant effects of genomic epidemiology in monitoring the dynamics of DENV and highlighted its prospects for real-time tracing of outbreaks of other novel agents in the future.
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Affiliation(s)
- Lina Tian
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China
| | - Chumin Liang
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xiaorong Huang
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Juan Su
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Chuan Guo
- Center for Disease Control and Prevention of Shantou City, Shantou, Guangdong, China
| | - Guanghu Zhu
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China
| | - Jiufeng Sun
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
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19
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Férez JA, Cuevas-Ferrando E, Ayala-San Nicolás M, Simón Andreu PJ, López R, Truchado P, Sánchez G, Allende A. Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach. Viruses 2023; 15:1499. [PMID: 37515186 PMCID: PMC10386001 DOI: 10.3390/v15071499] [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: 05/02/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
The COVID-19 pandemic has posed a significant global threat, leading to several initiatives for its control and management. One such initiative involves wastewater-based epidemiology, which has gained attention for its potential to provide early warning of virus outbreaks and real-time information on its spread. In this study, wastewater samples from two wastewater treatment plants (WWTPs) located in the southeast of Spain (region of Murcia), namely Murcia, and Cartagena, were analyzed using RT-qPCR and high-throughput sequencing techniques to describe the evolution of SARS-CoV-2 in the South-East of Spain. Additionally, phylogenetic analysis and machine learning approaches were applied to develop a pre-screening tool for the identification of differences among the variant composition of different wastewater samples. The results confirmed that the levels of SARS-CoV-2 in these wastewater samples changed concerning the number of SARS-CoV-2 cases detected in the population, and variant occurrences were in line with clinical reported data. The sequence analyses helped to describe how the different SARS-CoV-2 variants have been replaced over time. Additionally, the phylogenetic analysis showed that samples obtained at close sampling times exhibited a higher similarity than those obtained more distantly in time. A second analysis using a machine learning approach based on the mutations found in the SARS-CoV-2 spike protein was also conducted. Hierarchical clustering (HC) was used as an efficient unsupervised approach for data analysis. Results indicated that samples obtained in October 2022 in Murcia and Cartagena were significantly different, which corresponded well with the different virus variants circulating in the two locations. The proposed methods in this study are adequate for comparing consensus sequence types of the SARS-CoV-2 sequences as a preliminary evaluation of potential changes in the variants that are circulating in a given population at a specific time point.
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Affiliation(s)
- Jose A Férez
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
| | - Enric Cuevas-Ferrando
- Environmental Virology and Food Safety Lab (VISAFELab), Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, Av. Agustín Escardino 7, 46980 Valencia, Spain
| | - María Ayala-San Nicolás
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
| | - Pedro J Simón Andreu
- Entidad Regional de Saneamiento y Depuración de Murcia (ESAMUR), Avda. Juan Carlos I, s/n. Ed. Torre Jemeca, 30009 Murcia, Spain
| | - Román López
- Entidad Regional de Saneamiento y Depuración de Murcia (ESAMUR), Avda. Juan Carlos I, s/n. Ed. Torre Jemeca, 30009 Murcia, Spain
| | - Pilar Truchado
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
| | - Gloria Sánchez
- Environmental Virology and Food Safety Lab (VISAFELab), Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, Av. Agustín Escardino 7, 46980 Valencia, Spain
| | - Ana Allende
- Research Group on Microbiology and Quality of Fruit and Vegetables, CEBAS-CSIC, 30100 Murcia, Spain
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20
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Brand M, Keşmir C. Evolution of SARS-CoV-2-specific CD4 + T cell epitopes. Immunogenetics 2023; 75:283-293. [PMID: 36719467 PMCID: PMC9887569 DOI: 10.1007/s00251-023-01295-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 01/10/2023] [Indexed: 02/01/2023]
Abstract
Vaccination clearly decreases coronavirus disease 2019 (COVID-19) mortality; however, they also impose selection pressure on the virus, which promotes the evolution of immune escape variants. For example, despite the high vaccination level in especially Western countries, the Omicron variant caused millions of breakthrough infections, suggesting that the highly mutated spike protein in the Omicron variant can escape antibody immunity much more efficiently than the other variants of concern (VOCs). In this study, we investigated the resistance/susceptibility of T helper cell responses that are necessary for generating efficient long-lasting antibody immunity, in several VOCs. By predicting T helper cell epitopes on the spike protein for most common HLA-DRB1 alleles worldwide, we found that although most of high frequency HLA-DRB1 alleles have several potential T helper cell epitopes, few alleles like HLA-DRB1 13:01 and 11:01 are not predicted to have any significant T helper cell responses after vaccination. Using these predictions, a population based on realistic human leukocyte antigen-II (HLA-II) frequencies were simulated to visualize the T helper cell immunity on the population level. While a small fraction of this population had alarmingly little predicted CD4 T cell epitopes, the majority had several epitopes that should be enough to generate efficient B cell responses. Moreover, we show that VOC spike mutations hardly affect T helper epitopes and mainly occur in other residues of the spike protein. These results suggest that lack of long-lasting antibody responses is not likely due to loss of T helper cell epitopes in new VOCs.
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Affiliation(s)
- Marina Brand
- Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, Netherlands
| | - Can Keşmir
- Theoretical Biology & Bioinformatics, Utrecht University, Utrecht, Netherlands.
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21
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Klamser PP, d’Andrea V, Di Lauro F, Zachariae A, Bontorin S, Di Nardo A, Hall M, Maier BF, Ferretti L, Brockmann D, De Domenico M. Enhancing global preparedness during an ongoing pandemic from partial and noisy data. PNAS NEXUS 2023; 2:pgad192. [PMID: 37351112 PMCID: PMC10282504 DOI: 10.1093/pnasnexus/pgad192] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 06/24/2023]
Abstract
As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75, and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.
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Affiliation(s)
| | | | - Francesco Di Lauro
- Big Data Institute, University of Oxford, Old Road Campus, OX3 7LF Oxford, UK
| | - Adrian Zachariae
- Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany
- Department of Biology, Institute for Theoretical Biology, Humboldt-University of Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - Sebastiano Bontorin
- Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo (TN), Italy
- Department of Physics, University of Trento, Via Sommarive 14, 38123 Povo (TN), Italy
| | | | - Matthew Hall
- Big Data Institute, University of Oxford, Old Road Campus, OX3 7LF Oxford, UK
| | - Benjamin F Maier
- Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany
- Department of Biology, Institute for Theoretical Biology, Humboldt-University of Berlin, Philippstr. 13, 10115 Berlin, Germany
| | - Luca Ferretti
- Big Data Institute, University of Oxford, Old Road Campus, OX3 7LF Oxford, UK
| | - Dirk Brockmann
- Robert Koch-Institute, Nordufer 20, 13353 Berlin, Germany
- Department of Biology, Institute for Theoretical Biology, Humboldt-University of Berlin, Philippstr. 13, 10115 Berlin, Germany
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22
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Andrews KR, New DD, Gour DS, Francetich K, Minnich SA, Robison BD, Hovde CJ. Genomic surveillance identifies potential risk factors for SARS-CoV-2 transmission at a mid-sized university in a small rural town. Sci Rep 2023; 13:7902. [PMID: 37193760 PMCID: PMC10185956 DOI: 10.1038/s41598-023-34625-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/04/2023] [Indexed: 05/18/2023] Open
Abstract
Understanding transmission dynamics of SARS-CoV-2 in institutions of higher education (IHEs) is important because these settings have potential for rapid viral spread. Here, we used genomic surveillance to retrospectively investigate transmission dynamics throughout the 2020-2021 academic year for the University of Idaho ("University"), a mid-sized IHE in a small rural town. We generated genome assemblies for 1168 SARS-CoV-2 samples collected during the academic year, representing 46.8% of positive samples collected from the University population and 49.8% of positive samples collected from the surrounding community ("Community") at the local hospital during this time. Transmission dynamics differed for the University when compared to the Community, with more infection waves that lasted shorter lengths of time, potentially resulting from high-transmission congregate settings along with mitigation efforts implemented by the University to combat outbreaks. We found evidence for low transmission rates between the University and Community, with approximately 8% of transmissions into the Community originating from the University, and approximately 6% of transmissions into the University originating from the Community. Potential transmission risk factors identified for the University included congregate settings such as sorority and fraternity events and residences, holiday travel, and high caseloads in the surrounding community. Knowledge of these risk factors can help the University and other IHEs develop effective mitigation measures for SARS-CoV-2 and similar pathogens.
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Affiliation(s)
- Kimberly R Andrews
- Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID, 83844, USA.
| | - Daniel D New
- Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID, 83844, USA
| | - Digpal S Gour
- Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID, 83844, USA
| | | | - Scott A Minnich
- Department of Animal, Veterinary and Food Science, University of Idaho, Moscow, ID, 83844, USA
| | - Barrie D Robison
- Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID, 83844, USA
| | - Carolyn J Hovde
- Department of Animal, Veterinary and Food Science, University of Idaho, Moscow, ID, 83844, USA
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23
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Supplisson O, Charmet T, Galmiche S, Schaeffer L, Chény O, Lévy A, Jeandet N, Omar F, David C, Mailles A, Fontanet A. SARS-CoV-2 self-test uptake and factors associated with self-testing during Omicron BA.1 and BA.2 waves in France, January to May 2022. Euro Surveill 2023; 28:2200781. [PMID: 37140451 PMCID: PMC10161682 DOI: 10.2807/1560-7917.es.2023.28.18.2200781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/03/2023] [Indexed: 05/05/2023] Open
Abstract
BackgroundFollowing the SARS-CoV-2 Omicron variant spread, the use of unsupervised antigenic rapid diagnostic tests (self-tests) increased.AimThis study aimed to measure self-test uptake and factors associated with self-testing.MethodsIn this cross-sectional study from 20 January to 2 May 2022, the case series from a case-control study on factors associated with SARS-CoV-2 infection were used to analyse self-testing habits in France. A multivariable quasi-Poisson regression was used to explore the variables associated with self-testing among symptomatic cases who were not contacts of another infected individual. The control series from the same study was used as a proxy for the self-test background rate in the non-infected population of France.ResultsDuring the study period, 179,165 cases who tested positive through supervised tests were recruited. Of these, 64.7% had performed a self-test in the 3 days preceding this supervised test, of which 79,038 (68.2%) were positive. The most frequently reported reason for self-testing was the presence of symptoms (64.6%). Among symptomatic cases who were not aware of being contacts of another case, self-testing was positively associated with being female, higher education, household size, being a teacher and negatively associated with older age, not French by birth, healthcare-related work and immunosuppression. Among the control series, 12% self-tested during the 8 days preceding questionnaire filling, with temporal heterogeneity.ConclusionThe analysis showed high self-test uptake in France with some inequalities which must be addressed through education and facilitated access (cost and availability) for making it a more efficient epidemic control tool.
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Affiliation(s)
- Olivier Supplisson
- Institut Pasteur, Université Paris Cité, Emerging Diseases Epidemiology Unit, Paris, France
- Center for Interdisciplinary Research in Biology, Ecology and Evolution of Health team (Collège de France, CNRS/UMR 7241, Inserm U1050), Paris, France
- Sorbonne Université, Paris, France
| | - Tiffany Charmet
- Institut Pasteur, Université Paris Cité, Emerging Diseases Epidemiology Unit, Paris, France
| | - Simon Galmiche
- Institut Pasteur, Université Paris Cité, Emerging Diseases Epidemiology Unit, Paris, France
- Sorbonne Université, Paris, France
| | - Laura Schaeffer
- Institut Pasteur, Université Paris Cité, Emerging Diseases Epidemiology Unit, Paris, France
| | - Olivia Chény
- Institut Pasteur, Université Paris Cité, Clinical Operation Coordination Office, Paris, France
| | - Anne Lévy
- Caisse Nationale d'Assurance Maladie, Paris, France
| | | | | | | | | | - Arnaud Fontanet
- Institut Pasteur, Université Paris Cité, Emerging Diseases Epidemiology Unit, Paris, France
- Conservatoire National des Arts et Métiers, unité PACRI, Paris, France
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24
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Markov PV, Ghafari M, Beer M, Lythgoe K, Simmonds P, Stilianakis NI, Katzourakis A. The evolution of SARS-CoV-2. Nat Rev Microbiol 2023; 21:361-379. [PMID: 37020110 DOI: 10.1038/s41579-023-00878-2] [Citation(s) in RCA: 224] [Impact Index Per Article: 224.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2023] [Indexed: 04/07/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths and substantial morbidity worldwide. Intense scientific effort to understand the biology of SARS-CoV-2 has resulted in daunting numbers of genomic sequences. We witnessed evolutionary events that could mostly be inferred indirectly before, such as the emergence of variants with distinct phenotypes, for example transmissibility, severity and immune evasion. This Review explores the mechanisms that generate genetic variation in SARS-CoV-2, underlying the within-host and population-level processes that underpin these events. We examine the selective forces that likely drove the evolution of higher transmissibility and, in some cases, higher severity during the first year of the pandemic and the role of antigenic evolution during the second and third years, together with the implications of immune escape and reinfections, and the increasing evidence for and potential relevance of recombination. In order to understand how major lineages, such as variants of concern (VOCs), are generated, we contrast the evidence for the chronic infection model underlying the emergence of VOCs with the possibility of an animal reservoir playing a role in SARS-CoV-2 evolution, and conclude that the former is more likely. We evaluate uncertainties and outline scenarios for the possible future evolutionary trajectories of SARS-CoV-2.
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Affiliation(s)
- Peter V Markov
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
- London School of Hygiene & Tropical Medicine, University of London, London, UK.
| | - Mahan Ghafari
- Big Data Institute, University of Oxford, Oxford, UK
| | - Martin Beer
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Insel Riems, Germany
| | | | - Peter Simmonds
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikolaos I Stilianakis
- European Commission, Joint Research Centre (JRC), Ispra, Italy
- Department of Biometry and Epidemiology, University of Erlangen-Nuremberg, Erlangen, Germany
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25
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Lewinsohn MA, Bedford T, Müller NF, Feder AF. State-dependent evolutionary models reveal modes of solid tumour growth. Nat Ecol Evol 2023; 7:581-596. [PMID: 36894662 PMCID: PMC10089931 DOI: 10.1038/s41559-023-02000-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 01/26/2023] [Indexed: 03/11/2023]
Abstract
Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.
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Affiliation(s)
- Maya A Lewinsohn
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Trevor Bedford
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Nicola F Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Alison F Feder
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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26
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Senchyna F, Singh R. Dynamic Epidemiological Networks: A Data Representation Framework for Modeling and Tracking of SARS-CoV-2 Variants. J Comput Biol 2023; 30:446-468. [PMID: 37098217 DOI: 10.1089/cmb.2022.0469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2023] Open
Abstract
The large-scale real-time sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has allowed for rapid identification of concerning variants through phylogenetic analysis. However, the nature of phylogenetic reconstruction is typically static, in that the relationships between taxonomic units, once defined, are not subject to alterations. Furthermore, most phylogenetic methods are intrinsically batch mode in nature, requiring the presence of the entire data set. Finally, the emphasis of phylogenetics is on relating taxonomical units. These characteristics complicate the application of classical phylogenetics methods to represent relationships in molecular data collected from rapidly evolving strains of an etiological agent, such as SARS-CoV-2, since the molecular landscape is updated continuously as samples are collected. In such settings, variant definitions are subject to epistemological constraints and may change as data accumulate. Furthermore, representing within-variant molecular relationships may be as important as representing between variant relationships. This article describes a novel data representation framework called dynamic epidemiological networks (DENs) along with algorithms that underpin its construction to address these issues. The proposed representation is applied to study the molecular development underlying the spread of the COVID-19 (coronavirus disease 2019) pandemic in two countries: Israel and Portugal spanning a 2-year period from February 2020 to April 2022. The results demonstrate how this framework could be used to provide a multiscale representation of the data by capturing molecular relationships between samples as well as those between variants, automatically identifying the emergence of high frequency variants (lineages), including variants of concern such as Alpha and Delta, and tracking their growth. Additionally, we show how analyzing the evolution of the DEN can help identify changes in the viral population that could not be readily inferred from phylogenetic analysis.
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Affiliation(s)
- Fiona Senchyna
- Department of Computer Science, San Francisco State University, San Francisco, California, USA
| | - Rahul Singh
- Department of Computer Science, San Francisco State University, San Francisco, California, USA
- Center for Discovery and Innovation in Parasitic Diseases, University of California, San Diego, California, USA
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27
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Said KB, Alsolami A, Alshammari F, Alshammari KF, Alazmi M, Bhardwaj T, Najm MZ, Singh R, Kausar MA. Molecular evolutionary model based on phylogenetic and mutation analysis of SARS-CoV-2 spike protein sequences from Asian countries: A phylogenomic approach. INFORMATICS IN MEDICINE UNLOCKED 2023; 38:101221. [PMID: 36974160 PMCID: PMC10030443 DOI: 10.1016/j.imu.2023.101221] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/24/2023] Open
Abstract
The lethal pathogenic severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection has caused the COVID-19 pandemic, posing serious risks to people. The clove-like spike (S) protein that distinguishes coronaviruses from other viruses is important for viral pathogenicity, evolution, and transmission. The investigation of the unique structural mutations of the SARS-CoV-2 spike protein among 34 Asian countries, as well as the resulting phylogenetic relationship, provided critical information in understanding the pathogenesis. This can be utilized for the discovery of possible treatments and vaccine development. The current study analyzed and depicted phylogenetic and evolutionary models useful for understanding SARS-CoV-2 human-human transmission dynamics in Asian regions with shared land borders. Further, integrated bioinformatics analysis was performed to predict the pathogenic potential and stability of 53 mutational positions among 34 coronavirus strains. Mutations at positions N969K, D614G and S884F have deleterious effects on protein function. These findings are crucial because the Asian mutations could potentially provide a vaccine candidate with co-protection against all SARS-CoV-2 strains. This region is vulnerable because of the high population density and the volume of domestic and international travel for business and tourism. These discoveries would also aid in the development of plans for governments and the general populace to implement all required biocontainment protocols common to all countries.
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Affiliation(s)
- Kamaleldin B Said
- Department of Pathology and Microbiology, College of Medicine, University of Ha'il, Ha'il, 55476, Saudi Arabia
| | - Ahmed Alsolami
- Department of Internal Medicine, College of Medicine, University of Ha'il, Ha'il, 55476, Saudi Arabia
| | - Fawaz Alshammari
- Department of Dermatology, College of Medicine, University of Ha'il, Ha'il, 55476, Saudi Arabia
| | - Khalid Farhan Alshammari
- Department of Internal Medicine, College of Medicine at University of Ha'il, Ha'il, 2440, Saudi Arabia
| | - Meshari Alazmi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia
| | - Tulika Bhardwaj
- Department of Agriculture, Food and Nutritional Sciences, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | | | - Rajeev Singh
- Department of Environmental Science, Jamia Millia Islamia (Central University), New Delhi, 110025, India
| | - Mohd Adnan Kausar
- Department of Biochemistry, College of Medicine, University of Ha'il, Ha'il, 2440, Saudi Arabia
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28
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Minimal Antigenic Evolution after a Decade of Norovirus GII.4 Sydney_2012 Circulation in Humans. J Virol 2023; 97:e0171622. [PMID: 36688654 PMCID: PMC9973034 DOI: 10.1128/jvi.01716-22] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Norovirus is a major human pathogen that can cause severe gastroenteritis in vulnerable populations. The extensive viral diversity presented by human noroviruses constitutes a major roadblock for the development of effective vaccines. In addition to the large number of genotypes, antigenically distinct variants of GII.4 noroviruses have chronologically emerged over the last 3 decades. The last variant to emerge, Sydney_2012, has been circulating at high incidence worldwide for over a decade. We analyzed 1449 capsid sequences from GII.4 Sydney_2012 viruses to determine genetic changes indicative of antigenic diversification. Phylogenetic analyses show that Sydney_2012 viruses scattered within the tree topology with no single cluster dominating during a given year or geographical location. Fourteen residues presented high variability, 7 of which mapped to 4 antigenic sites. Notably, ~52% of viruses presented mutations at 2 or more antigenic sites. Mutational patterns showed that residues 297 and 372, which map to antigenic site A, changed over time. Virus-like particles (VLPs) developed from wild-type Sydney_2012 viruses and engineered to display all mutations detected at antigenic sites were tested against polyclonal sera and monoclonal antibodies raised against Sydney_2012 and Farmington_Hills_2002 VLPs. Minimal changes in reactivity were detected with polyclonal sera and only 4 MAbs lost binding, with all mapping to antigenic site A. Notably, reversion of residues from Sydney_2012 reconstituted epitopes from ancestral GII.4 variants. Overall, this study demonstrates that, despite circulating for over a decade, Sydney_2012 viruses present minimal antigenic diversification and provides novel insights on the diversification of GII.4 noroviruses that could inform vaccine design. IMPORTANCE GII.4 noroviruses are the major cause of acute gastroenteritis in all age groups. This predominance has been attributed to the continued emergence of phylogenetically discrete variants that escape immune responses to previous infections. The last GII.4 variant to emerge, Sydney_2012, has been circulating at high incidence for over a decade, raising the question of whether this variant is undergoing antigenic diversification without presenting a major distinction at the phylogenetic level. Sequence analyses that include >1400 capsid sequences from GII.4 Sydney_2012 showed changes in 4 out of the 6 major antigenic sites. Notably, while changes were detected in one of the most immunodominant sites over time, these resulted in minimal changes in the antigenic profile of these viruses. This study provides new insights on the mechanism governing the antigenic diversification of GII.4 norovirus that could help in the development of cross-protective vaccines to human noroviruses.
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Evaluating Data Sharing of SARS-CoV-2 Genomes for Molecular Epidemiology across the COVID-19 Pandemic. Viruses 2023; 15:v15020560. [PMID: 36851774 PMCID: PMC9959893 DOI: 10.3390/v15020560] [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: 12/31/2022] [Revised: 02/12/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Following the emergence of COVID-19 in December 2019, caused by the coronavirus SARS-CoV-2, the disease spread dramatically worldwide. The use of genomics to trace the dissemination of the virus and the identification of novel variants was essential in defining measures for containing the disease. We aim to evaluate the global effort to genomically characterize the circulating lineages of SARS-CoV-2, considering the data deposited in GISAID, the major platform for data sharing in a massive worldwide collaborative undertaking. We contextualize data for nearly three years (January 2020-October 2022) for the major contributing countries, percentage of characterized isolates and time for data processing in the context of the global pandemic. Within this collaborative effort, we also evaluated the early detection of seven major SARS-CoV-2 lineages, G, GR, GH, GK, GV, GRY and GRA. While Europe and the USA, following an initial period, showed positive results across time in terms of cases sequenced and time for data deposition, this effort is heterogeneous worldwide. Given the current immunization the major threat is the appearance of variants that evade the acquired immunity. In that scenario, the monitoring of those hypothetical variants will still play an essential role.
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30
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Duvvuri VR, Hicks JT, Damodaran L, Grunnill M, Braukmann T, Wu J, Gubbay JB, Patel SN, Bahl J. Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves. Infect Dis Model 2023; 8:240-252. [PMID: 36844759 PMCID: PMC9944206 DOI: 10.1016/j.idm.2023.02.003] [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: 12/02/2022] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023] Open
Abstract
Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R 0 ). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R 0 values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R 0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R 0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R 0 estimation and surveillance-based R 0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases.
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Affiliation(s)
- Venkata R. Duvvuri
- Public Health Ontario, Toronto, Ontario, Canada,Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada,Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada,Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Corresponding author. Public Health Ontario, Toronto, Ontario, Canada.
| | - Joseph T. Hicks
- Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Lambodhar Damodaran
- Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia
| | - Martin Grunnill
- Public Health Ontario, Toronto, Ontario, Canada,Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | | | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
| | - Jonathan B. Gubbay
- Public Health Ontario, Toronto, Ontario, Canada,Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Samir N. Patel
- Public Health Ontario, Toronto, Ontario, Canada,Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Justin Bahl
- Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Department of Epidemiology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia,Duke-NUS Graduate Medical School, Singapore,Corresponding author. Center for the Ecology of Infectious Disease, Department of Infectious Diseases, Institute of Bioinformatics, University of Georgia, Athens, Georgia, USA.
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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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32
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Towards precision medicine: Omics approach for COVID-19. BIOSAFETY AND HEALTH 2023; 5:78-88. [PMID: 36687209 PMCID: PMC9846903 DOI: 10.1016/j.bsheal.2023.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/19/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.
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Haan TJ, Smith LK, DeRonde S, House E, Zidek J, Puhak D, Mullen L, Redlinger M, Parker J, Barnes BM, Burkhead JL, Knall C, Bortz E, Chen J, Drown DM. A Repeat Pattern of Founder Events for SARS-CoV-2 Variants in Alaska. Viruses 2023; 15:222. [PMID: 36680262 PMCID: PMC9861170 DOI: 10.3390/v15010222] [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: 12/02/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 01/15/2023] Open
Abstract
Alaska is a unique US state because of its large size, geographically disparate population density, and physical distance from the contiguous United States. Here, we describe a pattern of SARS-CoV-2 variant emergence across Alaska reflective of these differences. Using genomic data, we found that in Alaska, the Omicron sublineage BA.2.3 overtook BA.1.1 by the week of 27 February 2022, reaching 48.5% of sequenced cases. On the contrary, in the contiguous United States, BA.1.1 dominated cases for longer, eventually being displaced by BA.2 sublineages other than BA.2.3. BA.2.3 only reached a prevalence of 10.9% in the contiguous United States. Using phylogenetics, we found evidence of potential origins of the two major clades of BA.2.3 in Alaska and with logistic regression estimated how it emerged and spread throughout the state. The combined evidence is suggestive of founder events in Alaska and is reflective of how Alaska's unique dynamics influence the emergence of SARS-CoV-2 variants.
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Affiliation(s)
- Tracie J. Haan
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
| | - Lisa K. Smith
- Alaska Division of Public Health, State of Alaska, Fairbanks, AK 99775, USA
| | - Stephanie DeRonde
- Alaska Division of Public Health, State of Alaska, Fairbanks, AK 99775, USA
| | - Elva House
- Alaska Division of Public Health, State of Alaska, Fairbanks, AK 99775, USA
| | - Jacob Zidek
- Alaska Division of Public Health, State of Alaska, Fairbanks, AK 99775, USA
| | - Diana Puhak
- Alaska Division of Public Health, State of Alaska, Fairbanks, AK 99775, USA
| | - Logan Mullen
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
| | - Matthew Redlinger
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, AK 99508, USA
| | - Jayme Parker
- Alaska Division of Public Health, State of Alaska, Fairbanks, AK 99775, USA
| | - Brian M. Barnes
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
| | - Jason L. Burkhead
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, AK 99508, USA
| | - Cindy Knall
- WWAMI School of Medical Education, University of Alaska Anchorage, Anchorage, AK 99508, USA
| | - Eric Bortz
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, AK 99508, USA
- WWAMI School of Medical Education, University of Alaska Anchorage, Anchorage, AK 99508, USA
| | - Jack Chen
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
- Alaska Division of Public Health, State of Alaska, Fairbanks, AK 99775, USA
- Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
| | - Devin M. Drown
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
- Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
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34
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Measurably recombining malaria parasites. Trends Parasitol 2023; 39:17-25. [PMID: 36435688 PMCID: PMC9893849 DOI: 10.1016/j.pt.2022.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/02/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022]
Abstract
Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens.
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35
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Delaye L. CurSa: scripts to curate metadata and sample genomes from GISAID for analysis and display in nextstrain and microreact. Biol Methods Protoc 2023; 8:bpad007. [PMID: 37180471 PMCID: PMC10174701 DOI: 10.1093/biomethods/bpad007] [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: 12/23/2022] [Revised: 03/26/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
The coronavirus SARS-CoV-2 is the most sequenced pathogen ever, with several million genome copies deposited in the GISAID database. This large amount of genomic information poses non-trivial bioinformatic challenges for those interested in studying the evolution of SARS-CoV-2. One common problem when studying the phylogeny of the coronavirus in its geographical context is to count with accurate information of the location of the samples. However, this information is filled by hand by research groups all over the world and sometimes typos and inconsistencies are introduced in the metadata when submitting the sequences to GISAID. Correcting these errors is laborious and time-consuming. Here, we provide a suite of Perl scripts designated to facilitate the curation of this vital information and perform a random sampling of genome sequences if necessary. The scripts provided here can be used to curate geographic information in the metadata and sample the sequences from any country of interest to ease the preparation of files for Nextstrain and Microreact, thus accelerating evolutionary studies of this important pathogen. CurSa scripts are accessible via: https://github.com/luisdelaye/CurSa/.
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Affiliation(s)
- Luis Delaye
- Correspondence address. Tel: +52 (462) 623 9600; E-mail:
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36
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Prosperi M, Rife B, Marini S, Salemi M. Transmission cluster characteristics of global, regional, and lineage-specific SARS-CoV-2 phylogenies. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:2940-2944. [PMID: 36780250 PMCID: PMC9912475 DOI: 10.1109/bibm55620.2022.9995364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The SARS-CoV-2 pandemic has been presenting in periodic waves and multiple variants, of which some dominated over time with increased transmissibility. SARS-CoV-2 is still adapting in the human population, thus it is crucial to understand its evolutionary patterns and dynamics ahead of time. In this work, we analyzed transmission clusters and topology of SARS-CoV-2 phylogenies at the global, regional (North America) and clade-specific (Delta and Omicron) epidemic scales. We used the Nextstrain's nCov open global all-time phylogeny (September 2022, 2,698 strains, 2,243 for North America, 499 for Delta21A, and 543 for Omicron20M), with Nextstrain's clade annotation and Pango lineages. Transmission clusters were identified using Phylopart, DYNAMITE, and several tree imbalance measures were calculated, including staircase-ness, Sackin and Colless index. We found that the phylogenetic clustering profiles of the global epidemic have highest diversification at a distance threshold of 3% (divergence of 10, where the tree sampled median is 49). Phylopart and DYNAMITE clusters moderately-to-highly agree with the Pango nomenclature and the Nextstrain's clade. At the regional and clade-specific scale, transmission clustering profiles tend to flatten and similar clusters are found at distance thresholds between 0.05% and 25%. All the considered phylogenies exhibit high tree imbalance with respect to what expected in random phylogenies, suggesting short infection times and antigenic drift, perhaps due to progressive transition from innate to adaptive immunity in the population.
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Affiliation(s)
- Mattia Prosperi
- Department of Epidemiology, College of Public Health and
Health Professions, University of Florida Gainesville, Fl,
USA
| | - Brittany Rife
- Department of Pathology, Immunology and Laboratory
Medicine, College of Medicine, University of Florida
Gainesville, Fl, USA
| | - Simone Marini
- Department of Epidemiology, College of Public Health and
Health Professions, University of Florida Gainesville, Fl,
USA
| | - Marco Salemi
- Department of Pathology, Immunology and Laboratory
Medicine, College of Medicine, University of Florida
Gainesville, Fl, USA
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37
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Chakraborty C, Bhattacharya M, Sharma AR, Dhama K. Evolution, epidemiology, geographical distribution, and mutational landscape of newly emerging monkeypox virus. GeroScience 2022; 44:2895-2911. [PMID: 36094771 PMCID: PMC9466330 DOI: 10.1007/s11357-022-00659-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/05/2022] [Indexed: 01/18/2023] Open
Abstract
Recent monkeypox (MPX) outbreaks are major ones in non-endemic countries. The present study analyzed molecular phylogenetics, divergence, epidemiology, the geographical distribution, entropy diversity of genome, mutational landscape, and evolution of the monkeypox virus (MPXV) genome and the current MPXV is entitled "hMPXV1." We used different in-silico and statistical methods to study our objectives. The developed phylogram from molecular phylogenetics describes the origin and evolution of hMPXV1 of A, A.1, A.1.1, A.2, and B.1 lineages. The microevolution of B.1 lineage shows its evolution from May to August 2022. B.1 lineage is further adapting and showing more mutation and sub-lineages. The scatter plot of all lineages shows the clustering pattern of lineages and the divergence. We also developed two statistical models of confirmed cases and a diagram of the age-related pattern of infected cases to illustrate the epidemiology of the MPX outbreaks. The entropy diversity and mutational landscape of the hMPXV1 genome were analyzed in nucleotide and codon contexts. Our study has shown the in-depth evolution pattern of different lineages of the hMPXV1. We found B.1 lineage is associated with the current outbreaks. The mutational landscape informs about the slow mutation of the virus. Finally, the study might assists the new therapeutic development considering all the above points and would help the researcher to set up their future research directions.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, Odisha, India
| | - Ashish Ranjan Sharma
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-si, 24252, Gangwon-do, Republic of Korea
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, 243122, Bareilly, India
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38
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Wu K, Yang Y, Zhang W, Jiang X, Zhuang W, Gao F, Du Z. Bayesian Phylogeographic Inference Suggests Japan as the Center for the Origin and Dissemination of Rice Stripe Virus. Viruses 2022; 14:v14112547. [PMID: 36423156 PMCID: PMC9698939 DOI: 10.3390/v14112547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/11/2022] [Accepted: 11/16/2022] [Indexed: 11/19/2022] Open
Abstract
Rice stripe virus (RSV) is one of the most important viral pathogens of rice in East Asia. The origin and dispersal of RSV remain poorly understood, but an emerging hypothesis suggests that: (i) RSV originates from Yunnan, a southwest province of China; and (ii) some places of eastern China have acted as a center for the international dissemination of RSV. This hypothesis, however, has never been tested rigorously. Using a data set comprising more than 200 time-stamped coat protein gene sequences of RSV from Japan, China and South Korea, we reconstructed the phylogeographic history of RSV with Bayesian phylogeographic inference. Unexpectedly, the results did not support the abovementioned hypothesis. Instead, they suggested that RSV originates from Japan and Japan has been the major center for the dissemination of RSV in the past decades. Based on these data and the temporal dynamics of RSV reported recently by another group, we proposed a new hypothesis to explain the origin and dispersal of RSV. This new hypothesis may be valuable for further studies aiming to clarify the epidemiology of RSV. It may also be useful in designing management strategies against this devastating virus.
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Affiliation(s)
- Kangcheng Wu
- Fujian Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Ministerial and Provincial Joint Innovation Centre for Safety Production of Cross-Strait Crops, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Yunyue Yang
- Fujian Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Ministerial and Provincial Joint Innovation Centre for Safety Production of Cross-Strait Crops, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Wenwen Zhang
- Fujian Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Ministerial and Provincial Joint Innovation Centre for Safety Production of Cross-Strait Crops, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xiaofeng Jiang
- Fujian Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Ministerial and Provincial Joint Innovation Centre for Safety Production of Cross-Strait Crops, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Weijian Zhuang
- Fujian Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Fangluan Gao
- Fujian Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Correspondence: (F.G.); (Z.D.)
| | - Zhenguo Du
- Fujian Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Ministerial and Provincial Joint Innovation Centre for Safety Production of Cross-Strait Crops, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Correspondence: (F.G.); (Z.D.)
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39
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Aksenova AY, Likhachev IV, Grishin SY, Galzitskaya OV. The Increased Amyloidogenicity of Spike RBD and pH-Dependent Binding to ACE2 May Contribute to the Transmissibility and Pathogenic Properties of SARS-CoV-2 Omicron as Suggested by In Silico Study. Int J Mol Sci 2022; 23:13502. [PMID: 36362302 PMCID: PMC9655063 DOI: 10.3390/ijms232113502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/19/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
SARS-CoV-2 is a rapidly evolving pathogen that has caused a global pandemic characterized by several consecutive waves. Based on epidemiological and NGS data, many different variants of SARS-CoV-2 were described and characterized since the original variant emerged in Wuhan in 2019. Notably, SARS-CoV-2 variants differ in transmissibility and pathogenicity in the human population, although the molecular basis for this difference is still debatable. A significant role is attributed to amino acid changes in the binding surface of the Spike protein to the ACE2 receptor, which may facilitate virus entry into the cell or contribute to immune evasion. We modeled in silico the interaction between Spike RBDs of Wuhan-Hu-1, Delta, and Omicron BA.1 variants and ACE2 at different pHs (pH 5 and pH 7) and showed that the strength of this interaction was higher for the Omicron BA.1 RBD compared to Wuhan-Hu-1 or Delta RBDs and that the effect was more profound at pH 5. This finding is strikingly related to the increased ability of Omicron variants to spread in the population. We also noted that during its spread in the population, SARS-CoV-2 evolved to a more charged, basic composition. We hypothesize that the more basic surface of the Omicron variant may facilitate its spread in the upper respiratory tract but not in the lower respiratory tract, where pH estimates are different. We calculated the amyloidogenic properties of Spike RBDs in different SARS-CoV-2 variants and found eight amyloidogenic regions in the Spike RBDs for each of the variants predicted by the FoldAmyloid program. Although all eight regions were almost identical in the Wuhan to Gamma variants, two of them were significantly longer in both Omicron variants, making the Omicron RBD more amyloidogenic. We discuss how the increased predicted amyloidogenicity of the Omicron variants RBDs may be important for protein stability, influence its interaction with ACE2 and contribute to immune evasion.
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Affiliation(s)
- Anna Y. Aksenova
- Laboratory of Amyloid Biology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Ilya V. Likhachev
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia
- Institute of Mathematical Problems of Biology RAS, The Branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Sergei Y. Grishin
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia
- Institute of Environmental and Agricultural Biology (X-BIO), Tyumen State University, 625003 Tyumen, Russia
| | - Oxana V. Galzitskaya
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
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40
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Douglas J, Winter D, McNeill A, Carr S, Bunce M, French N, Hadfield J, de Ligt J, Welch D, Geoghegan JL. Tracing the international arrivals of SARS-CoV-2 Omicron variants after Aotearoa New Zealand reopened its border. Nat Commun 2022; 13:6484. [PMID: 36309507 PMCID: PMC9617600 DOI: 10.1038/s41467-022-34186-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/18/2022] [Indexed: 12/25/2022] Open
Abstract
In the second quarter of 2022, there was a global surge of emergent SARS-CoV-2 lineages that had a distinct growth advantage over then-dominant Omicron BA.1 and BA.2 lineages. By generating 10,403 Omicron genomes, we show that Aotearoa New Zealand observed an influx of these immune-evasive variants (BA.2.12.1, BA.4, and BA.5) through the border. This is explained by the return to significant levels of international travel following the border's reopening in March 2022. We estimate one Omicron transmission event from the border to the community for every ~5,000 passenger arrivals at the current levels of travel and restriction. Although most of these introductions did not instigate any detected onward transmission, a small minority triggered large outbreaks. Genomic surveillance at the border provides a lens on the rate at which new variants might gain a foothold and trigger new waves of infection.
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Affiliation(s)
- Jordan Douglas
- grid.9654.e0000 0004 0372 3343Centre for Computational Evolution,School of Computer Science, University of Auckland, Auckland, New Zealand
| | - David Winter
- grid.419706.d0000 0001 2234 622XInstitute of Environmental Science and Research, Wellington, New Zealand
| | - Andrea McNeill
- grid.419706.d0000 0001 2234 622XInstitute of Environmental Science and Research, Wellington, New Zealand
| | - Sam Carr
- grid.419706.d0000 0001 2234 622XInstitute of Environmental Science and Research, Wellington, New Zealand
| | - Michael Bunce
- grid.419706.d0000 0001 2234 622XInstitute of Environmental Science and Research, Wellington, New Zealand
| | - Nigel French
- grid.148374.d0000 0001 0696 9806Tāwharau Ora/School of Veterinary Science, Massey University, Palmerston North, New Zealand ,grid.419706.d0000 0001 2234 622XTe Niwha, Infectious Diseases Research Platform, Institute of Environmental Science and Research, Palmerston North, New Zealand
| | - James Hadfield
- grid.270240.30000 0001 2180 1622Fred Hutchinson Cancer Research Centre, Seattle, WA USA
| | - Joep de Ligt
- grid.419706.d0000 0001 2234 622XInstitute of Environmental Science and Research, Wellington, New Zealand
| | - David Welch
- grid.9654.e0000 0004 0372 3343Centre for Computational Evolution,School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Jemma L. Geoghegan
- grid.419706.d0000 0001 2234 622XInstitute of Environmental Science and Research, Wellington, New Zealand ,grid.29980.3a0000 0004 1936 7830Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
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41
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Holt KE, Aanensen DM, Achtman M. Genomic population structures of microbial pathogens. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210230. [PMID: 35989608 PMCID: PMC9393556 DOI: 10.1098/rstb.2021.0230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/28/2022] [Indexed: 11/12/2022] Open
Affiliation(s)
- Kathryn E. Holt
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Mark Achtman
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
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42
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Suster CJE, Arnott A, Blackwell G, Gall M, Draper J, Martinez E, Drew AP, Rockett RJ, Chen SCA, Kok J, Dwyer DE, Sintchenko V. Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection. Front Public Health 2022; 10:1004201. [PMID: 36276383 PMCID: PMC9581317 DOI: 10.3389/fpubh.2022.1004201] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/16/2022] [Indexed: 01/27/2023] Open
Abstract
Genomic surveillance of SARS-CoV-2 has been essential to inform public health response to outbreaks. The high incidence of infection has resulted in a smaller proportion of cases undergoing whole genome sequencing due to finite resources. We present a framework for estimating the impact of reduced depths of genomic surveillance on the resolution of outbreaks, based on a clustering approach using pairwise genetic and temporal distances. We apply the framework to simulated outbreak data to show that outbreaks are detected less frequently when fewer cases are subjected to whole genome sequencing. The impact of sequencing fewer cases depends on the size of the outbreaks, and on the genetic and temporal similarity of the index cases of the outbreaks. We also apply the framework to an outbreak of the SARS-CoV-2 Delta variant in New South Wales, Australia. We find that the detection of clusters in the outbreak would have been delayed if fewer cases had been sequenced. Existing recommendations for genomic surveillance estimate the minimum number of cases to sequence in order to detect and monitor new virus variants, assuming representative sampling of cases. Our method instead measures the resolution of clustering, which is important for genomic epidemiology, and accommodates sampling biases.
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Affiliation(s)
- Carl J. E. Suster
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
| | - Alicia Arnott
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Grace Blackwell
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Mailie Gall
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Jenny Draper
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Elena Martinez
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Alexander P. Drew
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Rebecca J. Rockett
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
| | - Sharon C.-A. Chen
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Jen Kok
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Dominic E. Dwyer
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Vitali Sintchenko
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
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Porter AF, Sherry N, Andersson P, Johnson SA, Duchene S, Howden BP. New rules for genomics-informed COVID-19 responses-Lessons learned from the first waves of the Omicron variant in Australia. PLoS Genet 2022; 18:e1010415. [PMID: 36227810 PMCID: PMC9560517 DOI: 10.1371/journal.pgen.1010415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Ashleigh F. Porter
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Norelle Sherry
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Patiyan Andersson
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Sandra A. Johnson
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Sebastian Duchene
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Benjamin P. Howden
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Microbiological Diagnostic Unit Public Health Laboratory, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
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Palyanova N, Sobolev I, Alekseev A, Glushenko A, Kazachkova E, Markhaev A, Kononova Y, Gulyaeva M, Adamenko L, Kurskaya O, Bi Y, Xin Y, Sharshov K, Shestopalov A. Genomic and Epidemiological Features of COVID-19in the Novosibirsk Region during the Beginning of the Pandemic. Viruses 2022; 14:v14092036. [PMID: 36146842 PMCID: PMC9501018 DOI: 10.3390/v14092036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
In this retrospective, single-center study, we conducted an analysis of 13,699 samples from different individuals obtained from the Federal Research Center of Fundamental and Translational Medicine, from 1 April to 30 May 2020 in Novosibirsk region (population 2.8 million people). We identified 6.49% positive for SARS-CoV-2 cases out of the total number of diagnostic tests, and 42% of them were from asymptomatic people. We also detected two asymptomatic people, who had no confirmed contact with patients with COVID-19. The highest percentage of positive samples was observed in the 80+ group (16.3%), while among the children and adults it did not exceed 8%. Among all the people tested, 2423 came from a total of 80 different destinations and only 27 of them were positive for SARS-CoV-2. Out of all the positive samples, 15 were taken for SARS-CoV-2 sequencing. According to the analysis of the genome sequences, the SARS-CoV-2 variants isolated in the Novosibirsk region at the beginning of the pandemic belonged to three phylogenetic lineages according to the Pangolin classification: B.1, B.1.1, and B.1.1.129. All Novosibirsk isolates contained the D614G substitution in the Spike protein, two isolates werecharacterized by an additional M153T mutation, and one isolate wascharacterized by the L5F mutation.
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Affiliation(s)
- Natalia Palyanova
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
- Correspondence:
| | - Ivan Sobolev
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Alexander Alekseev
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Alexandra Glushenko
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Evgeniya Kazachkova
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Alexander Markhaev
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Yulia Kononova
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Marina Gulyaeva
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
- Department of Natural Science, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Lubov Adamenko
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Olga Kurskaya
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Yuhai Bi
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, Institute of Microbiology, Center for Influenza Research and Early-Warning (CASCIRE), Chinese Academy of Sciences (CAS), Beijing 100101, China
| | - Yuhua Xin
- China General Microbiological Culture Collection Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Kirill Sharshov
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
| | - Alexander Shestopalov
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, 630117 Novosibirsk, Russia
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Kryukov K, Jin L, Nakagawa S. Efficient compression of SARS-CoV-2 genome data using Nucleotide Archival Format. PATTERNS 2022; 3:100562. [PMID: 35818472 PMCID: PMC9259476 DOI: 10.1016/j.patter.2022.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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McLaughlin A, Montoya V, Miller RL, Mordecai GJ, Worobey M, Poon AFY, Joy JB. Genomic epidemiology of the first two waves of SARS-CoV-2 in Canada. eLife 2022; 11:e73896. [PMID: 35916373 PMCID: PMC9345601 DOI: 10.7554/elife.73896] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 07/12/2022] [Indexed: 12/15/2022] Open
Abstract
Tracking the emergence and spread of SARS-CoV-2 lineages using phylogenetics has proven critical to inform the timing and stringency of COVID-19 public health interventions. We investigated the effectiveness of international travel restrictions at reducing SARS-CoV-2 importations and transmission in Canada in the first two waves of 2020 and early 2021. Maximum likelihood phylogenetic trees were used to infer viruses' geographic origins, enabling identification of 2263 (95% confidence interval: 2159-2366) introductions, including 680 (658-703) Canadian sublineages, which are international introductions resulting in sampled Canadian descendants, and 1582 (1501-1663) singletons, introductions with no sampled descendants. Of the sublineages seeded during the first wave, 49% (46-52%) originated from the USA and were primarily introduced into Quebec (39%) and Ontario (36%), while in the second wave, the USA was still the predominant source (43%), alongside a larger contribution from India (16%) and the UK (7%). Following implementation of restrictions on the entry of foreign nationals on 21 March 2020, importations declined from 58.5 (50.4-66.5) sublineages per week to 10.3-fold (8.3-15.0) lower within 4 weeks. Despite the drastic reduction in viral importations following travel restrictions, newly seeded sublineages in summer and fall 2020 contributed to the persistence of COVID-19 cases in the second wave, highlighting the importance of sustained interventions to reduce transmission. Importations rebounded further in November, bringing newly emergent variants of concern (VOCs). By the end of February 2021, there had been an estimated 30 (19-41) B.1.1.7 sublineages imported into Canada, which increasingly displaced previously circulating sublineages by the end of the second wave.Although viral importations are nearly inevitable when global prevalence is high, with fewer importations there are fewer opportunities for novel variants to spark outbreaks or outcompete previously circulating lineages.
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Affiliation(s)
- Angela McLaughlin
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
- Bioinformatics, University of British ColumbiaVancouverCanada
| | - Vincent Montoya
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
| | - Rachel L Miller
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
- Bioinformatics, University of British ColumbiaVancouverCanada
| | - Gideon J Mordecai
- Department of Medicine, University of British ColumbiaVancouverCanada
| | | | - Michael Worobey
- Department of Ecology and Evolution, University of ArizonaTucsonUnited States
| | - Art FY Poon
- Department of Pathology and Laboratory Medicine, Western UniversityLondonCanada
| | - Jeffrey B Joy
- British Columbia Centre for Excellence in HIV/AIDSVancouverCanada
- Bioinformatics, University of British ColumbiaVancouverCanada
- Department of Medicine, University of British ColumbiaVancouverCanada
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48
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Global cooperation for a global pandemic. Nat Rev Genet 2022; 23:519. [PMID: 35869288 PMCID: PMC9305058 DOI: 10.1038/s41576-022-00522-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Haan TJ, Smith LK, DeRonde S, House E, Zidek J, Puhak D, Mullen L, Redlinger M, Parker J, Barnes BM, Burkhead JL, Knall C, Bortz E, Chen J, Drown DM. A repeat pattern of founder events for SARS-CoV-2 variants in Alaska. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.05.25.22275610. [PMID: 35664999 PMCID: PMC9164444 DOI: 10.1101/2022.05.25.22275610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Alaska is a unique US state because of its large size, geographically disparate population density, and physical distance from the contiguous United States. Here, we describe a pattern of SARS-CoV-2 variant emergence across Alaska reflective of these differences. Using genomic data, we found that in Alaska the Omicron sublineage BA.2.3 overtook BA.1.1 by the week of 2022-02-27, reaching 48.5% of sequenced cases. On the contrary in the contiguous United States, BA.1.1 dominated cases for longer, eventually being displaced by BA.2 sublineages other than BA.2.3. BA.2.3 only reached a prevalence of 10.9% in the contiguous United States. Using phylogenetics, we found evidence of potential origins of the two major clades of BA.2.3 in Alaska and with logistic regression estimated how it emerged and spread throughout the state. The combined evidence is suggestive of founder events in Alaska and is reflective of how Alaska’s unique dynamics influence the emergence of SARS-CoV-2 variants.
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Affiliation(s)
- Tracie J. Haan
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, USA
| | - Lisa K. Smith
- Alaska Division of Public Health, State of Alaska, Fairbanks, Alaska, USA
| | - Stephanie DeRonde
- Alaska Division of Public Health, State of Alaska, Fairbanks, Alaska, USA
| | - Elva House
- Alaska Division of Public Health, State of Alaska, Fairbanks, Alaska, USA
| | - Jacob Zidek
- Alaska Division of Public Health, State of Alaska, Fairbanks, Alaska, USA
| | - Diana Puhak
- Alaska Division of Public Health, State of Alaska, Fairbanks, Alaska, USA
| | - Logan Mullen
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, USA
| | - Matthew Redlinger
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, Alaska, USA
| | - Jayme Parker
- Alaska Division of Public Health, State of Alaska, Fairbanks, Alaska, USA
| | - Brian M. Barnes
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, USA
| | - Jason L. Burkhead
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, Alaska, USA
| | - Cindy Knall
- WWAMI School of Medical Education, University of Alaska Anchorage, Anchorage, Alaska, USA
| | - Eric Bortz
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, Alaska, USA
- WWAMI School of Medical Education, University of Alaska Anchorage, Anchorage, Alaska, USA
| | - Jack Chen
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, USA
- Alaska Division of Public Health, State of Alaska, Fairbanks, Alaska, USA
- Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, Alaska, USA
| | - Devin M. Drown
- Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, USA
- Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, Alaska, USA
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