39 results on '"John T McCrone"'
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2. Implementation of genomic surveillance of SARS-CoV-2 in the Caribbean: Lessons learned for sustainability in resource-limited settings.
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Nikita S D Sahadeo, Soren Nicholls, Filipe R R Moreira, Áine O'Toole, Vernie Ramkissoon, Charles Whittaker, Verity Hill, John T McCrone, Nicholas Mohammed, Anushka Ramjag, Arianne Brown Jordan, Sarah C Hill, Risha Singh, Sue-Min Nathaniel-Girdharrie, Avery Hinds, Nuala Ramkissoon, Kris V Parag, Naresh Nandram, Roshan Parasram, Zobida Khan-Mohammed, Lisa Edghill, Lisa Indar, Aisha Andrewin, Rhonda Sealey-Thomas, Pearl McMillan, Ayoola Oyinloye, Kenneth George, Irad Potter, John Lee, David Johnson, Shawn Charles, Narine Singh, Jacquiline Bisesor-McKenzie, Hazel Laws, Sharon Belmar-George, Simone Keizer-Beache, Sharra Greenaway-Duberry, Nadia Ashwood, Jerome E Foster, Karla Georges, Rahul Naidu, Marsha Ivey, Stanley Giddings, Rajini Haraksingh, Adesh Ramsubhag, Jayaraj Jayaraman, Chinnaraja Chinnadurai, Christopher Oura, Oliver G Pybus, Joy St John, Gabriel Gonzalez-Escobar, Nuno R Faria, and Christine V F Carrington
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Public aspects of medicine ,RA1-1270 - Abstract
The COVID-19 pandemic highlighted the importance of global genomic surveillance to monitor the emergence and spread of SARS-CoV-2 variants and inform public health decision-making. Until December 2020 there was minimal capacity for viral genomic surveillance in most Caribbean countries. To overcome this constraint, the COVID-19: Infectious disease Molecular epidemiology for PAthogen Control & Tracking (COVID-19 IMPACT) project was implemented to establish rapid SARS-CoV-2 whole genome nanopore sequencing at The University of the West Indies (UWI) in Trinidad and Tobago (T&T) and provide needed SARS-CoV-2 sequencing services for T&T and other Caribbean Public Health Agency Member States (CMS). Using the Oxford Nanopore Technologies MinION sequencing platform and ARTIC network sequencing protocols and bioinformatics pipeline, a total of 3610 SARS-CoV-2 positive RNA samples, received from 17 CMS, were sequenced in-situ during the period December 5th 2020 to December 31st 2021. Ninety-one Pango lineages, including those of five variants of concern (VOC), were identified. Genetic analysis revealed at least 260 introductions to the CMS from other global regions. For each of the 17 CMS, the percentage of reported COVID-19 cases sequenced by the COVID-19 IMPACT laboratory ranged from 0·02% to 3·80% (median = 1·12%). Sequences submitted to GISAID by our study represented 73·3% of all SARS-CoV-2 sequences from the 17 CMS available on the database up to December 31st 2021. Increased staffing, process and infrastructural improvement over the course of the project helped reduce turnaround times for reporting to originating institutions and sequence uploads to GISAID. Insights from our genomic surveillance network in the Caribbean region directly influenced non-pharmaceutical countermeasures in the CMS countries. However, limited availability of associated surveillance and clinical data made it challenging to contextualise the observed SARS-CoV-2 diversity and evolution, highlighting the need for development of infrastructure for collecting and integrating genomic sequencing data and sample-associated metadata.
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- 2023
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3. A speed-fidelity trade-off determines the mutation rate and virulence of an RNA virus.
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William J Fitzsimmons, Robert J Woods, John T McCrone, Andrew Woodman, Jamie J Arnold, Madhumita Yennawar, Richard Evans, Craig E Cameron, and Adam S Lauring
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Biology (General) ,QH301-705.5 - Abstract
Mutation rates can evolve through genetic drift, indirect selection due to genetic hitchhiking, or direct selection on the physicochemical cost of high fidelity. However, for many systems, it has been difficult to disentangle the relative impact of these forces empirically. In RNA viruses, an observed correlation between mutation rate and virulence has led many to argue that their extremely high mutation rates are advantageous because they may allow for increased adaptability. This argument has profound implications because it suggests that pathogenesis in many viral infections depends on rare or de novo mutations. Here, we present data for an alternative model whereby RNA viruses evolve high mutation rates as a byproduct of selection for increased replicative speed. We find that a poliovirus antimutator, 3DG64S, has a significant replication defect and that wild-type (WT) and 3DG64S populations have similar adaptability in 2 distinct cellular environments. Experimental evolution of 3DG64S under selection for replicative speed led to reversion and compensation of the fidelity phenotype. Mice infected with 3DG64S exhibited delayed morbidity at doses well above the lethal level, consistent with attenuation by slower growth as opposed to reduced mutational supply. Furthermore, compensation of the 3DG64S growth defect restored virulence, while compensation of the fidelity phenotype did not. Our data are consistent with the kinetic proofreading model for biosynthetic reactions and suggest that speed is more important than accuracy. In contrast with what has been suggested for many RNA viruses, we find that within-host spread is associated with viral replicative speed and not standing genetic diversity.
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- 2018
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4. Stochastic processes constrain the within and between host evolution of influenza virus
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John T McCrone, Robert J Woods, Emily T Martin, Ryan E Malosh, Arnold S Monto, and Adam S Lauring
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influenza virus ,bottleneck ,transmission ,diversity ,evolution ,Medicine ,Science ,Biology (General) ,QH301-705.5 - Abstract
The evolutionary dynamics of influenza virus ultimately derive from processes that take place within and between infected individuals. Here we define influenza virus dynamics in human hosts through sequencing of 249 specimens from 200 individuals collected over 6290 person-seasons of observation. Because these viruses were collected from individuals in a prospective community-based cohort, they are broadly representative of natural infections with seasonal viruses. Consistent with a neutral model of evolution, sequence data from 49 serially sampled individuals illustrated the dynamic turnover of synonymous and nonsynonymous single nucleotide variants and provided little evidence for positive selection of antigenic variants. We also identified 43 genetically-validated transmission pairs in this cohort. Maximum likelihood optimization of multiple transmission models estimated an effective transmission bottleneck of 1–2 genomes. Our data suggest that positive selection is inefficient at the level of the individual host and that stochastic processes dominate the host-level evolution of influenza viruses.
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- 2018
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5. Vaccination has minimal impact on the intrahost diversity of H3N2 influenza viruses.
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Kari Debbink, John T McCrone, Joshua G Petrie, Rachel Truscon, Emileigh Johnson, Emily K Mantlo, Arnold S Monto, and Adam S Lauring
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Immunologic diseases. Allergy ,RC581-607 ,Biology (General) ,QH301-705.5 - Abstract
While influenza virus diversity and antigenic drift have been well characterized on a global scale, the factors that influence the virus' rapid evolution within and between human hosts are less clear. Given the modest effectiveness of seasonal vaccination, vaccine-induced antibody responses could serve as a potent selective pressure for novel influenza variants at the individual or community level. We used next generation sequencing of patient-derived viruses from a randomized, placebo-controlled trial of vaccine efficacy to characterize the diversity of influenza A virus and to define the impact of vaccine-induced immunity on within-host populations. Importantly, this study design allowed us to isolate the impact of vaccination while still studying natural infection. We used pre-season hemagglutination inhibition and neuraminidase inhibition titers to quantify vaccine-induced immunity directly and to assess its impact on intrahost populations. We identified 166 cases of H3N2 influenza over 3 seasons and 5119 person-years. We obtained whole genome sequence data for 119 samples and used a stringent and empirically validated analysis pipeline to identify intrahost single nucleotide variants at ≥1% frequency. Phylogenetic analysis of consensus hemagglutinin and neuraminidase sequences showed no stratification by pre-season HAI and NAI titer, respectively. In our study population, we found that the vast majority of intrahost single nucleotide variants were rare and that very few were found in more than one individual. Most samples had fewer than 15 single nucleotide variants across the entire genome, and the level of diversity did not significantly vary with day of sampling, vaccination status, or pre-season antibody titer. Contrary to what has been suggested in experimental systems, our data indicate that seasonal influenza vaccination has little impact on intrahost diversity in natural infection and that vaccine-induced immunity may be only a minor contributor to antigenic drift at local scales.
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- 2017
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6. The Mutational Robustness of Influenza A Virus.
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Elisa Visher, Shawn E Whitefield, John T McCrone, William Fitzsimmons, and Adam S Lauring
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Immunologic diseases. Allergy ,RC581-607 ,Biology (General) ,QH301-705.5 - Abstract
A virus' mutational robustness is described in terms of the strength and distribution of the mutational fitness effects, or MFE. The distribution of MFE is central to many questions in evolutionary theory and is a key parameter in models of molecular evolution. Here we define the mutational fitness effects in influenza A virus by generating 128 viruses, each with a single nucleotide mutation. In contrast to mutational scanning approaches, this strategy allowed us to unambiguously assign fitness values to individual mutations. The presence of each desired mutation and the absence of additional mutations were verified by next generation sequencing of each stock. A mutation was considered lethal only after we failed to rescue virus in three independent transfections. We measured the fitness of each viable mutant relative to the wild type by quantitative RT-PCR following direct competition on A549 cells. We found that 31.6% of the mutations in the genome-wide dataset were lethal and that the lethal fraction did not differ appreciably between the HA- and NA-encoding segments and the rest of the genome. Of the viable mutants, the fitness mean and standard deviation were 0.80 and 0.22 in the genome-wide dataset and best modeled as a beta distribution. The fitness impact of mutation was marginally lower in the segments coding for HA and NA (0.88 ± 0.16) than in the other 6 segments (0.78 ± 0.24), and their respective beta distributions had slightly different shape parameters. The results for influenza A virus are remarkably similar to our own analysis of CirSeq-derived fitness values from poliovirus and previously published data from other small, single stranded DNA and RNA viruses. These data suggest that genome size, and not nucleic acid type or mode of replication, is the main determinant of viral mutational fitness effects.
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- 2016
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7. Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome.
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Simeone Marino, Hannah P Gideon, Chang Gong, Shawn Mankad, John T McCrone, Philana Ling Lin, Jennifer J Linderman, JoAnne L Flynn, and Denise E Kirschner
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Biology (General) ,QH301-705.5 - Abstract
Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2- year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. We emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.
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- 2016
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8. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil
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Esmenia C. Rocha, Vitor H. Nascimento, Pedro S. Peixoto, Thomas A. Mellan, Lucas A M Franco, Henrique Hoeltgebaum, Michaela A. C. Vollmer, Oliver Ratmann, Leonardo José Tadeu de Araújo, Nuno R. Faria, Philippe Lemey, Raphael Sonabend, Myuki A E Crispim, Nelson Gaburo, Charles Whittaker, Joice do P. Silva, Ruben J.G. Hulswit, Ricardo P Schnekenberg, Cecilia da C. Camilo, Mariana C. Pinho, Darlan da Silva Candido, Neil M. Ferguson, Helem M. dos Santos, Ester Cerdeira Sabino, Patrick G T Walker, Hannah M. Schlüter, Carlos A. Prete, Thais M. Coletti, Erika R. Manuli, Oliver G. Pybus, Samir Bhatt, Alessandro C. S. Ferreira, Mariana S. Ramundo, Danielle A G Zauli, Aline B. de Lima, Jaqueline Goes de Jesus, Iwona Hawryluk, Frederico S V Malta, Marc A. Suchard, Leandro Marques de Souza, Seth Flaxman, Moritz U. G. Kraemer, James A. Hay, Valentina S. Del Caro, Rosinaldo M. F. Filho, Axel Gandy, Pamela S Andrade, Andrew Rambaut, Ingra Morales Claro, Ana L. P. dos Santos, Christopher Dye, José Luiz Proença-Módena, Swapnil Mishra, Daniel J Laydon, William Marciel de Souza, Renato Santana Aguiar, Xenia Miscouridou, Camila A. M. Silva, Maria S. Vidal, John T. McCrone, Maria Perpétuo Socorro Sampaio Carvalho, Nicholas J. Loman, Giulia M. Ferreira, Bruce Walker Nelson, Chieh-Hsi Wu, Thomas A. Bowden, Melodie Monod, Helen Coupland, Rafael Henrique Moraes Pereira, Flavia C. S. Sales, Sergei L Kosakovsky Pond, Nelson Abrahim Fraiji, Bill & Melinda Gates Foundation, Wellcome Trust, and Medical Research Council (MRC)
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DYNAMICS ,0301 basic medicine ,MATEMÁTICA APLICADA ,Lineage (genetic) ,General Science & Technology ,LOCAL TRANSMISSION ,Genomics ,Genome, Viral ,Communicable Diseases, Emerging ,Genome ,DNA sequencing ,03 medical and health sciences ,0302 clinical medicine ,Molecular evolution ,Humans ,030212 general & internal medicine ,Molecular clock ,Molecular Epidemiology ,SITES ,Science & Technology ,RECEPTOR-BINDING DOMAIN ,Multidisciplinary ,Molecular epidemiology ,biology ,SARS-CoV-2 ,MOLECULAR CLOCK ,COVID-19 ,Models, Theoretical ,Viral Load ,PERFORMANCE ,biology.organism_classification ,Virology ,3. Good health ,Multidisciplinary Sciences ,030104 developmental biology ,Epidemiological Monitoring ,Mutation ,Spike Glycoprotein, Coronavirus ,Science & Technology - Other Topics ,Angiotensin-Converting Enzyme 2 ,RESPIRATORY-SYNDROME-CORONAVIRUS ,Brazil ,Betacoronavirus ,Protein Binding - Abstract
Unmitigated spread in Brazil Despite an extensive network of primary care availability, Brazil has suffered profoundly during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Using daily data from state health offices, Castro et al. analyzed the pattern of spread of COVID-19 cases and deaths in the country from February to October 2020. Clusters of deaths before cases became apparent indicated unmitigated spread. SARS-CoV-2 circulated undetected in Brazil for more than a month as it spread north from Sã o Paulo. In Manaus, transmission reached unprecedented levels after a momentary respite in mid-2020. Faria et al. tracked the evolution of a new, more aggressive lineage called P.1, which has 17 mutations, including three (K417T, E484K, and N501Y) in the spike protein. After a period of accelerated evolution, this variant emerged in Brazil during November 2020. Coupled with the emergence of P.1, disease spread was accelerated by stark local inequalities and political upheaval, which compromised a prompt federal response. Science , abh1558 and abh2644, this issue p. 821 and p. 815
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- 2021
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9. Evidence for SARS-CoV-2 Delta and Omicron co-infections and recombination
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Alexandre Bolze, Tracy Basler, Simon White, Andrew Dei Rossi, Dana Wyman, Hang Dai, Pavitra Roychoudhury, Alexander L. Greninger, Kathleen Hayashibara, Mark Beatty, Seema Shah, Sarah Stous, John T. McCrone, Eric Kil, Tyler Cassens, Kevin Tsan, Jason Nguyen, Jimmy Ramirez, Scotty Carter, Elizabeth T. Cirulli, Kelly Schiabor Barrett, Nicole L. Washington, Pedro Belda-Ferre, Sharoni Jacobs, Efren Sandoval, David Becker, James T. Lu, Magnus Isaksson, William Lee, and Shishi Luo
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Coinfection ,SARS-CoV-2 ,Humans ,COVID-19 ,General Medicine ,Genome, Viral ,Orthopoxvirus - Abstract
Between November 2021 and February 2022, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta and Omicron variants co-circulated in the United States, allowing for co-infections and possible recombination events.We sequenced 29,719 positive samples during this period and analyzed the presence and fraction of reads supporting mutations specific to either the Delta or Omicron variant.We identified 18 co-infections, one of which displayed evidence of a low Delta-Omicron recombinant viral population. We also identified two independent cases of infection by a Delta-Omicron recombinant virus, where 100% of the viral RNA came from one clonal recombinant. In the three cases, the 5' end of the viral genome was from the Delta genome and the 3' end from Omicron, including the majority of the spike protein gene, though the breakpoints were different.Delta-Omicron recombinant viruses were rare, and there is currently no evidence that Delta-Omicron recombinant viruses are more transmissible between hosts compared with the circulating Omicron lineages.This research was supported by the NIH RADx initiative and by the Centers for Disease Control Contract 75D30121C12730 (Helix).
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- 2022
10. Genomic epidemiology of early SARS-CoV-2 transmission dynamics, Gujarat, India
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Jayna Raghwani, Louis du Plessis, John T. McCrone, Sarah C. Hill, Kris V. Parag, Julien Thézé, Dinesh Kumar, Apurva Puvar, Ramesh Pandit, Oliver G. Pybus, Guillaume Fournié, Madhvi Joshi, and Chaitanya Joshi
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Microbiology (medical) ,Infectious Diseases ,Epidemiology ,SARS-CoV-2 ,COVID-19 ,Humans ,India ,Genome, Viral ,Genomics ,Phylogeny - Abstract
Limited genomic sampling in many high-incidence countries has impeded studies of severe respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic epidemiology. Consequently, critical questions remain about the generation and global distribution of virus genetic diversity. We investigated SARS-CoV-2 transmission dynamics in Gujarat, India, during the state’s first epidemic wave to shed light on spread of the virus in one of the regions hardest hit by the pandemic. By integrating case data and 434 whole-genome sequences sampled across 20 districts, we reconstructed the epidemic dynamics and spatial spread of SARS-CoV-2 in Gujarat. Our findings indicate global and regional connectivity and population density were major drivers of the Gujarat outbreak. We detected >100 virus lineage introductions, most of which appear to be associated with international travel. Within Gujarat, virus dissemination occurred predominantly from densely populated regions to geographically proximate locations that had low population density, suggesting that urban centers contributed disproportionately to virus spread.
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- 2022
11. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology
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Edward C. Holmes, Louis du Plessis, Áine O'Toole, Verity Hill, Andrew Rambaut, Oliver G. Pybus, John T. McCrone, and Christopher Ruis
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Microbiology (medical) ,Lineage (evolution) ,viruses ,Immunology ,Biology ,medicine.disease_cause ,Genome ,Applied Microbiology and Biotechnology ,Microbiology ,Article ,03 medical and health sciences ,Phylogenetics ,medicine ,Genetics ,Virus classification ,030304 developmental biology ,Coronavirus ,0303 health sciences ,Phylogenetic tree ,030306 microbiology ,COVID-19 ,Cell Biology ,Phylogenetic diversity ,Evolutionary biology ,Viral evolution - Abstract
The ongoing pandemic spread of a new human coronavirus, SARS-CoV-2, which is associated with severe pneumonia/disease (COVID-19), has resulted in the generation of tens of thousands of virus genome sequences. The rate of genome generation is unprecedented, yet there is currently no coherent nor accepted scheme for naming the expanding phylogenetic diversity of SARS-CoV-2. Here, we present a rational and dynamic virus nomenclature that uses a phylogenetic framework to identify those lineages that contribute most to active spread. Our system is made tractable by constraining the number and depth of hierarchical lineage labels and by flagging and delabelling virus lineages that become unobserved and hence are probably inactive. By focusing on active virus lineages and those spreading to new locations, this nomenclature will assist in tracking and understanding the patterns and determinants of the global spread of SARS-CoV-2.
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- 2020
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12. Assignment of Epidemiological Lineages in an Emerging Pandemic Using the Pangolin Tool
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Stephen W Attwood, Rachel M. Colquhoun, Louis du Plessis, Anthony Underwood, John T. McCrone, Edward C. Holmes, Andrew Rambaut, Daniel Maloney, David M. Aanensen, Áine O'Toole, Nathan C Medd, Ben Jackson, Verity Hill, Corin Yeats, Khalil Abudahab, Oliver G. Pybus, Ben Taylor, Emily Scher, Christopher Ruis, O'Toole, Áine [0000-0001-8083-474X], Scher, Emily [0000-0002-5401-5879], Jackson, Ben [0000-0002-9981-0649], Hill, Verity [0000-0002-3509-8146], Colquhoun, Rachel [0000-0002-5577-9897], Yeats, Corin [0000-0003-0080-6242], Medd, Nathan [0000-0001-7833-5909], Attwood, Stephen W [0000-0003-1717-1677], Holmes, Edward C [0000-0001-9596-3552], Pybus, Oliver G [0000-0002-8797-2667], Rambaut, Andrew [0000-0003-4337-3707], and Apollo - University of Cambridge Repository
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Lineage (evolution) ,viruses ,Genomics ,Microbiology ,Genome ,03 medical and health sciences ,Phylogenetics ,Virology ,Pandemic ,AcademicSubjects/MED00860 ,030304 developmental biology ,Whole genome sequencing ,0303 health sciences ,SARS-CoV-2 ,genomic surveillance ,phylogenetics ,software ,lineage ,biology ,Phylogenetic tree ,030306 microbiology ,Pangolin ,AcademicSubjects/SCI01130 ,AcademicSubjects/SCI02285 ,COVID-19 ,biology.organism_classification ,Resources ,3. Good health ,Coronavirus ,Evolutionary biology - Abstract
The response of the global virus genomics community to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been unprecedented, with significant advances made towards the ‘real-time’ generation and sharing of SARS-CoV-2 genomic data. The rapid growth in virus genome data production has necessitated the development of new analytical methods that can deal with orders of magnitude of more genomes than previously available. Here, we present and describe Phylogenetic Assignment of Named Global Outbreak Lineages (pangolin), a computational tool that has been developed to assign the most likely lineage to a given SARS-CoV-2 genome sequence according to the Pango dynamic lineage nomenclature scheme. To date, nearly two million virus genomes have been submitted to the web-application implementation of pangolin, which has facilitated the SARS-CoV-2 genomic epidemiology and provided researchers with access to actionable information about the pandemic’s transmission lineages., Virus Evolution, 7 (2), ISSN:2057-1577
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- 2021
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13. Genomic epidemiology of early SARS-CoV-2 transmission dynamics in Gujarat, India
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Ramesh J. Pandit, Chaitanya Joshi, Guillaume Fournie, Louis du Plessis, Sarah C. Hill, Madhvi Joshi, Kris V Parag, John T. McCrone, Apurva Puvar, Oliver G. Pybus, Julien Thézé, Jayna Raghwani, and Dinesh Kumar
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medicine.medical_specialty ,Genetic diversity ,viruses ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Outbreak ,Population density ,Virus ,law.invention ,Transmission (mechanics) ,Geography ,law ,Epidemiology ,Pandemic ,medicine ,Socioeconomics - Abstract
Genomic surveillance of SARS-CoV-2 has played a decisive role in understanding the transmission and evolution of the virus during its emergence and continued circulation. However, limited genomic sampling in many high-incidence countries has impeded detailed studies of SARS-CoV-2 genomic epidemiology. Consequently, critical questions remain about the generation and global distribution of virus genetic diversity. To address this gap, we investigated SARS-CoV-2 transmission dynamics in Gujarat, India, during its first epidemic wave and shed light on virus’ spread in one of the pandemic’s hardest-hit regions. By integrating regional case data and 434 whole virus genome sequences sampled across 20 districts from March to July 2020, we reconstructed the epidemic dynamics and spatial spread of SARS-CoV-2 in Gujarat, India. Our findings revealed that global and regional connectivity, along with population density, were significant drivers of the Gujarat SARS-CoV-2 outbreak. The three most populous districts in Gujarat accounted ∼84% of total cases during the first wave. Moreover, we detected over 100 virus lineage introductions, which were primarily associated with international travel. Within Gujarat, virus dissemination occurred predominantly from densely populated regions to geographically proximate locations with low-population density. Our findings suggest SARS-CoV-2 transmission follows a gravity model in India, with urban centres contributing disproportionately to onward virus spread.
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- 2021
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14. Generation and transmission of inter-lineage recombinants in the SARS-CoV-2 pandemic
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Ben Jackson, Amy Colleran, Alistair C. Darby, Emily Scher, Matthew J. Bull, Samuel M. Nicholls, Flora Todd, David Robertson, Nicholas J. Loman, Hermione J. Webster, Rachel M. Colquhoun, Sam Haldenby, Radoslaw Poplawski, John T. McCrone, Mark Whitehead, Andrew Rambaut, Thomas R. Connor, Oliver G. Pybus, Claudia Wierzbicki, Nicole Pacchiarini, Anita Lucaci, Áine O'Toole, Maciej F. Boni, and Verity Hill
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Genetics ,Transmission (mechanics) ,Lineage (genetic) ,law ,Genetic variation ,Recombinant DNA ,Single-nucleotide polymorphism ,Biology ,Recombinant virus ,Genome ,Virus ,law.invention - Abstract
SummaryWe present evidence for multiple independent origins of recombinant SARS-CoV-2 viruses sampled from late 2020 and early 2021 in the United Kingdom. Their genomes carry single nucleotide polymorphisms and deletions that are characteristic of the B.1.1.7 variant of concern, but lack the full complement of lineage-defining mutations. Instead, the remainder of their genomes share contiguous genetic variation with non-B.1.1.7 viruses circulating in the same geographic area at the same time as the recombinants. In four instances there was evidence for onward transmission of a recombinant-origin virus, including one transmission cluster of 45 sequenced cases over the course of two months. The inferred genomic locations of recombination breakpoints suggest that every community-transmitted recombinant virus inherited its spike region from a B.1.1.7 parental virus, consistent with a transmission advantage for B.1.1.7’s set of mutations.
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- 2021
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15. Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador
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Denisse Portugal, Mónica Becerra-Wong, Marina Escalera-Zamudio, Andrés Carrazco-Montalvo, Paul Cardenas, Leandro Patiño, Manuel Gonzalez, Josefina Coloma, Sully Márquez, Orson Mestanza, Gabriel Trueba, Oliver G. Pybus, Arne Kühne, Alberto Orlando, John T. McCrone, Domenica de Mora, Gabriela Sevillano, Nuno R. Faria, Rubén Armas-Gonzalez, Maritza Olmedo, Juan Carlos Fernandez-Cadena, Bernardo Gutierrez, Veronica Barragan, Moritz U. G. Kraemer, Alfredo Bruno, Louis du Plessis, Darlan da Silva Candido, Belén Prado-Vivar, Gabriel Morey-León, Jan Felix Drexler, Derly Andrade-Molina, Juan José Guadalupe, Anna-Lena Sander, Andres Moreira-Soto, Patricio Rojas-Silva, Michelle Grunauer, Sebastian Brünink, and Jeannete Zurita
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medicine.medical_specialty ,media_common.quotation_subject ,Context (language use) ,phylogeography ,molecular epidemiology ,Microbiology ,Genome ,Article ,law.invention ,03 medical and health sciences ,law ,Virology ,Epidemiology ,Pandemic ,medicine ,AcademicSubjects/MED00860 ,Molecular clock ,030304 developmental biology ,media_common ,0303 health sciences ,Genetic diversity ,Molecular epidemiology ,SARS-CoV-2 ,030306 microbiology ,AcademicSubjects/SCI01130 ,AcademicSubjects/SCI02285 ,phylogenetics ,Phylogeography ,Geography ,Transmission (mechanics) ,Evolutionary biology ,transmission lineages ,Research Article ,Diversity (politics) - Abstract
Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination., Virus Evolution, 7 (2), ISSN:2057-1577
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- 2021
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16. Addendum: A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology
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John T. McCrone, Áine O'Toole, Verity Hill, Christopher Ruis, Oliver G. Pybus, Louis du Plessis, Edward C. Holmes, and Andrew Rambaut
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Microbiology (medical) ,medicine.medical_specialty ,2019-20 coronavirus outbreak ,Genotype ,Coronavirus disease 2019 (COVID-19) ,Classification and taxonomy ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Pneumonia, Viral ,Immunology ,Genomics ,Genome, Viral ,Biology ,medicine.disease_cause ,Applied Microbiology and Biotechnology ,Microbiology ,Betacoronavirus ,Epidemiology ,medicine ,Genetics ,Humans ,Viral evolution ,Pandemics ,Nomenclature ,Phylogeny ,Coronavirus ,Molecular Epidemiology ,SARS-CoV-2 ,COVID-19 ,Cell Biology ,Virology ,Addendum ,Phylogenetics ,Coronavirus Infections - Abstract
The ongoing pandemic spread of a new human coronavirus, SARS-CoV-2, which is associated with severe pneumonia/disease (COVID-19), has resulted in the generation of tens of thousands of virus genome sequences. The rate of genome generation is unprecedented, yet there is currently no coherent nor accepted scheme for naming the expanding phylogenetic diversity of SARS-CoV-2. Here, we present a rational and dynamic virus nomenclature that uses a phylogenetic framework to identify those lineages that contribute most to active spread. Our system is made tractable by constraining the number and depth of hierarchical lineage labels and by flagging and delabelling virus lineages that become unobserved and hence are probably inactive. By focusing on active virus lineages and those spreading to new locations, this nomenclature will assist in tracking and understanding the patterns and determinants of the global spread of SARS-CoV-2.
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- 2021
- Full Text
- View/download PDF
17. Epidemic waves of COVID-19 in Scotland: a genomic perspective on the impact of the introduction and relaxation of lockdown on SARS-CoV-2
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Rachel M. Colquhoun, David Robertson, Stephen Carmichael, Gianluigi Rossi, Tom Stanton, Andrew Rambaut, James Shepherd, Stefan Rooke, Ana da Silva Filipe, Amy Shepherd, Alasdair MacLean, Elihu Aranday-Cortes, Carlos E Balcazar-Lopez, Verity Hill, Igor Starinskij, Lu Lu, Katherine Smollett, Kathy Li, Michael Gallagher, Kathleen A. Williamson, John T. McCrone, Rory Gunson, Ben Jackson, Thomas C Williams, Rebecca Dewar, Kirstyn Brunker, Rhys Inward, Sharif Shaaban, Martin P McHugh, Kate Templeton, Seb Cotton, Mark E. J. Woolhouse, Daniel Balaz, Alice Broos, Sarah E. McDonald, Rajiv Shah, Jenna Nichols, Lily Tong, Thomas Doherty, Rowland R. Kao, Áine O'Toole, Natasha Johnson, Patawee Asamaphan, Yasmin A Parr, Vattipally B. Sreenu, E. Thomson, Marc Niebel, Natasha Jesudason, Daniel Mair, Kyriaki Nomikou, Emily Scher, Matthew T. G. Holden, Samantha Lycett, and Joseph Hughes
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medicine.medical_specialty ,education.field_of_study ,Coronavirus disease 2019 (COVID-19) ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Public health ,Population ,National health service ,Quarter (United States coin) ,Geography ,Epidemiology ,medicine ,Health board ,education ,Demography - Abstract
The second SARS virus, SARS-CoV-2, emerged in December 2019, and within a month was globally distributed. It was first introduced into Scotland in February 2020 associated with returning travellers and visitors. By March it was circulating in communities across the UK, and to control COVID-19 cases, and prevent overwhelming of the National Health Service (NHS), a ‘lockdown’ was introduced on 23rd March 2020 with a restriction of people’s movements. To augment the public health efforts a large-scale genome epidemiology effort (as part of the COVID-19 Genomics UK (COG-UK) consortium) resulted in the sequencing of over 5000 SARS-CoV-2 genomes by 18th August 2020 from Scottish cases, about a quarter of the estimated number of cases at that time. Here we quantify the geographical origins of the first wave introductions into Scotland from abroad and other UK regions, the spread of these SARS-CoV-2 lineages to different regions within Scotland (defined at the level of NHS Health Board) and the effect of lockdown on virus ‘success’. We estimate that approximately 300 introductions seeded lineages in Scotland, with around 25% of these lineages composed of more than five viruses, but by June circulating lineages were reduced to low levels, in line with low numbers of recorded positive cases. Lockdown was, thus, associated with a dramatic reduction in infection numbers and the extinguishing of most virus lineages. Unfortunately since the summer cases have been rising in Scotland in a second wave, with >1000 people testing positive on a daily basis, and hospitalisation of COVID-19 cases on the rise again. Examining the available Scottish genome data from the second wave, and comparing it to the first wave, we find that while some UK lineages have persisted through the summer, the majority of lineages responsible for the second wave are new introductions from outside of Scotland and many from outside of the UK. This indicates that, while lockdown in Scotland is directly linked with the first wave case numbers being brought under control, travel-associated imports (mostly from Europe or other parts of the UK) following the easing of lockdown are responsible for seeding the current epidemic population. This demonstrates that the impact of stringent public health measures can be compromised if following this, movements from regions of high to low prevalence are not minimised.
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- 2021
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18. Evaluating the effects of SARS-CoV-2 Spike mutation D614G on transmissibility and pathogenicity
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Erik Volz, Verity Hill, John T. McCrone, Anna Price, David Jorgensen, Áine O’Toole, Joel Southgate, Robert Johnson, Ben Jackson, Fabricia F. Nascimento, Sara M. Rey, Samuel M. Nicholls, Rachel M. Colquhoun, Ana da Silva Filipe, James Shepherd, David J. Pascall, Rajiv Shah, Natasha Jesudason, Kathy Li, Ruth Jarrett, Nicole Pacchiarini, Matthew Bull, Lily Geidelberg, Igor Siveroni, Ian Goodfellow, Nicholas J. Loman, Oliver G. Pybus, David L. Robertson, Emma C. Thomson, Andrew Rambaut, Thomas R. Connor, Cherian Koshy, Emma Wise, Nick Cortes, Jessica Lynch, Stephen Kidd, Matilde Mori, Derek J. Fairley, Tanya Curran, James P. McKenna, Helen Adams, Christophe Fraser, Tanya Golubchik, David Bonsall, Catrin Moore, Sarah L. Caddy, Fahad A. Khokhar, Michelle Wantoch, Nicola Reynolds, Ben Warne, Joshua Maksimovic, Karla Spellman, Kathryn McCluggage, Michaela John, Robert Beer, Safiah Afifi, Sian Morgan, Angela Marchbank, Christine Kitchen, Huw Gulliver, Ian Merrick, Martyn Guest, Robert Munn, Trudy Workman, William Fuller, Catherine Bresner, Luke B. Snell, Themoula Charalampous, Gaia Nebbia, Rahul Batra, Jonathan Edgeworth, Samuel C. Robson, Angela Beckett, Katie F. Loveson, David M. Aanensen, Anthony P. Underwood, Corin A. Yeats, Khalil Abudahab, Ben E.W. Taylor, Mirko Menegazzo, Gemma Clark, Wendy Smith, Manjinder Khakh, Vicki M. Fleming, Michelle M. Lister, Hannah C. Howson-Wells, Louise Berry, Tim Boswell, Amelia Joseph, Iona Willingham, Paul Bird, Thomas Helmer, Karlie Fallon, Christopher Holmes, Julian Tang, Veena Raviprakash, Sharon Campbell, Nicola Sheriff, Matthew W. Loose, Nadine Holmes, Christopher Moore, Matthew Carlile, Victoria Wright, Fei Sang, Johnny Debebe, Francesc Coll, Adrian W. Signell, Gilberto Betancor, Harry D. Wilson, Theresa Feltwell, Charlotte J. Houldcroft, Sahar Eldirdiri, Anita Kenyon, Thomas Davis, Oliver Pybus, Louis du Plessis, Alex Zarebski, Jayna Raghwani, Moritz Kraemer, Sarah Francois, Stephen Attwood, Tetyana Vasylyeva, M. Estee Torok, William L. Hamilton, Ian G. Goodfellow, Grant Hall, Aminu S. Jahun, Yasmin Chaudhry, Myra Hosmillo, Malte L. Pinckert, Iliana Georgana, Anna Yakovleva, Luke W. Meredith, Samuel Moses, Hannah Lowe, Felicity Ryan, Chloe L. Fisher, Ali R. Awan, John Boyes, Judith Breuer, Kathryn Ann Harris, Julianne Rose Brown, Divya Shah, Laura Atkinson, Jack C.D. Lee, Adela Alcolea-Medina, Nathan Moore, Nicholas Cortes, Rebecca Williams, Michael R. Chapman, Lisa J. Levett, Judith Heaney, Darren L. Smith, Matthew Bashton, Gregory R. Young, John Allan, Joshua Loh, Paul A. Randell, Alison Cox, Pinglawathee Madona, Alison Holmes, Frances Bolt, James Price, Siddharth Mookerjee, Aileen Rowan, Graham P. Taylor, Manon Ragonnet-Cronin, Rob Johnson, Olivia Boyd, Erik M. Volz, Kirstyn Brunker, Katherine L. Smollett, Joshua Quick, Claire McMurray, Joanne Stockton, Sam Nicholls, Will Rowe, Radoslaw Poplawski, Rocio T. Martinez-Nunez, Jenifer Mason, Trevor I. Robinson, Elaine O'Toole, Joanne Watts, Cassie Breen, Angela Cowell, Catherine Ludden, Graciela Sluga, Nicholas W. Machin, Shazaad S.Y. Ahmad, Ryan P. George, Fenella Halstead, Venkat Sivaprakasam, James G. Shepherd, Patawee Asamaphan, Marc O. Niebel, Kathy K. Li, Rajiv N. Shah, Natasha G. Jesudason, Yasmin A. Parr, Lily Tong, Alice Broos, Daniel Mair, Jenna Nichols, Stephen N. Carmichael, Kyriaki Nomikou, Elihu Aranday-Cortes, Natasha Johnson, Igor Starinskij, Richard J. Orton, Joseph Hughes, Sreenu Vattipally, Joshua B. Singer, Antony D. Hale, Louissa R. Macfarlane-Smith, Katherine L. Harper, Yusri Taha, Brendan A.I. Payne, Shirelle Burton-Fanning, Sheila Waugh, Jennifer Collins, Gary Eltringham, Kate E. Templeton, Martin P. McHugh, Rebecca Dewar, Elizabeth Wastenge, Samir Dervisevic, Rachael Stanley, Reenesh Prakash, Claire Stuart, Ngozi Elumogo, Dheeraj K. Sethi, Emma J. Meader, Lindsay J. Coupland, Will Potter, Clive Graham, Edward Barton, Debra Padgett, Garren Scott, Emma Swindells, Jane Greenaway, Andrew Nelson, Wen C. Yew, Paola C. Resende Silva, Monique Andersson, Robert Shaw, Timothy Peto, Anita Justice, David Eyre, Derrick Crooke, Sarah Hoosdally, Tim J. Sloan, Nichola Duckworth, Sarah Walsh, Anoop J. Chauhan, Sharon Glaysher, Kelly Bicknell, Sarah Wyllie, Ethan Butcher, Scott Elliott, Allyson Lloyd, Robert Impey, Nick Levene, Lynn Monaghan, Declan T. Bradley, Elias Allara, Clare Pearson, Peter Muir, Ian B. Vipond, Richard Hopes, Hannah M. Pymont, Stephanie Hutchings, Martin D. Curran, Surendra Parmar, Angie Lackenby, Tamyo Mbisa, Steven Platt, Shahjahan Miah, David Bibby, Carmen Manso, Jonathan Hubb, Meera Chand, Gavin Dabrera, Mary Ramsay, Daniel Bradshaw, Alicia Thornton, Richard Myers, Ulf Schaefer, Natalie Groves, Eileen Gallagher, David Lee, David Williams, Nicholas Ellaby, Ian Harrison, Hassan Hartman, Nikos Manesis, Vineet Patel, Chloe Bishop, Vicki Chalker, Husam Osman, Andrew Bosworth, Esther Robinson, Matthew T.G. Holden, Sharif Shaaban, Alec Birchley, Alexander Adams, Alisha Davies, Amy Gaskin, Amy Plimmer, Bree Gatica-Wilcox, Caoimhe McKerr, Catherine Moore, Chris Williams, David Heyburn, Elen De Lacy, Ember Hilvers, Fatima Downing, Giri Shankar, Hannah Jones, Hibo Asad, Jason Coombes, Joanne Watkins, Johnathan M. Evans, Laia Fina, Laura Gifford, Lauren Gilbert, Lee Graham, Malorie Perry, Mari Morgan, Michelle Cronin, Noel Craine, Rachel Jones, Robin Howe, Sally Corden, Sara Rey, Sara Kumziene-Summerhayes, Sarah Taylor, Simon Cottrell, Sophie Jones, Sue Edwards, Justin O’Grady, Andrew J. Page, John Wain, Mark A. Webber, Alison E. Mather, David J. Baker, Steven Rudder, Muhammad Yasir, Nicholas M. Thomson, Alp Aydin, Ana P. Tedim, Gemma L. Kay, Alexander J. Trotter, Rachel A.J. Gilroy, Nabil-Fareed Alikhan, Leonardo de Oliveira Martins, Thanh Le-Viet, Lizzie Meadows, Anastasia Kolyva, Maria Diaz, Andrew Bell, Ana Victoria Gutierrez, Ian G. Charles, Evelien M. Adriaenssens, Robert A. Kingsley, Anna Casey, David A. Simpson, Zoltan Molnar, Thomas Thompson, Erwan Acheson, Jane A.H. Masoli, Bridget A. Knight, Andrew Hattersley, Sian Ellard, Cressida Auckland, Tabitha W. Mahungu, Dianne Irish-Tavares, Tanzina Haque, Yann Bourgeois, Garry P. Scarlett, David G. Partridge, Mohammad Raza, Cariad Evans, Kate Johnson, Steven Liggett, Paul Baker, Sarah Essex, Ronan A. Lyons, Laura G. Caller, Sergi Castellano, Rachel J. Williams, Mark Kristiansen, Sunando Roy, Charlotte A. Williams, Patricia L. Dyal, Helena J. Tutill, Yasmin N. Panchbhaya, Leysa M. Forrest, Paola Niola, Jacqueline Findlay, Tony T. Brooks, Artemis Gavriil, Lamia Mestek-Boukhibar, Sam Weeks, Sarojini Pandey, Lisa Berry, Katie Jones, Alex Richter, Andrew Beggs, Colin P. Smith, Giselda Bucca, Andrew R. Hesketh, Ewan M. Harrison, Sharon J. Peacock, Sophie Palmer, Carol M. Churcher, Katherine L. Bellis, Sophia T. Girgis, Plamena Naydenova, Beth Blane, Sushmita Sridhar, Chris Ruis, Sally Forrest, Claire Cormie, Harmeet K. Gill, Joana Dias, Ellen E. Higginson, Mailis Maes, Jamie Young, Leanne M. Kermack, Nazreen F. Hadjirin, Dinesh Aggarwal, Luke Griffith, Tracey Swingler, Rose K. Davidson, Thomas Williams, Carlos E. Balcazar, Michael D. Gallagher, Áine O'Toole, Stefan Rooke, Rachel Colquhoun, Jordan Ashworth, J.T. McCrone, Emily Scher, Xiaoyu Yu, Kathleen A. Williamson, Thomas D. Stanton, Stephen L. Michell, Claire M. Bewshea, Ben Temperton, Michelle L. Michelsen, Joanna Warwick-Dugdale, Robin Manley, Audrey Farbos, James W. Harrison, Christine M. Sambles, David J. Studholme, Aaron R. Jeffries, Alistair C. Darby, Julian A. Hiscox, Steve Paterson, Miren Iturriza-Gomara, Kathryn A. Jackson, Anita O. Lucaci, Edith E. Vamos, Margaret Hughes, Lucille Rainbow, Richard Eccles, Charlotte Nelson, Mark Whitehead, Lance Turtle, Sam T. Haldenby, Richard Gregory, Matthew Gemmell, Dominic Kwiatkowski, Thushan I. de Silva, Nikki Smith, Adrienn Angyal, Benjamin B. Lindsey, Danielle C. Groves, Luke R. Green, Dennis Wang, Timothy M. Freeman, Matthew D. Parker, Alexander J. Keeley, Paul J. Parsons, Rachel M. Tucker, Rebecca Brown, Matthew Wyles, Chrystala Constantinidou, Meera Unnikrishnan, Sascha Ott, Jeffrey K.J. Cheng, Hannah E. Bridgewater, Lucy R. Frost, Grace Taylor-Joyce, Richard Stark, Laura Baxter, Mohammad T. Alam, Paul E. Brown, Patrick C. McClure, Joseph G. Chappell, Theocharis Tsoleridis, Jonathan Ball, Dimitris Grammatopoulos, David Buck, John A. Todd, Angie Green, Amy Trebes, George MacIntyre-Cockett, Mariateresa de Cesare, Cordelia Langford, Alex Alderton, Roberto Amato, Sonia Goncalves, David K. Jackson, Ian Johnston, John Sillitoe, Steve Palmer, Mara Lawniczak, Matt Berriman, John Danesh, Rich Livett, Lesley Shirley, Ben Farr, Mike Quail, Scott Thurston, Naomi Park, Emma Betteridge, Danni Weldon, Scott Goodwin, Rachel Nelson, Charlotte Beaver, Laura Letchford, David A. Jackson, Luke Foulser, Liz McMinn, Liam Prestwood, Sally Kay, Leanne Kane, Matthew J. Dorman, Inigo Martincorena, Christoph Puethe, Jon-Paul Keatley, Gerry Tonkin-Hill, Christen Smith, Dorota Jamrozy, Mathew A. Beale, Minal Patel, Cristina Ariani, Michael Spencer-Chapman, Eleanor Drury, Stephanie Lo, Shavanthi Rajatileka, Carol Scott, Keith James, Sarah K. Buddenborg, Duncan J. Berger, Gaurang Patel, Maria V. Garcia-Casado, Thomas Dibling, Samantha McGuigan, Hazel A. Rogers, Adam D. Hunter, Emily Souster, Alexandra S. Neaverson, Medical Research Council (MRC), Pascall, David [0000-0002-7543-0860], and Apollo - University of Cambridge Repository
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Genome ,Genetic analysis ,0302 clinical medicine ,Clade ,11 Medical and Health Sciences ,Genetics ,0303 health sciences ,education.field_of_study ,Phylogenetic tree ,Virulence ,C500 ,COG-UK Consortium ,C700 ,Transmissibility (vibration) ,3. Good health ,founder effect ,Spike Glycoprotein, Coronavirus ,epidemiology ,Viral load ,Population ,Glycine ,B100 ,Genomics ,Genome, Viral ,Biology ,Article ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,evolution ,Humans ,education ,030304 developmental biology ,Aspartic Acid ,Whole Genome Sequencing ,Biochemistry, Genetics and Molecular Biology(all) ,SARS-CoV-2 ,COVID-19 ,spike ,The COVID-19 Genomics UK Consortium ,A300 ,06 Biological Sciences ,United Kingdom ,Amino Acid Substitution ,Evolutionary biology ,Mutation ,Biological dispersal ,030217 neurology & neurosurgery ,Founder effect ,Developmental Biology - Abstract
Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant., Graphical Abstract, Highlights • Increasing frequency of SARS-CoV-2 D614G is consistent with a selective advantage • Phylodynamic analyses do not show significantly different growth of D614G clusters • There is no association of D614G replacement with greater severity of infection • The D614G replacement is associated with higher viral loads and younger patient age, Analysis of the spread and frequency of SARS-CoV-2 D614G in the United Kingdom suggests a selective advantage for this strain that is associated with higher viral loads in younger patients but not higher COVID-19 clinical severity or mortality.
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- 2021
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19. Transmission of SARS-CoV-2 Lineage B.1.1.7 in England: Insights from linking epidemiological and genetic data
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Susan Hopkins, Olivia Boyd, Ben Jackson, Katy A. M. Gaythorpe, Ian Harrison, Swapnil Mishra, Gavin Dabrera, Nicholas J. Loman, Andrew Rambaut, Samir Bhatt, Manon Ragonnet-Cronin, Daniel J Laydon, Jeffrey C. Barrett, Roberto Amato, Cog-Uk, Erik M. Volz, Seth Flaxman, Axel Gandy, David Jorgensen, John Sillitoe, John T. McCrone, David K. Jackson, Cristina V. Ariani, Richard M. Myers, Meera Chand, Natalie Groves, Robert Johnson, Wes Hinsley, Verity Hill, Dominic P. Kwiatkowski, Oliver Ratman, Neil M. Ferguson, Áine O'Toole, Lily Geidelberg, and Sónia Gonçalves
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Genetic diversity ,medicine.medical_specialty ,Generation time ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Epidemiology ,Genetic variants ,medicine ,Age composition ,Genetic data ,Biology ,Demography ,Late summer - Abstract
The SARS-CoV-2 lineage B.1.1.7, now designated Variant of Concern 202012/01 (VOC) by Public Health England, originated in the UK in late Summer to early Autumn 2020. We examine epidemiological evidence for this VOC having a transmission advantage from several perspectives. First, whole genome sequence data collected from community-based diagnostic testing provides an indication of changing prevalence of different genetic variants through time. Phylodynamic modelling additionally indicates that genetic diversity of this lineage has changed in a manner consistent with exponential growth. Second, we find that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S-gene target failures (SGTF) in community-based diagnostic PCR testing. Third, we examine growth trends in SGTF and non-SGTF case numbers at local area level across England, and show that the VOC has higher transmissibility than non-VOC lineages, even if the VOC has a different latent period or generation time. Available SGTF data indicate a shift in the age composition of reported cases, with a larger share of under 20 year olds among reported VOC than non-VOC cases. Fourth, we assess the association of VOC frequency with independent estimates of the overall SARS-CoV-2 reproduction number through time. Finally, we fit a semi-mechanistic model directly to local VOC and non-VOC case incidence to estimate the reproduction numbers over time for each. There is a consensus among all analyses that the VOC has a substantial transmission advantage, with the estimated difference in reproduction numbers between VOC and non-VOC ranging between 0.4 and 0.7, and the ratio of reproduction numbers varying between 1.4 and 1.8. We note that these estimates of transmission advantage apply to a period where high levels of social distancing were in place in England; extrapolation to other transmission contexts therefore requires caution.
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- 2021
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20. Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence
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Rachel M. Colquhoun, Louis du Plessis, Alessandro Vespignani, Christopher Ruis, Sumali Bajaj, Guy Baele, Verity Hill, Nuno R. Faria, Moritz U. G. Kraemer, Anya Lindström Battle, Áine O'Toole, Ben Jackson, Andrew Rambaut, David M. Aanensen, Erik M. Volz, Samuel V. Scarpino, Simon Cauchemez, Kris V Parag, Oliver G. Pybus, Nicholas J. Loman, John T. McCrone, Simon Dellicour, Bernardo Gutierrez, Brennan Klein, Harvard Medical School [Boston] (HMS), Boston Children's Hospital, University of Oxford, University of Edinburgh, University of Cambridge [UK] (CAM), Université libre de Bruxelles (ULB), Department of Microbiology, Immunology and Transplantation [Leuven], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Rega Institute for Medical Research [Leuven, België], Northeastern University [Boston], Biotempo, Centre for Genomic Pathogen Surveillance, The Wellcome Trust Sanger Institute [Cambridge], Nuffield Department of Medicine [Oxford, UK] (Big Data Institute), University of Birmingham [Birmingham], Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), V.H. was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) (grant BB/M010996/1). A.R. acknowledges the support of the Wellcome Trust (Collaborators Award 206298/Z/17/Z–ARTIC network) and the European Research Council (grant agreement 725422–ReservoirDOCS). M.U.G.K. acknowledges support from the Branco Weiss Fellowship. M.U.G.K. and S.D. acknowledge support from the European Union’s Horizon 2020 project MOOD (grant agreement 874850). O.G.P. and M.U.G.K. acknowledge support from the Oxford Martin School. A.L.B., S.V.S., and M.U.G.K. acknowledge support from the Rockefeller Foundation and Google.org. C.R. was supported by a Fondation Botnar Research Award (Programme grant 6063) and UK Cystic Fibrosis Trust (Innovation Hub Award 001). A.L.B. acknowledges support from the Biotechnologyand Biological Sciences Research Council (BBSRC) [grant BB/M011224/1]. S.D. acknowledges support from the Fonds National de la Recherche Scientifique (FNRS, Belgium). G.B. acknowledges support from the Research Foundation–Flanders (Fonds voor Wetenschappelijk Onderzoek–Vlaanderen, G0E1420N and G098321N) and from the Interne Fondsen KU Leuven/Internal Funds KU Leuven under grant agreement C14/18/094. COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research and Innovation (UKRI), the National Institute of Health Research (NIHR), and Genome Research Limited, operating as the Wellcome Sanger Institute. A.O. is supported by the Wellcome Trust Hosts, Pathogens and Global Health Programme (grant grant.203783/Z/16/Z) and Fast Grants (award 2236). S.B. is supported by the Clarendon Scholarship, University of Oxford and NERC DTP (grant NE/S007474/1). N.R.F. acknowledges support from Wellcome Trust and Royal Society (Sir Henry Dale Fellowship: 204311/Z/16/Z) and Medical Research Council–São Paulo Research Foundation CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0)., European Project: 725422,ERC-2016-COG,ReservoirDOCS(2017), European Project: 874850,H2020-SC1-2019-Single-Stage-RTD,MOOD(2020), Kraemer, Moritz UG [0000-0001-8838-7147], Hill, Verity [0000-0002-3509-8146], Ruis, Christopher [0000-0003-0977-5534], Dellicour, Simon [0000-0001-9558-1052], Bajaj, Sumali [0000-0002-8313-819X], McCrone, John T [0000-0002-9846-8917], Baele, Guy [0000-0002-1915-7732], Parag, Kris V [0000-0002-7806-3605], Battle, Anya Lindström [0000-0001-6356-4688], Gutierrez, Bernardo [0000-0002-9220-2739], Jackson, Ben [0000-0002-9981-0649], Colquhoun, Rachel [0000-0002-5577-9897], O'Toole, Áine [0000-0001-8083-474X], Klein, Brennan [0000-0001-8326-5044], Vespignani, Alessandro [0000-0003-3419-4205], Volz, Erik [0000-0001-6268-8937], Faria, Nuno R [0000-0002-9747-8822], Aanensen, David M [0000-0001-6688-0854], Loman, Nicholas J [0000-0002-9843-8988], du Plessis, Louis [0000-0003-0352-6289], Cauchemez, Simon [0000-0001-9186-4549], Rambaut, Andrew [0000-0003-4337-3707], Scarpino, Samuel V [0000-0001-5716-2770], Pybus, Oliver G [0000-0002-8797-2667], Apollo - University of Cambridge Repository, Medical Research Council-São Paulo Research Foundation (FAPESP), Wellcome Trust, and Medical Research Council (MRC)
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0301 basic medicine ,2019-20 coronavirus outbreak ,Lineage (genetic) ,Coronavirus disease 2019 (COVID-19) ,General Science & Technology ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,EPIDEMICS ,VARIANT ,B100 ,Context (language use) ,B200 ,METAPOPULATION DYNAMICS ,Genome, Viral ,Biology ,03 medical and health sciences ,0302 clinical medicine ,Spatio-Temporal Analysis ,COVID-19 Genomics UK (COG-UK) Consortium ,Invasion process ,[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,Pandemic ,Humans ,RISK ,Travel ,[SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases ,Science & Technology ,Multidisciplinary ,SARS-CoV-2 ,Incidence ,COVID-19 ,C500 ,A300 ,C700 ,United Kingdom ,3. Good health ,Multidisciplinary Sciences ,Phylogeography ,030104 developmental biology ,Evolutionary biology ,COVID-19 Nucleic Acid Testing ,Communicable Disease Control ,[SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology ,Science & Technology - Other Topics ,VIRUS ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,SPREAD ,030217 neurology & neurosurgery - Abstract
Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7's increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates. ispartof: SCIENCE vol:373 issue:6557 pages:889-+ ispartof: location:United States status: published
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- 2021
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21. Resurgence of Ebola Virus in 2021 in Guinea Suggests a New Paradigm for Outbreaks
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Fodé B. Sako, Jacob Camara, Amadou A. Sall, Ariane Düx, Giuditta Annibaldis, Meike Pahlmann, Abdoulaye Toure, Ousmane Faye, Mamadou Condé, Haby Diallo, Steven T. Pullan, Amadou Sidibe, Moriba Povogui, Fara Raymond Koundouno, Christophe Peyrefitte, Liana E. Kafetzopoulou, Alpha Kabinet Keita, Saa L. Millimono, Mandiou Diakite, Ahmadou Doré, Karla Pietro, N'. Faly Magassouba, Fabian H. Leendertz, Nicole Vidal, Stephan Günther, Anke Thielebein, Julia Hinzmann, Dembo Diakite, Ahidjo Ayouba, Cheikh Loucoubar, Kaka Kourouma, Martin Faye, Philippe Lemey, Moussa Moïse Diagne, Mamadou D. Barry, Fodé Y. Soumah, Annick Renevey, Georges Ki-Zerbo, Kékoura Ifono, Andrew Rambaut, Abdoul K. Soumah, Miles W. Carroll, Mamadou S. Bah, Joseph Akoi Bore, Ibrahima Camara, Mamadou S. Sow, Sébastien Calvignac-Spencer, Fodé A. Traore, Sophie Duraffour, Assaïtou Bah, John T. McCrone, Nathalie J. Vielle, Joshua Quick, Noël Tordo, Moussa Baldé, Mamadou Diop, Youssouf Sidibé, Martine Peeters, Madeleine Kourouma, Sakoba Keita, Mamadou B. Keita, Frédéric Le Marcis, Cé D. Saouromou, Anais Legand, Amadou Diallo, Pierre Formenty, Almudena Mari-Saez, Michael R. Wiley, Karifa Kourouma, Eric Delaporte, Barré Soropogui, Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques et émergentes (TransVIHMI), Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Université Gamal Abdel Nasser de Conakry, Laboratoire des Fièvres Hémorragiques en Guinée, Bernhard Nocht Institute for Tropical Medicine - Bernhard-Nocht-Institut für Tropenmedizin [Hamburg, Germany] (BNITM), Institut Pasteur de Dakar, Réseau International des Instituts Pasteur (RIIP), Robert Koch Institute [Berlin] (RKI), German Center for Infection Research - Partner Site Hamburg-Lübeck-Borstel-Riems, German Centre for Infection Research (DZIF), Centre de Recherche et de Formation en Infectiologie de Guinée [Conakry, Guinée] (CERFIG), Triangle : action, discours, pensée politique et économique (TRIANGLE), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Hôpital National Donka, The Wellcome Trust Centre for Human Genetics [Oxford], University of Oxford, Epidemiology of Highly Pathogenic Microorganisms, Vecteurs - Infections tropicales et méditerranéennes (VITROME), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge] (IRBA), CEA Tech Grand-Est (DGDE), CEA Tech en régions (CEA-TECH-Reg), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut National de Santé Publique [Conakry, Guinée] (INSP), Ministère de la Santé [Conakry, Guinea], Laboratoire de Physique de l'Atmosphère et de l'Océan Siméon Fongang (LPAO-SF), École Supérieure Polytechnique de Dakar (ESP), Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD), Public Health England, University of Birmingham [Birmingham], Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), World Health Organization [Geneva], The University of Texas Medical Branch (UTMB), University of Nebraska Medical Center, University of Nebraska System, Institut Pasteur de Guinée, Unité des Virus Emergents (UVE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche Biomédicale des Armées [Antenne Marseille] (IRBA), University of Edinburgh, Division Prévention et Lutte contre la Maladie, Ministère de la Santé et de l’Hygiène Publique, Ecole Nationale Superieure de Lyon, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Infectious and Tropical Diseases Department [Montpellier], Institut de Recherche pour le Développement (IRD)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD), Max Planck Institute for Evolutionary Anthropology [Leipzig], Max-Planck-Gesellschaft, ANR-16-IDEX-0006,MUSE,MUSE(2016), Recherches Translationnelles sur le VIH et les maladies infectieuses endémiques er émergentes (TransVIHMI), Université Cheikh Anta Diop [Dakar, Sénégal] (UCAD)-Institut de Recherche pour le Développement (IRD)-Université de Yaoundé I-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Université Montpellier 1 (UM1), Centre National de la Recherche Scientifique (CNRS)-Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Université Lumière - Lyon 2 (UL2)-École normale supérieure - Lyon (ENS Lyon), University of Oxford [Oxford], Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes (URMITE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR48, INSB-INSB-Centre National de la Recherche Scientifique (CNRS), CEA Tech Alsace Champagne-Ardenne Lorraine, École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Sciences Po Lyon - Institut d'études politiques de Lyon (IEP Lyon), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Institut de Recherche pour le Développement (IRD)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Michel de l'Hospital : laboratoire de recherche en sciences juridiques et politiques (CMH ), and Université Clermont Auvergne (UCA)
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Male ,Zaire ebolavirus ,Time Factors ,viruses ,Disease ,Biology ,medicine.disease_cause ,Disease cluster ,Models, Biological ,Viral Zoonoses ,Virus ,Disease Outbreaks ,03 medical and health sciences ,Ebola virus ,0302 clinical medicine ,medicine ,Animals ,Humans ,Survivors ,030212 general & internal medicine ,Clade ,Phylogeny ,030304 developmental biology ,0303 health sciences ,Multidisciplinary ,Transmission (medicine) ,Guinea-2021 ,High-Throughput Nucleotide Sequencing ,Outbreak ,Hemorrhagic Fever, Ebola ,[SHS.ANTHRO-SE]Humanities and Social Sciences/Social Anthropology and ethnology ,Ebolavirus ,Virology ,3. Good health ,Democratic Republic of the Congo ,Female ,Persistent Infection ,epidemiology ,Guinea ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie - Abstract
Seven years after the declaration of the first epidemic of Ebola virus disease in Guinea, the country faced a new outbreak—between 14 February and 19 June 2021—near the epicentre of the previous epidemic1,2. Here we use next-generation sequencing to generate complete or near-complete genomes of Zaire ebolavirus from samples obtained from 12 different patients. These genomes form a well-supported phylogenetic cluster with genomes from the previous outbreak, which indicates that the new outbreak was not the result of a new spillover event from an animal reservoir. The 2021 lineage shows considerably lower divergence than would be expected during sustained human-to-human transmission, which suggests a persistent infection with reduced replication or a period of latency. The resurgence of Zaire ebolavirus from humans five years after the end of the previous outbreak of Ebola virus disease reinforces the need for long-term medical and social care for patients who survive the disease, to reduce the risk of re-emergence and to prevent further stigmatization. The viral lineage responsible for the February 2021 outbreak of Ebola virus disease in Guinea is nested within a clade that predominantly consists of genomes sampled during the 2013–2016 epidemic, suggesting that the virus might have re-emerged after a long period of latency within a previously infected individual.
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- 2021
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22. Establishment & lineage dynamics of the SARS-CoV-2 epidemic in the UK
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Jordan Ashworth, Nuno R. Faria, Samuel M. Nicholls, Alexander E. Zarebski, Thomas R. Connor, Benjamin C. Jackson, Tetyana I. Vasylyeva, Áine O'Toole, Bernardo Gutierrez, Oliver G. Pybus, John T. McCrone, Verity Hill, Emily Scher, Erik M. Volz, Jayna Raghwani, Alexander Watts, Isaac I. Bogoch, Christopher Ruis, Nicholas J. Loman, Rachel M. Colquhoun, Andrew Rambaut, Moritz U. G. Kraemer, Louis du Plessis, David M. Aanensen, Kris V Parag, and Kamran Khan
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2019-20 coronavirus outbreak ,Transmission (mechanics) ,Phylogenetic tree ,Coronavirus disease 2019 (COVID-19) ,law ,Evolutionary biology ,Lineage (evolution) ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Biology ,Genome ,Virus ,law.invention - Abstract
The UK’s COVID-19 epidemic during early 2020 was one of world’s largest and unusually well represented by virus genomic sampling. Here we reveal the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 SARS-CoV-2 genomes, including 26,181 from the UK sampled throughout the country’s first wave of infection. Using large-scale phylogenetic analyses, combined with epidemiological and travel data, we quantify the size, spatio-temporal origins and persistence of genetically-distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown were larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, whilst lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.
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- 2020
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23. The effective population size and mutation rate of influenza A virus in acutely infected individuals
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Emily T. Martin, Arnold S. Monto, Robert J. Woods, Adam S. Lauring, and John T. McCrone
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Mutation rate ,Data sequences ,Effective population size ,Population size ,Influenza A virus ,medicine ,Community setting ,Biology ,medicine.disease_cause ,Evolutionary dynamics ,Virology ,In vivo mutation - Abstract
The global evolutionary dynamics of influenza viruses ultimately derive from processes that take place within and between infected individuals. Recent work suggests that within-host populations are dynamic, but anin vivoestimate of mutation rate and population size in naturally infected individuals remains elusive. Here we model the within-host dynamics of influenza A viruses using high depth of coverage sequence data from 200 acute infections in an outpatient, community setting. Using a Wright-Fisher model, we estimate a within-host effective population size of 32-72 and anin vivomutation rate of 3.4×10−6per nucleotide per generation.
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- 2020
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24. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2
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Andrew Rambaut, Samuel L. Hong, Guy Baele, Martha I. Nelson, Michael Worobey, Áine O'Toole, Philippe Lemey, Vittoria Colizza, John T. McCrone, Verity Hill, Kristian G. Andersen, Chiara Poletto, and Marc A. Suchard
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0106 biological sciences ,0301 basic medicine ,General Physics and Astronomy ,Inference ,01 natural sciences ,Bayes' theorem ,Viral ,lcsh:Science ,Lung ,Phylogeny ,Sampling bias ,Travel ,Genome ,Multidisciplinary ,Phylogenomics ,Sampling (statistics) ,Phylogenetics ,Phylogeography ,Infectious Diseases ,Geography ,Trait ,Infection ,Coronavirus Infections ,Science ,Pneumonia, Viral ,Bayesian probability ,Context (language use) ,Genome, Viral ,010603 evolutionary biology ,Article ,General Biochemistry, Genetics and Molecular Biology ,Vaccine Related ,Betacoronavirus ,03 medical and health sciences ,Biodefense ,Humans ,Pandemics ,Ecological epidemiology ,SARS-CoV-2 ,Prevention ,COVID-19 ,Bayes Theorem ,Pneumonia ,General Chemistry ,Emerging Infectious Diseases ,030104 developmental biology ,Evolutionary biology ,lcsh:Q - Abstract
Spatiotemporal bias in genome sampling can severely confound discrete trait phylogeographic inference. This has impeded our ability to accurately track the spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, despite the availability of unprecedented numbers of SARS-CoV-2 genomes. Here, we present an approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2. We demonstrate that including travel history data yields i) more realistic hypotheses of virus spread and ii) higher posterior predictive accuracy compared to including only sampling location. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts., Spatiotemporal sampling gaps in existing pathogen genomic data limits their use in understanding epidemiological patterns. Here, the authors apply a phylogeographic approach with SARS-CoV-2 genomes to accurately reproduce pathogen spread by accounting for spatial biases and travel history of the individual.
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- 2020
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25. Accommodating individual travel history, global mobility, and unsampled diversity in phylogeography: a SARS-CoV-2 case study
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Martha I. Nelson, Michael Worobey, Áine O'Toole, John T. McCrone, Chiara Poletto, Samuel L. Hong, Vittoria Colizza, Marc A. Suchard, Verity Hill, Kristian G. Andersen, Guy Baele, Philippe Lemey, and Andrew Rambaut
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Ancestral reconstruction ,Geography ,Contextual design ,Evolutionary biology ,Bayesian probability ,Sampling (statistics) ,Inference ,Context (language use) ,computer.software_genre ,computer ,Data integration ,Sampling bias - Abstract
Spatiotemporal bias in genome sequence sampling can severely confound phylogeographic inference based on discrete trait ancestral reconstruction. This has impeded our ability to accurately track the emergence and spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic. Despite the availability of unprecedented numbers of SARS-CoV-2 genomes on a global scale, evolutionary reconstructions are hindered by the slow accumulation of sequence divergence over its relatively short transmission history. When confronted with these issues, incorporating additional contextual data may critically inform phylodynamic reconstructions. Here, we present a new approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2, while also including global air transportation data. We demonstrate that including travel history data for each SARS-CoV-2 genome yields more realistic reconstructions of virus spread, particularly when travelers from undersampled locations are included to mitigate sampling bias. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations in the analyses. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts. Although further research is needed to fully examine the performance of our travel-aware phylogeographic analyses with unsampled diversity and to further improve them, they represent multiple new avenues for directly addressing the colossal issue of sample bias in phylogeographic inference.
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- 2020
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26. A dynamic nomenclature proposal for SARS-CoV-2 to assist genomic epidemiology
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Verity Hill, John T. McCrone, Andrew Rambaut, Áine O'Toole, Christopher Ruis, Louis du Plessis, Oliver G. Pybus, and Edward C. Holmes
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0303 health sciences ,Phylogenetic tree ,030306 microbiology ,Lineage (evolution) ,viruses ,COVID-19 ,Biology ,medicine.disease_cause ,Genome ,Coronavirus ,03 medical and health sciences ,Phylogenetic diversity ,Evolutionary biology ,Pandemic ,medicine ,Nomenclature ,Virus classification ,030304 developmental biology - Abstract
The ongoing pandemic spread of a novel human coronavirus, SARS-COV-2, associated with severe pneumonia disease (COVID-19), has resulted in the generation of thousands of virus genome sequences The rate of genome generation is unprecedented, yet there is currently no coherent nor accepted scheme for naming the expanding phylogenetic diversity of SARS-CoV-2 We present a rational and dynamic virus nomenclature that uses a phylogenetic framework to identify those lineages that contribute most to active spread Our system is made tractable by constraining the number and depth of hierarchical lineage labels and by flagging and declassifying virus lineages that become unobserved and hence are likely inactive By focusing on active virus lineages and those spreading to new locations this nomenclature will assist in tracking and understanding the patterns and determinants of the global spread of SARS-CoV-2 ### Competing Interest Statement The authors have declared no competing interest
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- 2020
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27. Genetic bottlenecks in intraspecies virus transmission
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Adam S. Lauring and John T. McCrone
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0301 basic medicine ,Swine ,Virus transmission ,Biology ,Article ,Bottleneck ,law.invention ,Evolution, Molecular ,03 medical and health sciences ,law ,Zoonoses ,Virology ,Animals ,Humans ,Genetics ,Genetic diversity ,Host Microbial Interactions ,Host (biology) ,Genetic Variation ,Plants ,030104 developmental biology ,Transmission (mechanics) ,Virus Diseases ,Viral evolution ,Mutation ,Viruses - Abstract
Ultimately, viral evolution is a consequence of mutations that arise within and spread between infected hosts. The transmission bottleneck determines how much of the viral diversity generated in one host passes to another during transmission. It therefore plays a vital role in linking within-host processes to larger evolutionary trends. Although many studies suggest that transmission severely restricts the amount of genetic diversity that passes between individuals, there are important exceptions to this rule. In many cases, the factors that determine the size of the transmission bottleneck are only beginning to be understood. Here, we review how transmission bottlenecks are measured, how they arise, and their consequences for viral evolution.
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- 2018
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28. Influenza B viruses exhibit lower within-host diversity than influenza A viruses in human hosts
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Arnold S. Monto, William J. Fitzsimmons, Emily T. Martin, John T. McCrone, Joshua G. Petrie, Adam S. Lauring, and Andrew L Valesano
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Influenza vaccine ,viruses ,Immunology ,Reassortment ,Genome, Viral ,Biology ,medicine.disease_cause ,Microbiology ,Antigenic drift ,Virus ,Herd immunity ,Evolution, Molecular ,03 medical and health sciences ,Virology ,Influenza, Human ,Influenza A virus ,medicine ,Humans ,Prospective Studies ,030304 developmental biology ,0303 health sciences ,030306 microbiology ,Transmission (medicine) ,Host (biology) ,Genetic Variation ,virus diseases ,Viral Load ,3. Good health ,Influenza B virus ,Genetic Diversity and Evolution ,Influenza Vaccines ,Insect Science ,Host-Pathogen Interactions - Abstract
Influenza B virus undergoes seasonal antigenic drift more slowly than influenza A, but the reasons for this difference are unclear. While the evolutionary dynamics of influenza viruses play out globally, they are fundamentally driven by mutation, reassortment, drift, and selection within individual hosts. These processes have recently been described for influenza A virus, but little is known about the evolutionary dynamics of influenza B virus (IBV) at the level of individual infections and transmission events. Here we define the within-host evolutionary dynamics of influenza B virus by sequencing virus populations from naturally-infected individuals enrolled in a prospective, community-based cohort over 8176 person-seasons of observation. Through analysis of high depth-of-coverage sequencing data from samples from 91 individuals with influenza B, we find that influenza B virus accumulates lower genetic diversity than previously observed for influenza A virus during acute infections. Consistent with studies of influenza A viruses, the within-host evolution of influenza B viruses is characterized by purifying selection and the general absence of widespread positive selection of within-host variants. Analysis of shared genetic diversity across 15 sequence-validated transmission pairs suggests that IBV experiences a tight transmission bottleneck similar to that of influenza A virus. These patterns of local-scale evolution are consistent with influenza B virus’ slower global evolutionary rate.ImportanceThe evolution of influenza virus is a significant public health problem and necessitates the annual evaluation of influenza vaccine formulation to keep pace with viral escape from herd immunity. Influenza B virus is a serious health concern for children, in particular, yet remains understudied compared to influenza A virus. Influenza B virus evolves more slowly than influenza A, but the factors underlying this are not completely understood. We studied how the within-host diversity of influenza B virus relates to its global evolution by sequencing viruses from a community-based cohort. We found that influenza B virus populations have lower within-host genetic diversity than influenza A virus and experience a tight genetic bottleneck during transmission. Our work provides insights into the varying dynamics of influenza viruses in human infection.
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- 2019
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29. A speed-fidelity trade-off determines the mutation rate and virulence of an RNA virus
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Andrew Woodman, Madhumita Yennawar, Craig E. Cameron, Jamie J. Arnold, Adam S. Lauring, Richard Evans, William J. Fitzsimmons, John T. McCrone, and Robert J. Woods
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0301 basic medicine ,Male ,RNA viruses ,Mutation rate ,Virus Replication ,Pathology and Laboratory Medicine ,Enteroviruses ,Mice ,Mutation Rate ,Medicine and Health Sciences ,Natural Selection ,Biology (General) ,Genetics ,0303 health sciences ,Experimental evolution ,Natural selection ,Virulence ,biology ,General Neuroscience ,Microbial Mutation ,030302 biochemistry & molecular biology ,3T3 Cells ,Poliovirus ,Deletion Mutation ,Medical Microbiology ,Viral Pathogens ,Viruses ,Female ,Pathogens ,Kinetic proofreading ,General Agricultural and Biological Sciences ,Evolutionary Processes ,QH301-705.5 ,education ,Mice, Transgenic ,Polymorphism, Single Nucleotide ,Microbiology ,General Biochemistry, Genetics and Molecular Biology ,Viral Proteins ,03 medical and health sciences ,dsRNA viruses ,Genetic drift ,Theilovirus ,Virology ,Animals ,Microbial Pathogens ,030304 developmental biology ,Evolutionary Biology ,General Immunology and Microbiology ,Host Microbial Interactions ,Models, Genetic ,Biology and life sciences ,Population Biology ,Point mutation ,Organisms ,RNA ,RNA virus ,RNA-Dependent RNA Polymerase ,biology.organism_classification ,Primer ,Viral Replication ,Kinetics ,Genetic hitchhiking ,030104 developmental biology ,Viral replication ,Amino Acid Substitution ,Mutation ,Mutagenesis, Site-Directed ,Directed Molecular Evolution ,Population Genetics - Abstract
Mutation rates can evolve through genetic drift, indirect selection due to genetic hitchhiking, or direct selection on the physicochemical cost of high fidelity. However, for many systems, it has been difficult to disentangle the relative impact of these forces empirically. In RNA viruses, an observed correlation between mutation rate and virulence has led many to argue that their extremely high mutation rates are advantageous, because they may allow for increased adaptability. This argument has profound implications, as it suggests that pathogenesis in many viral infections depends on rare orde novomutations. Here we present data for an alternative model whereby RNA viruses evolve high mutation rates as a byproduct of selection for increased replicative speed. We find that a poliovirus antimutator, 3DG64S, has a significant replication defect and that wild type and 3DG64Spopulations have similar adaptability in two distinct cellular environments. Experimental evolution of 3DG64Sunder r-selection led to reversion and compensation of the fidelity phenotype. Mice infected with 3DG64Sexhibited delayed morbidity at doses well above the LD50, consistent with attenuation by slower growth as opposed to reduced mutational supply. Furthermore, compensation of the 3DG64Sgrowth defect restored virulence, while compensation of the fidelity phenotype did not. Our data are consistent with the kinetic proofreading model for biosynthetic reactions and suggest that speed is more important than accuracy. In contrast to what has been suggested for many RNA viruses, we find that within host spread is associated with viral replicative speed and not standing genetic diversity.Author SummaryMutation rate evolution has long been a fundamental problem in evolutionary biology. The polymerases of RNA viruses generally lack proofreading activity and exhibit extremely high mutation rates. Since most mutations are deleterious and mutation rates are tuned by natural selection, we asked why hasn’t the virus evolved to have a lower mutation rate? We used experimental evolution and a murine infection model to show that RNA virus mutation rates may actually be too high and are not necessarily adaptive. Rather, our data indicate that viral mutation rates are driven higher as a result of selection for viruses with faster replication kinetics. We suggest that viruses have high mutation rates, not because they facilitate adaption, but because it is hard to be both fast and accurate.
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- 2018
30. Author response: Stochastic processes constrain the within and between host evolution of influenza virus
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Ryan E. Malosh, Arnold S. Monto, Adam S. Lauring, John T. McCrone, Emily T. Martin, and Robert J. Woods
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0301 basic medicine ,03 medical and health sciences ,030104 developmental biology ,Evolutionary biology ,Stochastic process ,Host (biology) ,Biology ,Virus - Published
- 2018
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31. Stochastic processes constrain the within and between host evolution of influenza virus
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Adam S. Lauring, Ryan E. Malosh, Emily T. Martin, Arnold S. Monto, Robert J. Woods, and John T. McCrone
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0301 basic medicine ,Nonsynonymous substitution ,bottleneck ,QH301-705.5 ,Science ,030106 microbiology ,Genomics ,Biology ,Genome ,Polymorphism, Single Nucleotide ,General Biochemistry, Genetics and Molecular Biology ,Virus ,influenza virus ,diversity ,Evolution, Molecular ,03 medical and health sciences ,Influenza, Human ,evolution ,Humans ,Point Mutation ,Selection, Genetic ,Biology (General) ,Evolutionary dynamics ,Stochastic Processes ,Microbiology and Infectious Disease ,General Immunology and Microbiology ,Stochastic process ,General Neuroscience ,transmission ,General Medicine ,Sequence Analysis, DNA ,Orthomyxoviridae ,030104 developmental biology ,Genomics and Evolutionary Biology ,Evolutionary biology ,Infectious disease (medical specialty) ,Cohort ,Medicine ,Research Article - Abstract
The evolutionary dynamics of influenza virus ultimately derive from processes that take place within and between infected individuals. Here we define influenza virus dynamics in human hosts through sequencing of 249 specimens from 200 individuals collected over 6290 person-seasons of observation. Because these viruses were collected from individuals in a prospective community-based cohort, they are broadly representative of natural infections with seasonal viruses. Consistent with a neutral model of evolution, sequence data from 49 serially sampled individuals illustrated the dynamic turnover of synonymous and nonsynonymous single nucleotide variants and provided little evidence for positive selection of antigenic variants. We also identified 43 genetically-validated transmission pairs in this cohort. Maximum likelihood optimization of multiple transmission models estimated an effective transmission bottleneck of 1–2 genomes. Our data suggest that positive selection is inefficient at the level of the individual host and that stochastic processes dominate the host-level evolution of influenza viruses.
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- 2018
32. Stochastic processes dominate the within and between host evolution of influenza virus
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John T. McCrone, Emily T. Martin, Robert J. Woods, Adam S. Lauring, Ryan E. Malosh, and Arnold S. Monto
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0303 health sciences ,030306 microbiology ,Host (biology) ,Transmission (medicine) ,Biology ,medicine.disease_cause ,Genome ,Virus ,03 medical and health sciences ,Genetic drift ,Effective population size ,Evolutionary biology ,Influenza A virus ,medicine ,Evolutionary dynamics ,030304 developmental biology - Abstract
The global evolutionary dynamics of influenza virus ultimately derive from processes that take place within and between infected individuals. Here we define the dynamics of influenza A virus populations in human hosts through next generation sequencing of 249 specimens from 200 individuals collected over 6290 person-seasons of observation. Because these viruses were collected over 5 seasons from individuals in a prospective community-based cohort, they are broadly representative of natural human infections with seasonal viruses. We used viral sequence data from 35 serially sampled individuals to estimate a within host effective population size of 30-70 and an in vivo mutation rate of 4x10−5per nucleotide per cellular infectious cycle. These estimates are consistent across several models and robust to the models' underlying assumptions. We also identified 43 epidemiologically linked and genetically validated transmission pairs. Maximum likelihood optimization of multiple transmission models estimates an effective transmission bottleneck of 1-2 distinct genomes. Our data suggest that positive selection of novel viral variants is inefficient at the level of the individual host and that genetic drift and other stochastic processes dominate the within and between host evolution of influenza A viruses.
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- 2017
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33. Vaccination has minimal impact on the intrahost diversity of H3N2 influenza viruses
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Emily K. Mantlo, Kari Debbink, Adam S. Lauring, Emileigh Johnson, Arnold S. Monto, Joshua G. Petrie, John T. McCrone, and Rachel Truscon
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0301 basic medicine ,RNA viruses ,Viral Diseases ,Influenza Viruses ,Physiology ,Pathology and Laboratory Medicine ,medicine.disease_cause ,Biochemistry ,Immune Physiology ,Medicine and Health Sciences ,Influenza A virus ,Public and Occupational Health ,lcsh:QH301-705.5 ,Phylogeny ,Genetics ,Vaccines ,0303 health sciences ,Immune System Proteins ,biology ,Vaccination ,Antibody titer ,High-Throughput Nucleotide Sequencing ,Vaccination and Immunization ,Antigenic Variation ,3. Good health ,Infectious Diseases ,Hemagglutinins ,Medical Microbiology ,Influenza Vaccines ,Viral evolution ,Viral Pathogens ,Viruses ,Seasons ,Pathogens ,Research Article ,lcsh:Immunologic diseases. Allergy ,Evolutionary Immunology ,Immunology ,Neuraminidase ,Hemagglutinin (influenza) ,Genome, Viral ,Microbiology ,Viral Evolution ,Antigenic drift ,Viral Proteins ,03 medical and health sciences ,Virology ,Influenza, Human ,Antigenic variation ,medicine ,Humans ,Antigens ,Molecular Biology ,Microbial Pathogens ,030304 developmental biology ,Evolutionary Biology ,Hemagglutination assay ,030306 microbiology ,Influenza A Virus, H3N2 Subtype ,Organisms ,Immunity ,Biology and Life Sciences ,Proteins ,Genetic Variation ,Sequence Analysis, DNA ,Hemagglutination Inhibition Tests ,Influenza ,Organismal Evolution ,030104 developmental biology ,Viral phylodynamics ,lcsh:Biology (General) ,Microbial Evolution ,biology.protein ,Parasitology ,Preventive Medicine ,lcsh:RC581-607 ,Orthomyxoviruses - Abstract
While influenza virus diversity and antigenic drift have been well characterized on a global scale, the factors that influence the virus’ rapid evolution within and between human hosts are less clear. Given the modest effectiveness of seasonal vaccination, vaccine-induced antibody responses could serve as a potent selective pressure for novel influenza variants at the individual or community level. We used next generation sequencing of patient-derived viruses from a randomized, placebo-controlled trial of vaccine efficacy to characterize the diversity of influenza A virus and to define the impact of vaccine-induced immunity on within-host populations. Importantly, this study design allowed us to isolate the impact of vaccination while still studying natural infection. We used pre-season hemagglutination inhibition and neuraminidase inhibition titers to quantify vaccine-induced immunity directly and to assess its impact on intrahost populations. We identified 166 cases of H3N2 influenza over 3 seasons and 5119 person-years. We obtained whole genome sequence data for 119 samples and used a stringent and empirically validated analysis pipeline to identify intrahost single nucleotide variants at ≥1% frequency. Phylogenetic analysis of consensus hemagglutinin and neuraminidase sequences showed no stratification by pre-season HAI and NAI titer, respectively. In our study population, we found that the vast majority of intrahost single nucleotide variants were rare and that very few were found in more than one individual. Most samples had fewer than 15 single nucleotide variants across the entire genome, and the level of diversity did not significantly vary with day of sampling, vaccination status, or pre-season antibody titer. Contrary to what has been suggested in experimental systems, our data indicate that seasonal influenza vaccination has little impact on intrahost diversity in natural infection and that vaccine-induced immunity may be only a minor contributor to antigenic drift at local scales., Author summary Influenza is a significant global health problem. Vaccination is the best way to prevent influenza virus infection, and seasonal influenza vaccines are considered for reformulation each year in order to keep up with the virus’ evolution. Despite these efforts, vaccine recipients often develop an immune response that does not protect from infection. Given the current recommendation that all people over 6 months of age get vaccinated, it is important to understand how vaccination itself may impact viral evolution during natural human infection. We studied how vaccination may alter viral evolution within individuals, as each person harbors many highly-related influenza variants that differ in their ability to escape the immune response. We compared groups of people in a vaccine trial to determine the impact that vaccination has on viral diversity and variant selection within individuals. We did not detect significant differences in the number of variants detected or in the prevalence of mutations that could impact antibody binding based on vaccination group or antibody response. Our work suggests that vaccination is not a major factor in driving the emergence of new influenza strains at the level of the individual host.
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- 2017
34. The Mutational Robustness of Influenza A Virus
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William J. Fitzsimmons, Adam S. Lauring, Shawn E. Whitefield, John T. McCrone, and Elisa Visher
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0301 basic medicine ,RNA viruses ,Mutant ,Genetic Fitness ,medicine.disease_cause ,Influenza A virus ,lcsh:QH301-705.5 ,Pathology and laboratory medicine ,Genetics ,Viral Genomics ,Microbial Mutation ,High-Throughput Nucleotide Sequencing ,Genomics ,Medical microbiology ,Resistance mutation ,3. Good health ,Hemagglutinins ,Viruses ,Pathogens ,Research Article ,lcsh:Immunologic diseases. Allergy ,Substitution Mutation ,030106 microbiology ,Immunology ,Neuraminidase ,Microbial Genomics ,Biology ,Real-Time Polymerase Chain Reaction ,Transfection ,Research and Analysis Methods ,Microbiology ,Evolution, Molecular ,03 medical and health sciences ,Molecular evolution ,Virology ,medicine ,Humans ,Point Mutation ,Influenza viruses ,Molecular Biology Techniques ,Genome size ,Molecular Biology ,Medicine and health sciences ,Models, Genetic ,Point mutation ,Organisms ,Viral pathogens ,Robustness (evolution) ,Biology and Life Sciences ,Computational Biology ,Genome Analysis ,Genomic Libraries ,Microbial pathogens ,030104 developmental biology ,lcsh:Biology (General) ,A549 Cells ,DNA, Viral ,Mutation ,Mutagenesis, Site-Directed ,Parasitology ,lcsh:RC581-607 ,Orthomyxoviruses - Abstract
A virus’ mutational robustness is described in terms of the strength and distribution of the mutational fitness effects, or MFE. The distribution of MFE is central to many questions in evolutionary theory and is a key parameter in models of molecular evolution. Here we define the mutational fitness effects in influenza A virus by generating 128 viruses, each with a single nucleotide mutation. In contrast to mutational scanning approaches, this strategy allowed us to unambiguously assign fitness values to individual mutations. The presence of each desired mutation and the absence of additional mutations were verified by next generation sequencing of each stock. A mutation was considered lethal only after we failed to rescue virus in three independent transfections. We measured the fitness of each viable mutant relative to the wild type by quantitative RT-PCR following direct competition on A549 cells. We found that 31.6% of the mutations in the genome-wide dataset were lethal and that the lethal fraction did not differ appreciably between the HA- and NA-encoding segments and the rest of the genome. Of the viable mutants, the fitness mean and standard deviation were 0.80 and 0.22 in the genome-wide dataset and best modeled as a beta distribution. The fitness impact of mutation was marginally lower in the segments coding for HA and NA (0.88 ± 0.16) than in the other 6 segments (0.78 ± 0.24), and their respective beta distributions had slightly different shape parameters. The results for influenza A virus are remarkably similar to our own analysis of CirSeq-derived fitness values from poliovirus and previously published data from other small, single stranded DNA and RNA viruses. These data suggest that genome size, and not nucleic acid type or mode of replication, is the main determinant of viral mutational fitness effects., Author Summary Like other RNA viruses, influenza virus has a very high mutation rate. While high mutation rates may increase the rate at which influenza virus will adapt to a new host, acquire a new route of transmission, or escape from host immune surveillance, data from model systems suggest that most new viral mutations are either lethal or highly detrimental. Mutational robustness refers to the ability of a virus to tolerate, or buffer, these mutations. The mutational robustness of a virus will determine which mutations are maintained in a population and may have a greater impact on viral evolution than mutation rate. We defined the mutational robustness of influenza A virus by measuring the fitness of a large number of viruses, each with a single point mutation. We found that the overall robustness of influenza was similar to that of poliovirus and other viruses of similar size. Interestingly, mutations appeared to be more easily accommodated in hemagglutinin and neuraminidase than elsewhere in the genome. This work will inform models of influenza evolution at the global and molecular scale.
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- 2016
35. Measurements of Intrahost Viral Diversity Are Extremely Sensitive to Systematic Errors in Variant Calling
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Adam S. Lauring and John T. McCrone
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0301 basic medicine ,Systematic error ,030106 microbiology ,Immunology ,Computational biology ,Genome, Viral ,Biology ,medicine.disease_cause ,Microbiology ,Set (abstract data type) ,03 medical and health sciences ,Virology ,Rare mutations ,Influenza, Human ,Influenza A virus ,medicine ,Humans ,Genetic Variation ,High-Throughput Nucleotide Sequencing ,Data set ,030104 developmental biology ,Genetic Diversity and Evolution ,Insect Science ,Benchmark (computing) ,RNA, Viral ,Sample collection ,Algorithms ,Diversity (business) - Abstract
With next-generation sequencing technologies, it is now feasible to efficiently sequence patient-derived virus populations at a depth of coverage sufficient to detect rare variants. However, each sequencing platform has characteristic error profiles, and sample collection, target amplification, and library preparation are additional processes whereby errors are introduced and propagated. Many studies account for these errors by using ad hoc quality thresholds and/or previously published statistical algorithms. Despite common usage, the majority of these approaches have not been validated under conditions that characterize many studies of intrahost diversity. Here, we use defined populations of influenza virus to mimic the diversity and titer typically found in patient-derived samples. We identified single-nucleotide variants using two commonly employed variant callers, DeepSNV and LoFreq. We found that the accuracy of these variant callers was lower than expected and exquisitely sensitive to the input titer. Small reductions in specificity had a significant impact on the number of minority variants identified and subsequent measures of diversity. We were able to increase the specificity of DeepSNV to >99.95% by applying an empirically validated set of quality thresholds. When applied to a set of influenza virus samples from a household-based cohort study, these changes resulted in a 10-fold reduction in measurements of viral diversity. We have made our sequence data and analysis code available so that others may improve on our work and use our data set to benchmark their own bioinformatics pipelines. Our work demonstrates that inadequate quality control and validation can lead to significant overestimation of intrahost diversity. IMPORTANCE Advances in sequencing technology have made it feasible to sequence patient-derived viral samples at a level sufficient for detection of rare mutations. These high-throughput, cost-effective methods are revolutionizing the study of within-host viral diversity. However, the techniques are error prone, and the methods commonly used to control for these errors have not been validated under the conditions that characterize patient-derived samples. Here, we show that these conditions affect measurements of viral diversity. We found that the accuracy of previously benchmarked analysis pipelines was greatly reduced under patient-derived conditions. By carefully validating our sequencing analysis using known control samples, we were able to identify biases in our method and to improve our accuracy to acceptable levels. Application of our modified pipeline to a set of influenza virus samples from a cohort study provided a realistic picture of intrahost diversity and suggested the need for rigorous quality control in such studies.
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- 2016
36. Next-Generation Sequencing of Influenza Viruses in a Household Cohort Accurately Identifies Transmission Pairs and Reveals a Bottleneck Size of Close to One
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Rachel Truscon, Emileigh Johnson, Ryan E. Malosh, John T. McCrone, Adam S. Lauring, Joshua G. Petrie, Arnold S. Monto, Kari Debbink, and Suzanne E. Ohmit
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Infectious Diseases ,Transmission (mechanics) ,Oncology ,law ,Cohort ,Orthomyxoviridae ,Computational biology ,Biology ,biology.organism_classification ,Bioinformatics ,DNA sequencing ,Bottleneck ,law.invention - Published
- 2016
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37. Effect of diet on the survival and phenotype of a mouse model for spinal muscular atrophy
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Matthew E.R. Butchbach, Ferrill F. Rose, John Marston, Rachel Sinnott, John T. McCrone, Christian L. Lorson, and Sarah Rhoades
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Genetically modified mouse ,Blood Glucose ,Male ,medicine.medical_specialty ,Biophysics ,Mice, Transgenic ,Biology ,Biochemistry ,Article ,Muscular Atrophy, Spinal ,Mice ,Atrophy ,Internal medicine ,medicine ,Animals ,Molecular Biology ,Proximal spinal muscular atrophy ,3-Hydroxybutyric Acid ,Cell Biology ,Anatomy ,Spinal muscular atrophy ,Motor neuron ,medicine.disease ,SMA ,Spinal cord ,Phenotype ,Survival of Motor Neuron 1 Protein ,Diet ,Disease Models, Animal ,Endocrinology ,medicine.anatomical_structure ,Female - Abstract
Proximal spinal muscular atrophy (SMA) is a leading genetic cause of infant death. Patients with SMA lose alpha-motor neurons in the ventral horn of the spinal cord which leads to skeletal muscle weakness and atrophy. SMA is the result of reduction in Survival Motor Neuron (SMN) expression. Transgenic mouse models of SMA have been generated and are extremely useful in understanding the mechanisms of motor neuron degeneration in SMA and in developing new therapeutic candidates for SMA patients. Several research groups have reported varying average lifespans of SMNDelta7 SMA mice (SMN2(+/+);SMNDelta7(+/+);mSmn(-/-)), the most commonly used mouse model for preclinical therapeutic candidate testing. One environmental factor that varied between research groups was maternal diet. In this study, we compared the effects of two different commercially available rodent chows (PicoLab20 Mouse diet and Harlan-Teklad 22/5 diet) on the survival and motor phenotype of the SMNDelta7 mouse model of SMA. Specifically, the PicoLab20 diet significantly extends the average lifespan of the SMNDelta7 SMA mice by approximately 25% and improved the motor phenotype as compared to the Harlan diet. These findings indicate that maternal diet alone can have considerable impact on the SMA phenotype.
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- 2009
38. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2
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Philippe Lemey, Samuel L. Hong, Verity Hill, Guy Baele, Chiara Poletto, Vittoria Colizza, Áine O’Toole, John T. McCrone, Kristian G. Andersen, Michael Worobey, Martha I. Nelson, Andrew Rambaut, and Marc A. Suchard
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Science - Abstract
Spatiotemporal sampling gaps in existing pathogen genomic data limits their use in understanding epidemiological patterns. Here, the authors apply a phylogeographic approach with SARS-CoV-2 genomes to accurately reproduce pathogen spread by accounting for spatial biases and travel history of the individual.
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- 2020
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39. Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020
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Alm E., Broberg E.K., Connor T., Hodcroft E.B., Komissarov A.B., Maurer-Stroh S., Melidou A., Neher R.A., O'Toole A., Pereyaslov D., Beerenwinkel N., Posada-Cespedes S., Jablonski K.P., Ferreira P.F., Topolsky I., Avsic-Zupanc T., Korva M., Poljak M., Zakotnik S., Zorec T.M., Bragstad K., Hungnes O., Stene-Johansen K., Reusken C., Meijer A., Vennema H., Ruiz-Roldan L., Bracho M.A., Garcia-Gonzalez N., Chiner-Oms A., Cancino-Munoz I., Comas I., Goig G.A., Torres-Puente M., Lopez M.G., Martinez-Priego L., D'Auria G., Ruiz-Hueso P., Ferrus-Abad L., de Marco G., Galan-Vendrell I., Carbo-Ramirez S., Ruiz-Rodriguez P., Coscolla M., Polackova K., Kramna L., Cinek O., Richter J., Krashias G., Tryfonos C., Bashiardes S., Koptides D., Christodoulou C., Bartolini B., Gruber C.E., Di Caro A., Castilletti C., Stefani F., Rimoldi S.G., Romeri F., Salerno F., Polesello S., Nagy A., Jirincova H., Vecerova J., Novakova L., Cordey S., Murtskhvaladze M., Kotaria N., Schar T., Beisel C., Vugrek O., Rokic F., Trgovec-Greif L., Jurak I., Rukavina T., Sucic N., Schonning K., Karst S.M., Kirkegaard R.H., Michaelsen T.Y., Sorensen E.A., Knutson S., Brandt J., Le-Quy V., Sorensen T., Petersen C., Pedersen M.S., Larsen S.L., Skov M.N., Rasmussen M., Fonager J., Fomsgaard A., Maksyutov R.A., Gavrilova E.V., Pyankov O.V., Bodnev S.A., Tregubchak T.V., Shvalov A.N., Antonets D.V., Resende P.C., Goya S., Perrin A., Lee R.T., Yadahalli S., Han A.X., Russell C.A., Schmutz S., Zaheri M., Kufner V., Huber M., Trkola A., Antwerpen M., Walter M.C., van der Werf S., Gambaro F., Behillil S., Enouf V., Donati F., Ustinova M., Rovite V., Klovins J., Savicka O., Wienecke-Baldacchino A.K., Ragimbeau C., Fournier G., Mossong J., Aberle S.W., Haukland M., Enkirch T., Advani A., Karlberg M.L., Lindsjo O.K., Broddesson S., Slavikova M., Lickova M., Klempa B., Staronova E., Ticha E., Szemes T., Rusnakova D., Stadler T., Quer J., Anton A., Andres C., Pinana M., Garcia-Cehic D., Pumarola T., Izopet J., Gioula G., Exindari M., Papa A., Chatzidimitriou D., Metallidis S., Pappa S., Macek M., Geryk J., Broz P., Briksi A., Hubacek P., Drevinek P., Zajac M., Kvapil P., Holub M., Kvapilova K., Novotny A., Kasny M., Klempt P., Vapalahti O., Smura T., Sironen T., Selhorst P., Anthony C., Arien K., Simon-Loriere E., Rabalski L., Bienkowska-Szewczyk K., Borges V., Isidro J., Gomes J.P., Guiomar R., Pechirra P., Costa I., Duarte S., Vieira L., Pyrc K., Zuckerman N.S., Turdikulova S., Abdullaev A., Dalimova D., Abdurakhimov A., Tagliabracci A., Alessandrini F., Melchionda F., Onofri V., Turchi C., Bagnarelli P., Menzo S., Caucci S., Di Sante L., Popa A., Genger J.-W., Agerer B., Lercher A., Endler L., Smyth M., Penz T., Schuster M., Senekowitsch M., Laine J., Bock C., Bergthaler A., Shevtsov A., Kalendar R., Ramanculov Y., Graf A., Muenchhoff M., Keppler O.T., Krebs S., Blum H., Marcello A., Licastro D., D'Agaro P., Laubscher F., Vidanovic D., Tesovic B., Volkening J., Clementi N., Mancini N., Rupnik M., Mahnic A., Walker A., Houwaart T., Wienemann T., Vasconcelos M.K., Strelow D., Jensen B.-E.O., Senff T., Hulse L., Adams O., Andree M., Hauka S., Feldt T., Keitel V., Kindgen-Milles D., Timm J., Pfeffer K., Dilthey A.T., Moore C., Ozdarendeli A., Pavel S.T.I., Yetiskin H., Aydin G., Holyavkin C., Uygut M.A., Cevik C., Shchetinin A., Gushchin V., Dinler-Doganay G., Doganay L., Kizilboga-Akgun T., Karacan I., Pancer K., Maes P., Marti-Carreras J., Wawina-Bokalanga T., Vanmechelen B., Thurmer A., Wedde M., Durrwald R., von Kleist M., Drechsel O., Wolff T., Fuchs S., Kmiecinski R., Michel J., Nitsche A., Casas I., Caballero M.I., Zaballos A., Jimenez P., Jimenez M., Fernandez S.M., Fernandez S.V., de la Plaza I.C., Fadeev A., Ivanova A., Sergeeva M., Stefanelli P., Estee Torok M., Hall G., da Silva Filipe A., Turtle L., Afifi S., McCluggage K., Beer R., Ledesma J., Maksimovic J., Spellman K., Hamilton W.L., Marchbank A., Southgate J.A., Underwood A., Taylor B., Yeats C., Abudahab K., Gemmell M.R., Eccles R., Lucaci A., Nelson C.A., Rainbow L., Whitehead M., Gregory R., Haldenby S., Paterson S., Hughes M.A., Curran M.D., Baker D., Tucker R., Green L.R., Feltwell T., Halstead F.D., Wyles M., Jahun A.S., Ahmad S.S.Y., Georgana I., Goodfellow I., Yakovleva A., Meredith L.W., Gavriil A., Awan A.R., Fisher C., Edgeworth J., Lynch J., Moore N., Williams R., Kidd S.P., Cortes N., Brunker K., McCrone J.T., Quick J., Duckworth N., Walsh S., Sloan T., Ludden C., George R.P., Eltringham G., Brown J.R., Aranday-Cortes E., Shepherd J.G., Hughes J., Li K.K., Williams T.C., Johnson N., Jesudason N., Mair D., Thomson E., Shah R., Parr Y.A., Carmichael S., Robertson D.L., Nomikou K., Broos A., Niebel M., Smollett K., Tong L., Miah S., Wittner A., Phillips N., Payne B., Dewar R., Holmes A., Bolt F., Price J.R., Mookerjee S., Sethi D.K., Potter W., Stanley R., Prakash R., Dervisevic S., Graham J.C., Nelson A., Smith D., Young G.R., Yew W.C., Todd J.A., Trebes A., Andersson M., Bull M., Watkins J., Birchley A., Gatica-Wilcox B., Gilbert L., Kumziene-Summerhayes S., Rey S., Chauhan A., Butcher E., Bicknell K., Elliott S., Glaysher S., Lackenby A., Bibby D., Platt S., Mohamed H., Machin N.W., Mbisa J.L., Evans J., Perry M., Pacchiarini N., Corden S., Adams A.G., Gaskin A., Coombs J., Graham L.J., Cottrell S., Morgan M., Gifford L., Kolyva A., Rudder S.J., Trotter A.J., Mather A.E., Aydin A., Page A.J., Kay G.L., de Oliveira Martins L., Yasir M., Alikhan N.-F., Thomson N.M., Gilroy R., Kingsley R.A., O'Grady J., Gutierrez A.V., Diaz M., Viet T.L., Tedim A.P., Adriaenssens E.M., Patrick Mcclure C., Sang F., Clark G., Howson-Wells H.C., Debebe J., Ball J., Chappell J., Khakh M., Carlile M., Loose M., Lister M.M., Holmes N., Tsoleridis T., Fleming V.M., Wright V., Smith W., Gallagher M.D., Parker M., Partridge D.G., Evans C., Baker P., Essex S., Liggett S., Keeley A.J., Bashton M., Rooke S., Dervisavic S., Meader E.J., Lopez C.E.B., Angyal A., Kristiansen M., Tutill H.J., Findlay J., Mestek-Boukhibar L., Forrest L., Dyal P., Williams R.J., Panchbhaya Y., Williams C.A., Roy S., Pandey S., Stockton J., Loman N.J., Poplawski R., Nicholls S., Rowe W.P.M., Khokhar F., Pinckert M.L., Hosmillo M., Chaudhry Y., Caller L.G., Davidson R.K., Griffith L., Rambaut A., Jackson B., Colquhoun R., Hill V., Nichols J., Asamaphan P., Darby A., Jackson K.A., Iturriza-Gomara M., Vamos E.E., Green A., Aanensen D., Bonsall D., Buck D., Macintyre-Cockett G., de Cesare M., Pybus O., Golubchik T., Scarlett G., Loveson K.F., Robson S.C., Beckett A., Lindsey B., Groves D.C., Parsons P.J., McHugh M.P., Barnes J.D., Manso C.F., Grammatopoulos D., Menger K.E., Harrison E., Gunson R., Peacock S.J., Gonzalez G., Carr M., Mihaela L., Popovici O., Brytting M., Bresner C., Fuller W., Workman T., Mentis A.F., Kossyvakis A., Karamitros T., Pogka V., Kalliaropoulos A., Horefti E., Kontou A., Martinez-Gonzalez B., Labropoulou V., Voulgari-Kokota A., Evangelidou M., Bizta P., Belimezi M., Lambrechts L., Doymaz M.Z., Yazici M.K., Cetin N.S., Karaaslan E., Kallio-Kokko H., Virtanen J., Suvanto M., Nguyen P.T., Ellonen P., Hannula S., Kangas H., Sreenu V.B., Burian K., Terhes G., Gombos K., Gyenesei A., Urban P., Herczeg R., Jakab F., Kemenesi G., Toth G.E., Somogyi B., Zana B., Zeghbib S., Kuczmog A., Foldes F., Lanszki Z., Madai M., Papp H., Pereszlenyi C.I., Babinszky G.C., Dudas G., Csoma E., Abou Tayoun A.N., Alsheikh-Ali A.A., Loney T., Nowotny N., Abdul-Wahab O., Gonzalez-Candelas F., Andersen M.H., Taylor S., MARTI CARRERAS, Joan, Vanmechelen, Bert, Wawina, Tony, Medical Microbiology and Infection Prevention, AII - Infectious diseases, WHO European Region Sequencing Lab, GISAID EpiCoV Grp, Erik, Alm, Eeva K, Broberg, Thomas, Connor, Emma B, Hodcroft, Andrey B, Komissarov, Sebastian, Maurer-Stroh, Angeliki, Melidou, Richard A, Neher, Áine, O’Toole, Dmitriy, Pereyaslov, WHO European Region sequencing laboratories and GISAID EpiCoV group (Niko Beerenwinkel, The, Posada-Céspedes, Susana, Philipp, Kim, Jablonski, Falé Ferreira, Pedro, Topolsky, Ivan, Avšičžupanc, Tatjana, Korva, Miša, Poljak, Mario, Zakotnik, Samo, Tomaž, Zorec, Mark, Bragstad, Karoline, Hungnes, Olav, Stene-Johansen, Kathrine, Reusken, Chantal, Meijer, Adam, Vennema, Harry, Ruiz-Roldán, Lidia, Alma Bracho, María, García-González, Neri, Chiner-Oms, Álvaro, Cancino-Muñoz, Irving, Comas, Iñaki, A Goig, Galo, Torres-Puente, Manuela, G López, Mariana, Martínez-Priego, Llúcia, D’Auria, Giuseppe, LoretoFerrús-Abad, de Marco, Griselda, Galan-Vendrell, Inmaculada, Carbó-Ramirez, Sandra, Ruíz-Hueso, Paula, Coscollá, Mireia, Polackova, Katerina, Kramna, Lenka, Cinek, Ondrej, Richter, Jan, Krashias, George, Tryfonos, Christina, Bashiardes, Stavro, Koptides, Dana, Christodoulou, Christina, Bartolini, Barbara, Em Gruber, Cesare, Di Caro, Antonino, Castilletti, Concetta, Stefani, Fabrizio, Giordana Rimoldi, Sara, Romeri, Francesca, Salerno, Franco, Polesello, Stefano, Nagy, Alexander, Jirincova, Helena, Vecerova, Jaromira, Novakova, Ludmila, Cordey, Samuel, Murtskhvaladze, Marine, Kotaria, Nato, Schär, Tobia, Beisel, Christian, Vugrek, Oliver, Rokić, Filip, Trgovecgreif, Lovro, Jurak, Igor, Rukavina, Tomislav, Sučić, Neven, Schønning, Kristian, M Karst, Søren, H Kirkegaard, Rasmu, Y Michaelsen, Thoma, Aa Sørensen, Emil, Knutson, Simon, Brandt, Jakob, Le-Quy, Vang, Sørensen, Trine, Petersen, Celine, Schou Pedersen, Martin, Løkkegaard Larsen, Sanne, Nielsine Skov, Marianne, Rasmussen, Morten, Fonager, Jannik, Fomsgaard, Ander, Amirovich Maksyutov, Rinat, Vasil’Evna Gavrilova, Elena, Victorovich Pyankov, Oleg, Alexandrovich Bodnev, Sergey, Vladimirovna Tregubchak, Tatyana, Nikolayevich Shvalov, Alexander, Victorovich Antonets, Deni, Cristina Resende, Paola, Goya, Stephanie, Perrin, Amandine, Tc Lee, Raphael, Yadahalli, Shilpa, X Han, Alvin, A Russell, Colin, Schmutz, Stefan, Zaheri, Maryam, Kufner, Verena, Huber, Michael, Trkola, Alexandra, Antwerpen, Marku, C Walter, Mathia, van der Werf, Sylvie, Gambaro, Fabiana, Behillil, Sylvie, Enouf, Vincent, Donati, Flora, Ustinova, Monta, Rovite, Vita, Klovins, Jani, Savicka, Oksana, K Wienecke-Baldacchino, Anke, Ragimbeau, Catherine, Fournier, Guillaume, Mossong, Joël, W Aberle, Stephan, Haukland, Mattia, Enkirch, Theresa, Advani, Abdolreza, Lind Karlberg, Maria, Karlsson Lindsjö, Oskar, Broddesson, Sandra, Sláviková, Monika, Ličková, Martina, Klempa, Bori, Staroňová, Edita, Tichá, Elena, Szemes, Tomáš, Rusňáková, Diana, Stadler, Tanja, Quer, Josep, Anton, Andre, Andres, Cristina, Piñana, Maria, Garcia-Cehic, Damir, Pumarola, Toma, Izopet, Jacque, Gioula, Georgia, Exindari, Maria, Papa, Anna, Chatzidimitriou, Dimitrio, Metallidis, Symeon, Pappa, Stella, Macek Jr, Milan, Geryk, Jan, Brož, Petr, Briksí, Aleš, Hubáček, Petr, Dřevínek, Pavel, Zajac, Miroslav, Kvapil, Petr, Holub, Michal, Kvapilová, Kateřina, Novotný, Adam, Kašný, Martin, Klempt, Petr, Vapalahti, Olli, Smura, Teemu, Sironen, Tarja, Selhorst, Philippe, Anthony, Colin, Ariën, Kevin, Simon-Loriere, Etienne, Rabalski, Lukasz, Bienkowska-Szewczyk, Krystyna, Borges, Vítor, Isidro, Joana, Paulo Gomes, João, Guiomar, Raquel, Pechirra, Pedro, Costa, Inê, Duarte, Sílvia, Vieira, Luí, Pyrc, Krzysztof, S Zuckerman, Neta, Turdikulova, Shahlo, Abdullaev, Alisher, Dalimova, Dilbar, Abdurakhimov, Abror, Tagliabracci, Adriano, Alessandrini, Federica, Melchionda, Filomena, Onofri, Valerio, Turchi, Chiara, Bagnarelli, Patrizia, Menzo, Stefano, Caucci, Sara, Di Sante, Laura, Popa, Alexandra, Genger, Jakob-Wendelin, Agerer, Benedikt, Lercher, Alexander, Endler, Luka, Smyth, Mark, Penz, Thoma, Schuster, Michael, Senekowitsch, Martin, Laine, Jan, Bock, Christoph, Bergthaler, Andrea, Shevtsov, Alexandr, Kalendar, Ruslan, Ramanculov, Yerlan, Graf, Alexander, Muenchhoff, Maximilian, T Keppler, Oliver, Krebs, Stefan, Blum, Helmut, Marcello, Alessandro, Licastro, Danilo, D’Agaro, Pierlanfranco, Laubscher, Florian, Vidanovic, Dejan, Tesovic, Bojana, Volkening, Jeremy, Clementi, Nicola, Mancini, Nicasio, Rupnik, Maja, Mahnic, Aleksander, Walker, Andrea, Houwaart, Torsten, Wienemann, Tobia, Kohns Vasconcelos, Malte, Strelow, Daniel, Ole Jensen, Björn-Erik, Senff, Tina, Hülse, Lisanna, Adams, Ortwin, Andree, Marcel, Hauka, Sandra, Feldt, Torsten, Keitel, Verena, Kindgen-Milles, Detlef, Timm, Jörg, Pfeffer, Klau, T Dilthey, Alexander, Moore, Catherine, Ozdarendeli, Aykut, Terkis Islam Pavel, Shaikh, Yetiskin, Hazel, Aydin, Gunsu, Holyavkin, Can, Ali Uygut, Muhammet, Cevik, Ceren, Shchetinin, Alexey, Gushchin, Vladimir, Dinler-Doganay, Gizem, Doganay, Levent, Kizilboga-Akgun, Tugba, Karacan, Ilker, Pancer, Katarzyna, Maes, Piet, Martí-Carreras, Joan, Wawina-Bokalanga, Tony, Thürmer, Andrea, Wedde, Marianne, Dürrwald, Ralf, Von Kleist, Max, Drechsel, Oliver, Wolff, Thorsten, Fuchs, Stephan, Kmiecinski, Rene, Michel, Janine, Nitsche, Andrea, Casas, Inmaculada, Iglesias Caballero, María, Zaballos, Ángel, Jiménez, Pilar, Jiménez, Mercede, Monzón Fernández, Sara, Varona Fernández, Sarai, Cuesta De La Plaza, Isabel, Fadeev, Artem, Ivanova, Anna, Sergeeva, Mariia, Stefanelli, Paola, Estee Torok, M, Hall, Grant, da Silva Filipe, Ana, Turtle, Lance, Afifi, Safiah, Mccluggage, Kathryn, Beer, Robert, Ledesma, Juan, Maksimovic, Joshua, Spellman, Karla, L Hamilton, William, Marchbank, Angela, Alexander Southgate, Joel, Underwood, Anthony, Taylor, Ben, Yeats, Corin, Abudahab, Khalil, R Gemmell, Matthew, Eccles, Richard, Lucaci, Anita, Abigail Nelson, Charlotte, Rainbow, Lucille, Whitehead, Mark, Gregory, Richard, Haldenby, Sam, Paterson, Steve, A Hughes, Margaret, D Curran, Martin, Baker, David, Tucker, Rachel, R Green, Luke, Feltwell, Theresa, D Halstead, Fenella, Wyles, Matthew, S Jahun, Aminu, Y Ahmad, Shazaad S, Georgana, Iliana, Goodfellow, Ian, Yakovleva, Anna, W Meredith, Luke, Gavriil, Artemi, Raza Awan, Ali, Fisher, Chloe, Jonathan, European Centre for Disease Prevention and Control [Stockholm, Sweden] (ECDC), Cardiff University, Public Health Wales [Cardiff, Royaume uni], University of Basel (Unibas), Research Institute of Influenza, St. Petersburg, Russia, Agency for science, technology and research [Singapore] (A*STAR), National University of Singapore (NUS), University of Edinburgh, WHO Regional Office for Europe [Copenhagen], We gratefully acknowledge the authors, originating and submitting laboratories of the sequences from GISAID’s EpiCoV Database used in the phylogenetic analysis. We gratefully acknowledge all the staff working with sample collection, sample preparation, sequencing, data analysis and data sharing in all laboratories in the WHO European Region for making this work possible, The WHO European Region sequencing laboratories and GISAID EpiCoV group*: Niko Beerenwinkel, Susana Posada-Céspedes, Kim Philipp Jablonski, Pedro Falé Ferreira, Ivan Topolsky, Tatjana Avšič-Županc, Miša Korva, Mario Poljak, Samo Zakotnik, Tomaž Mark Zorec, Karoline Bragstad, Olav Hungnes, Kathrine Stene-Johansen, Chantal Reusken, Adam Meijer, Harry Vennema, Lidia Ruiz-Roldán, María Alma Bracho, Neris García-González, Álvaro Chiner-Oms, Irving Cancino-Muñoz, Iñaki Comas, Galo A Goig, Manuela Torres-Puente, Mariana G López, Llúcia Martínez-Priego, Giuseppe D'Auria, Paula Ruíz-Hueso, Loreto Ferrús-Abad, Griselda de Marco, Inmaculada Galan-Vendrell, Sandra Carbó-Ramirez, Paula Ruiz-Rodriguez, Mireia Coscollá, Katerina Polackova, Lenka Kramna, Ondrej Cinek, Jan Richter, George Krashias, Christina Tryfonos, Stavros Bashiardes, Dana Koptides, Christina Christodoulou, Barbara Bartolini, Cesare Em Gruber, Antonino Di Caro, Concetta Castilletti, Fabrizio Stefani, Sara Giordana Rimoldi, Francesca Romeri, Franco Salerno, Stefano Polesello, Alexander Nagy, Helena Jirincova, Jaromira Vecerova, Ludmila Novakova, Samuel Cordey, Marine Murtskhvaladze, Nato Kotaria, Tobias Schär, Christian Beisel, Oliver Vugrek, Filip Rokić, Lovro Trgovec-Greif, Igor Jurak, Tomislav Rukavina, Neven Sučić, Kristian Schønning, Søren M Karst, Rasmus H Kirkegaard, Thomas Y Michaelsen, Emil Aa Sørensen, Simon Knutson, Jakob Brandt, Vang Le-Quy, Trine Sørensen, Celine Petersen, Martin Schou Pedersen, Sanne Løkkegaard Larsen, Marianne Nielsine Skov, Morten Rasmussen, Jannik Fonager, Anders Fomsgaard, Rinat Amirovich Maksyutov, Elena Vasil'Evna Gavrilova, Oleg Victorovich Pyankov, Sergey Alexandrovich Bodnev, Tatyana Vladimirovna Tregubchak, Alexander Nikolayevich Shvalov, Denis Victorovich Antonets, Paola Cristina Resende, Stephanie Goya, Amandine Perrin, Raphael Tc Lee, Shilpa Yadahalli, Alvin X Han, Colin A Russell, Stefan Schmutz, Maryam Zaheri, Verena Kufner, Michael Huber, Alexandra Trkola, Markus Antwerpen, Mathias C Walter, Sylvie van der Werf, Fabiana Gambaro, Sylvie Behillil, Vincent Enouf, Flora Donati, Monta Ustinova, Vita Rovite, Janis Klovins, Oksana Savicka, Anke K Wienecke-Baldacchino, Catherine Ragimbeau, Guillaume Fournier, Joël Mossong, Stephan W Aberle, Mattias Haukland, Theresa Enkirch, Abdolreza Advani, Maria Lind Karlberg, Oskar Karlsson Lindsjö, Sandra Broddesson, Monika Sláviková, Martina Ličková, Boris Klempa, Edita Staroňová, Elena Tichá, Tomáš Szemes, Diana Rusňáková, Tanja Stadler, Josep Quer, Andres Anton, Cristina Andres, Maria Piñana, Damir Garcia-Cehic, Tomas Pumarola, Jacques Izopet, Georgia Gioula, Maria Exindari, Anna Papa, Dimitrios Chatzidimitriou, Symeon Metallidis, Stella Pappa, Milan Macek Jr, Jan Geryk, Petr Brož, Aleš Briksí, Petr Hubáček, Pavel Dřevínek, Miroslav Zajac, Petr Kvapil, Michal Holub, Kateřina Kvapilová, Adam Novotný, Martin Kašný, Petr Klempt, Olli Vapalahti, Teemu Smura, Tarja Sironen, Philippe Selhorst, Colin Anthony, Kevin Ariën, Etienne Simon-Loriere, Lukasz Rabalski, Krystyna Bienkowska-Szewczyk, Vítor Borges, Joana Isidro, João Paulo Gomes, Raquel Guiomar, Pedro Pechirra, Inês Costa, Sílvia Duarte, Luís Vieira, Krzysztof Pyrc, Neta S Zuckerman, Shahlo Turdikulova, Alisher Abdullaev, Dilbar Dalimova, Abror Abdurakhimov, Adriano Tagliabracci, Federica Alessandrini, Filomena Melchionda, Valerio Onofri, Chiara Turchi, Patrizia Bagnarelli, Stefano Menzo, Sara Caucci, Laura Di Sante, Alexandra Popa, Jakob-Wendelin Genger, Benedikt Agerer, Alexander Lercher, Lukas Endler, Mark Smyth, Thomas Penz, Michael Schuster, Martin Senekowitsch, Jan Laine, Christoph Bock, Andreas Bergthaler, Alexandr Shevtsov, Ruslan Kalendar, Yerlan Ramanculov, Alexander Graf, Maximilian Muenchhoff, Oliver T Keppler, Stefan Krebs, Helmut Blum, Alessandro Marcello, Danilo Licastro, Pierlanfranco D'Agaro, Florian Laubscher, Dejan Vidanovic, Bojana Tesovic, Jeremy Volkening, Nicola Clementi, Nicasio Mancini, Maja Rupnik, Aleksander Mahnic, Andreas Walker, Torsten Houwaart, Tobias Wienemann, Malte Kohns Vasconcelos, Daniel Strelow, Björn-Erik Ole Jensen, Tina Senff, Lisanna Hülse, Ortwin Adams, Marcel Andree, Sandra Hauka, Torsten Feldt, Verena Keitel, Detlef Kindgen-Milles, Jörg Timm, Klaus Pfeffer, Alexander T Dilthey, Catherine Moore, Aykut Ozdarendeli, Shaikh Terkis Islam Pavel, Hazel Yetiskin, Gunsu Aydin, Can Holyavkin, Muhammet Ali Uygut, Ceren Cevik, Alexey Shchetinin, Vladimir Gushchin, Gizem Dinler-Doganay, Levent Doganay, Tugba Kizilboga-Akgun, Ilker Karacan, Katarzyna Pancer, Piet Maes, Joan Martí-Carreras, Tony Wawina-Bokalanga, Bert Vanmechelen, Andrea Thürmer, Marianne Wedde, Ralf Dürrwald, Max Von Kleist, Oliver Drechsel, Thorsten Wolff, Stephan Fuchs, Rene Kmiecinski, Janine Michel, Andreas Nitsche, Inmaculada Casas, María Iglesias Caballero, Ángel Zaballos, Pilar Jiménez, Mercedes Jiménez, Sara Monzón Fernández, Sarai Varona Fernández, Isabel Cuesta De La Plaza, Artem Fadeev, Anna Ivanova, Mariia Sergeeva, Paola Stefanelli, M Estee Torok, Grant Hall, Ana da Silva Filipe, Lance Turtle, Safiah Afifi, Kathryn Mccluggage, Robert Beer, Juan Ledesma, Joshua Maksimovic, Karla Spellman, William L Hamilton, Angela Marchbank, Joel Alexander Southgate, Anthony Underwood, Ben Taylor, Corin Yeats, Khalil Abudahab, Matthew R Gemmell, Richard Eccles, Anita Lucaci, Charlotte Abigail Nelson, Lucille Rainbow, Mark Whitehead, Richard Gregory, Sam Haldenby, Steve Paterson, Margaret A Hughes, Martin D Curran, David Baker, Rachel Tucker, Luke R Green, Theresa Feltwell, Fenella D Halstead, Matthew Wyles, Aminu S Jahun, Shazaad S Y Ahmad, Iliana Georgana, Ian Goodfellow, Anna Yakovleva, Luke W Meredith, Artemis Gavriil, Ali Raza Awan, Chloe Fisher, Jonathan Edgeworth, Jessica Lynch, 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Ethan Butcher, Kelly Bicknell, Scott Elliott, Sharon Glaysher, Angie Lackenby, David Bibby, Steven Platt, Hodan Mohamed, Nicholas William Machin, Jean Lutamyo Mbisa, Jonathan Evans, Malorie Perry, Nicole Pacchiarini, Sally Corden, Alexander Geraint Adams, Amy Gaskin, Jason Coombs, Lee John Graham, Simon Cottrell, Mari Morgan, Laura Gifford, Anastasia Kolyva, Steven John Rudder, Alexander J Trotter, Alison E Mather, Alp Aydin, Andrew J Page, Gemma L Kay, Leonardo de Oliveira Martins, Muhammad Yasir, Nabil-Fareed Alikhan, Nicholas M Thomson, Rachel Gilroy, Robert A Kingsley, Justin O'Grady, Ana Victoria Gutierrez, Maria Diaz, Thanh Le Viet, Ana P Tedim, Evelien M Adriaenssens, C Patrick Mcclure, Christopher Moore, Fei Sang, Gemma Clark, Hannah C Howson-Wells, Johnny Debebe, Jonathan Ball, Joseph Chappell, Manjinder Khakh, Matthew Carlile, Matthew Loose, Michelle M Lister, Nadine Holmes, Theocharis Tsoleridis, Vicki M Fleming, Victoria Wright, Wendy Smith, Michael D Gallagher, Matthew 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H., Taylor, S., European Centre for Disease Prevention and Control (ECDC), Public Health Wales Microbiology Cardiff, Faculty of Agriculture and Forestry, Department of Agricultural Sciences, and Institute of Biotechnology
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Infecções Respiratórias ,0301 basic medicine ,MESH: Coronavirus Infections ,Epidemiology ,[SDV]Life Sciences [q-bio] ,Distribution (economics) ,Wastewater ,MESH: Base Sequence ,Severe Acute Respiratory Syndrome ,MESH: World Health Organization ,Pandemic ,MESH: Coronavirus ,MESH: COVID-19 ,Sequencing ,Viral ,Clade ,Nomenclature ,Genome ,biology ,COVID-19 ,Europe ,NGS ,SARS-CoV-2 ,WGS ,nomenclature ,sequencing ,Base Sequence ,Betacoronavirus ,Coronavirus ,Coronavirus Infections ,Genome, Viral ,Humans ,Phylogeography ,Pneumonia, Viral ,RNA, Viral ,RNA-Dependent RNA Polymerase ,Spatio-Temporal Analysis ,World Health Organization ,Pandemics ,C500 ,European region ,3. Good health ,Geography ,MESH: Phylogeography ,MESH: RNA-Dependent RNA Polymerase ,MESH: RNA, Viral ,MESH: Betacoronavirus ,Spatio-Temporal Analysi ,MESH: Genome, Viral ,Cartography ,Human ,Bioquímica ,MESH: Pandemics ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Coronaviru ,030106 microbiology ,03 medical and health sciences ,MESH: Spatio-Temporal Analysis ,MESH: Severe Acute Respiratory Syndrome ,Virology ,MESH: SARS-CoV-2 ,Whole genome sequencing ,MESH: Humans ,Whole Genome Sequencing ,Betacoronaviru ,Coronavirus Infection ,business.industry ,Public Health, Environmental and Occupational Health ,Pneumonia ,biology.organism_classification ,B900 ,030104 developmental biology ,MESH: Pneumonia, Viral ,RNA ,SARS_CoV-2 ,3111 Biomedicine ,MESH: Europe ,Human medicine ,business - Abstract
8 páginas, 3 figuras, We show the distribution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three genomic nomenclature systems to all sequence data from the World Health Organization European Region available until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation, compare the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2., We gratefully acknowledge the authors, originating and submitting laboratories of the sequences from GISAID’s EpiCoV Database used in the phylogenetic analysis. We gratefully acknowledge all the staff working with sample collection, sample preparation, sequencing, data analysis and data sharing in all laboratories in the WHO European Region for making this work possible.
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