1
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Parino F, Gustani-Buss E, Bedford T, Suchard MA, Trovão NS, Rambaut A, Colizza V, Poletto C, Lemey P. Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation. PNAS NEXUS 2025; 4:pgae561. [PMID: 39737444 PMCID: PMC11683419 DOI: 10.1093/pnasnexus/pgae561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 11/21/2024] [Indexed: 01/01/2025]
Abstract
Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological, and virological data, integrating different data sources. We propose a novel-combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic, and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across countries simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales-local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.
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Affiliation(s)
- Francesco Parino
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
| | - Emanuele Gustani-Buss
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven 3000, Belgium
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Howard Hughes Medical Institute, Seattle, WA 98109, USA
| | - Marc A Suchard
- Departments of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA
| | - Nídia S Trovão
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Andrew Rambaut
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, Padova 35121, Italy
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, Leuven 3000, Belgium
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2
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Arias JS. Phylogenetic Biogeography Inference Using Dynamic Paleogeography Models and Explicit Geographic Ranges. Syst Biol 2024; 73:995-1014. [PMID: 39177659 DOI: 10.1093/sysbio/syae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 08/15/2024] [Accepted: 08/20/2024] [Indexed: 08/24/2024] Open
Abstract
To model distribution ranges, the most popular methods of phylogenetic biogeography divide Earth into a handful of predefined areas. Other methods use explicit geographic ranges, but unfortunately, these methods assume a static Earth, ignoring the effects of plate tectonics and the changes in the landscape. To address this limitation, I propose a method that uses explicit geographic ranges and incorporates a plate motion model and a paleolandscape model directly derived from the models used by geologists in their tectonic and paleogeographic reconstructions. The underlying geographic model is a high-resolution pixelation of a spherical Earth. Biogeographic inference is based on diffusion, approximates the effects of the landscape, uses a time-stratified model to take into account the geographic changes, and directly integrates over all probable histories. By using a simplified stochastic mapping algorithm, it is possible to infer the ancestral locations as well as the distance traveled by the ancestral lineages. For illustration, I applied the method to an empirical phylogeny of the Sapindaceae plants. This example shows that methods based on explicit geographic data, coupled with high-resolution paleogeographic models, can provide detailed reconstructions of the ancestral areas but also include inferences about the probable dispersal paths and diffusion speed across the taxon history. The method is implemented in the program PhyGeo.
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Affiliation(s)
- J Salvador Arias
- Unidad Ejecutora Lillo (CONICET-Fundación Miguel Lillo), Miguel Lillo 251, CP 4000, S.M. de Tucumán, Tucumán, Argentina
- Laboratorio de Genética Evolutiva, Instituto de Biología Subtropical (CONICET-UNaM), Facultad de Ciencias Exactas, Químicas y Naturales, Universidad Nacional de Misiones, Felix de Azara 1552, CP 3300, Posadas, Misiones, Argentina
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3
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Li YT, Ko HY, Hughes J, Liu MT, Lin YL, Hampson K, Brunker K. From emergence to endemicity of highly pathogenic H5 avian influenza viruses in Taiwan. Nat Commun 2024; 15:9348. [PMID: 39472594 PMCID: PMC11522503 DOI: 10.1038/s41467-024-53816-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 10/22/2024] [Indexed: 11/02/2024] Open
Abstract
A/goose/Guangdong/1/96-like (GsGd) highly pathogenic avian influenza (HPAI) H5 viruses cause severe outbreaks in poultry when introduced. Since emergence in 1996, control measures in most countries have suppressed local GsGd transmission following introductions, making persistent transmission in domestic birds rare. However, geographical expansion of clade 2.3.4.4 sublineages has raised concern about establishment of endemic circulation, while mechanistic drivers leading to endemicity remain unknown. We reconstructed the evolutionary history of GsGd sublineage, clade 2.3.4.4c, in Taiwan using a time-heterogeneous rate phylogeographic model. During Taiwan's initial epidemic wave (January 2015 - August 2016), we inferred that localised outbreaks had multiple origins from rapid spread between counties/cities nationwide. Subsequently, outbreaks predominantly originated from a single county, Yunlin, where persistent transmission harbours the trunk viruses of the sublineage. Endemic hotspots determined by phylogeographic reconstruction largely predicted the locations of re-emerging outbreaks in Yunlin. The transition to endemicity involved a shift to chicken-dominant circulation, following the initial bidirectional spread between chicken and domestic waterfowl. Our results suggest that following their emergence in Taiwan, source-sink dynamics from a single county have maintained GsGd endemicity up until 2023, pointing to where control efforts should be targeted to eliminate the disease.
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Affiliation(s)
- Yao-Tsun Li
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - Hui-Ying Ko
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Ming-Tsan Liu
- Center for Diagnostics and Vaccine Development, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Yi-Ling Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan
| | - Katie Hampson
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Kirstyn Brunker
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow, UK.
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK.
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4
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Clement AM, Cloutier R, Lee MSY, King B, Vanhaesebroucke O, Bradshaw CJA, Dutel H, Trinajstic K, Long JA. A Late Devonian coelacanth reconfigures actinistian phylogeny, disparity, and evolutionary dynamics. Nat Commun 2024; 15:7529. [PMID: 39266502 PMCID: PMC11392942 DOI: 10.1038/s41467-024-51238-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 08/02/2024] [Indexed: 09/14/2024] Open
Abstract
The living coelacanth Latimeria (Sarcopterygii: Actinistia) is an iconic, so-called 'living fossil' within one of the most apparently morphologically conservative vertebrate groups. We describe a new, 3-D preserved coelacanth from the Late Devonian Gogo Formation in Western Australia. We assemble a comprehensive analysis of the group to assess the phylogeny, evolutionary rates, and morphological disparity of all coelacanths. We reveal a major shift in morphological disparity between Devonian and post-Devonian coelacanths. The newly described fossil fish fills a critical transitional stage in coelacanth disparity and evolution. Since the mid-Cretaceous, discrete character changes (representing major morphological innovations) have essentially ceased, while meristic and continuous characters have continued to evolve within coelacanths. Considering a range of putative environmental drivers, tectonic activity best explains variation in the rates of coelacanth evolution.
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Affiliation(s)
- Alice M Clement
- College of Science and Engineering, Flinders University, Adelaide, 5042, Australia.
| | - Richard Cloutier
- College of Science and Engineering, Flinders University, Adelaide, 5042, Australia
- Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, Rimouski, G5L 3A1, Canada
- Excellence Center in Evolution of Life, Basin Studies and Applied Paleontology,, Palaeontological Research and Education Centre, Mahasarakham University, Maha Sarakham, 44150, Thailand
| | - Michael S Y Lee
- College of Science and Engineering, Flinders University, Adelaide, 5042, Australia
- Earth Sciences Section, South Australian Museum, North Terrace, Adelaide, 5000, Australia
| | - Benedict King
- Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103, Leipzig, Germany
| | - Olivia Vanhaesebroucke
- Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, Rimouski, G5L 3A1, Canada
| | - Corey J A Bradshaw
- Global Ecology | Partuyarta Ngadluku Wardli Kuu, College of Science and Engineering, Flinders University, Adelaide, 5001, Australia
| | - Hugo Dutel
- University of Bristol, School of Earth Sciences, Bristol, UK
| | - Kate Trinajstic
- School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, Perth, 6845, Australia
- Earth and Planetary Sciences, Western Australian Museum, 49 Kew Street, Welshpool, 6106, Australia
| | - John A Long
- College of Science and Engineering, Flinders University, Adelaide, 5042, Australia
- Earth and Planetary Sciences, Western Australian Museum, 49 Kew Street, Welshpool, 6106, Australia
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5
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Magee AF, Holbrook AJ, Pekar JE, Caviedes-Solis IW, Matsen IV FA, Baele G, Wertheim JO, Ji X, Lemey P, Suchard MA. Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations. Syst Biol 2024; 73:562-578. [PMID: 38712512 PMCID: PMC11498053 DOI: 10.1093/sysbio/syae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 02/26/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024] Open
Abstract
Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.
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Affiliation(s)
- Andrew F Magee
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California - Los Angeles, Los Angeles, CA, USA
| | - Andrew J Holbrook
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California - Los Angeles, Los Angeles, CA, USA
| | - Jonathan E Pekar
- Bioinformatics and Systems Biology Graduate Program, University of California - San Diego, La Jolla, CA, USA
- Department of Biomedical Informatics, University of California - San Diega, La Jolla, CA, USA
| | | | - Fredrick A Matsen IV
- Howard Hughes Medical Institute, Seattle, Washington, USA
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
- Department of Statistics, University of Washington, Seattle, Washington, USA
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Joel O Wertheim
- Department of Medicine, University of California - San Diego, La Jolla, CA, USA
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, LA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California - Los Angeles, Los Angeles, CA, USA
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California - Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California - Los Angeles, Los Angeles, CA, USA
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6
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Langedijk AC, Vrancken B, Lebbink RJ, Wilkins D, Kelly EJ, Baraldi E, Mascareñas de Los Santos AH, Danilenko DM, Choi EH, Palomino MA, Chi H, Keller C, Cohen R, Papenburg J, Pernica J, Greenough A, Richmond P, Martinón-Torres F, Heikkinen T, Stein RT, Hosoya M, Nunes MC, Verwey C, Evers A, Kragten-Tabatabaie L, Suchard MA, Kosakovsky Pond SL, Poletto C, Colizza V, Lemey P, Bont LJ. The genomic evolutionary dynamics and global circulation patterns of respiratory syncytial virus. Nat Commun 2024; 15:3083. [PMID: 38600104 PMCID: PMC11006891 DOI: 10.1038/s41467-024-47118-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 03/14/2024] [Indexed: 04/12/2024] Open
Abstract
Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection in young children and the second leading cause of infant death worldwide. While global circulation has been extensively studied for respiratory viruses such as seasonal influenza, and more recently also in great detail for SARS-CoV-2, a lack of global multi-annual sampling of complete RSV genomes limits our understanding of RSV molecular epidemiology. Here, we capitalise on the genomic surveillance by the INFORM-RSV study and apply phylodynamic approaches to uncover how selection and neutral epidemiological processes shape RSV diversity. Using complete viral genome sequences, we show similar patterns of site-specific diversifying selection among RSVA and RSVB and recover the imprint of non-neutral epidemic processes on their genealogies. Using a phylogeographic approach, we provide evidence for air travel governing the global patterns of RSVA and RSVB spread, which results in a considerable degree of phylogenetic mixing across countries. Our findings highlight the potential of systematic global RSV genomic surveillance for transforming our understanding of global RSV spread.
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Affiliation(s)
- Annefleur C Langedijk
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Lundlaan 6, 3584 EA, Utrecht, the Netherlands
| | - Bram Vrancken
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical and Epidemiological Virology, Herestraat 49, 3000, Leuven, Belgium
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
| | - Robert Jan Lebbink
- Department of Medical Microbiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Deidre Wilkins
- Translational Medicine, Vaccines & Immune Therapies, BioPharmaceuticals R&D, AstraZeneca, 1 MedImmune Way, Gaithersburg, MD, USA
| | - Elizabeth J Kelly
- Translational Medicine, Vaccines & Immune Therapies, BioPharmaceuticals R&D, AstraZeneca, 1 MedImmune Way, Gaithersburg, MD, USA
| | - Eugenio Baraldi
- Department of Woman's and Child's Health, University Hospital of Padova, Padova, Italy
- ReSViNET Foundation, Zeist, the Netherlands
- Institute of Pediatric Research "Città della Speranza", Padova, Italy
| | | | - Daria M Danilenko
- Smorodintsev Research Institute of Influenza, St. Petersburg, Russia
| | - Eun Hwa Choi
- Seoul National University Children's Hospital, Seoul, South Korea
| | | | - Hsin Chi
- MacKay Children's Hospital, New Taipei, Taiwan, ROC
| | - Christian Keller
- Institute of Virology, University Hospital Giessen and Marburg, Marburg, Germany
| | | | | | | | - Anne Greenough
- ReSViNET Foundation, Zeist, the Netherlands
- King's College London, London, UK
| | | | - Federico Martinón-Torres
- ReSViNET Foundation, Zeist, the Netherlands
- Hospital Clínico Universitario de Santiago, Galicia, Spain
| | - Terho Heikkinen
- ReSViNET Foundation, Zeist, the Netherlands
- University of Turku and Turku University Hospital, Turku, Finland
| | - Renato T Stein
- ReSViNET Foundation, Zeist, the Netherlands
- Pontificia Universidade Catolica de Rio Grande do Sul, Porto Alegre, Brazil
| | - Mitsuaki Hosoya
- Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Marta C Nunes
- ReSViNET Foundation, Zeist, the Netherlands
- Department of Paediatrics and Child Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- South African Medical Research Council, Vaccines & Infectious Diseases Analytics Research Unit, and Department of Science and Technology/National Research Foundation, South African Research Chair Initiative in Vaccine Preventable Diseases, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Charl Verwey
- Department of Paediatrics and Child Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Hospices Civils de Lyon and the Centre International de Recherche en Infectiologie (CIRI) Inserm U1111, CNRS UMR5308, ENS de Lyon, UCBL1, Lyon, France
| | - Anouk Evers
- Department of Medical Microbiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | | | - Marc A Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Sergei L Kosakovsky Pond
- Institute for Genomics and Evolutionary Medicine, Department of Biology, Temple University, 801 N Broad St, Philadelphia, PA, 19122, USA
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical and Epidemiological Virology, Herestraat 49, 3000, Leuven, Belgium
| | - Louis J Bont
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Centre Utrecht, Lundlaan 6, 3584 EA, Utrecht, the Netherlands.
- ReSViNET Foundation, Zeist, the Netherlands.
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7
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Parino F, Gustani-Buss E, Bedford T, Suchard MA, Trovão NS, Rambaut A, Colizza V, Poletto C, Lemey P. Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.14.24303719. [PMID: 38559244 PMCID: PMC10980132 DOI: 10.1101/2024.03.14.24303719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.
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Affiliation(s)
- Francesco Parino
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
| | - Emanuele Gustani-Buss
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, 3000 Leuven, Belgium
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA
- Howard Hughes Medical Institute, Seattle, Washington 98109, USA
| | - Marc A. Suchard
- Departments of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, 90095, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | | | - Andrew Rambaut
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121 Padova, Italy
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, 3000 Leuven, Belgium
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8
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Gräf T, Martinez AA, Bello G, Dellicour S, Lemey P, Colizza V, Mazzoli M, Poletto C, Cardoso VLO, da Silva AF, Motta FC, Resende PC, Siqueira MM, Franco L, Gresh L, Gabastou JM, Rodriguez A, Vicari A, Aldighieri S, Mendez-Rico J, Leite JA. Dispersion patterns of SARS-CoV-2 variants Gamma, Lambda and Mu in Latin America and the Caribbean. Nat Commun 2024; 15:1837. [PMID: 38418815 PMCID: PMC10902334 DOI: 10.1038/s41467-024-46143-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Latin America and Caribbean (LAC) regions were an important epicenter of the COVID-19 pandemic and SARS-CoV-2 evolution. Through the COVID-19 Genomic Surveillance Regional Network (COVIGEN), LAC countries produced an important number of genomic sequencing data that made possible an enhanced SARS-CoV-2 genomic surveillance capacity in the Americas, paving the way for characterization of emerging variants and helping to guide the public health response. In this study we analyzed approximately 300,000 SARS-CoV-2 sequences generated between February 2020 and March 2022 by multiple genomic surveillance efforts in LAC and reconstructed the diffusion patterns of the main variants of concern (VOCs) and of interest (VOIs) possibly originated in the Region. Our phylogenetic analysis revealed that the spread of variants Gamma, Lambda and Mu reflects human mobility patterns due to variations of international air passenger transportation and gradual lifting of social distance measures previously implemented in countries. Our results highlight the potential of genetic data to reconstruct viral spread and unveil preferential routes of viral migrations that are shaped by human mobility patterns.
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Affiliation(s)
- Tiago Gräf
- Laboratório de Virologia Molecular, Instituto Carlos Chagas, Fundação Oswaldo Cruz, Curitiba, Brazil.
| | - Alexander A Martinez
- Gorgas Memorial Institute for Health Studies, Panama City, Panama
- National Research System (SNI), National Secretary of Research, Technology and Innovation (SENACYT), Panama City, Panama
- Department of Microbiology and Immunology, University of Panama, Panama City, Panama
| | - Gonzalo Bello
- Laboratório de Arbovírus e Vírus Hemorrágicos, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 av. FD Roosevelt, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, University of Leuven, Leuven, Belgium
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Mattia Mazzoli
- Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121, Padova, Italy
| | - Vanessa Leiko Oikawa Cardoso
- Laboratório de Enfermidades Infecciosas Transmitidas por Vetores, Instituto Gonçalo Moniz, FIOCRUZ-Bahia, Salvador, Brazil
| | | | - Fernando Couto Motta
- Laboratório de Vírus Respiratórios, Exantemáticos, Enterovírus e Emergências Virais, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Paola Cristina Resende
- Laboratório de Vírus Respiratórios, Exantemáticos, Enterovírus e Emergências Virais, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Marilda M Siqueira
- Laboratório de Vírus Respiratórios, Exantemáticos, Enterovírus e Emergências Virais, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Leticia Franco
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA
| | - Lionel Gresh
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA
| | - Jean-Marc Gabastou
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA
| | - Angel Rodriguez
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA
| | - Andrea Vicari
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA
| | - Sylvain Aldighieri
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA
| | - Jairo Mendez-Rico
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA
| | - Juliana Almeida Leite
- Infectious Hazards Management Unit, Health Emergencies Department, Pan American Health Organization, Washington D.C., USA.
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9
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Yang Q, Wang B, Lemey P, Dong L, Mu T, Wiebe RA, Guo F, Trovão NS, Park SW, Lewis N, Tsui JLH, Bajaj S, Cheng Y, Yang L, Haba Y, Li B, Zhang G, Pybus OG, Tian H, Grenfell B. Synchrony of Bird Migration with Global Dispersal of Avian Influenza Reveals Exposed Bird Orders. Nat Commun 2024; 15:1126. [PMID: 38321046 PMCID: PMC10847442 DOI: 10.1038/s41467-024-45462-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024] Open
Abstract
Highly pathogenic avian influenza virus (HPAIV) A H5, particularly clade 2.3.4.4, has caused worldwide outbreaks in domestic poultry, occasional spillover to humans, and increasing deaths of diverse species of wild birds since 2014. Wild bird migration is currently acknowledged as an important ecological process contributing to the global dispersal of HPAIV H5. However, this mechanism has not been quantified using bird movement data from different species, and the timing and location of exposure of different species is unclear. We sought to explore these questions through phylodynamic analyses based on empirical data of bird movement tracking and virus genome sequences of clade 2.3.4.4 and 2.3.2.1. First, we demonstrate that seasonal bird migration can explain salient features of the global dispersal of clade 2.3.4.4. Second, we detect synchrony between the seasonality of bird annual cycle phases and virus lineage movements. We reveal the differing exposed bird orders at geographical origins and destinations of HPAIV H5 clade 2.3.4.4 lineage movements, including relatively under-discussed orders. Our study provides a phylodynamic framework that links the bird movement ecology and genomic epidemiology of avian influenza; it highlights the importance of integrating bird behavior and life history in avian influenza studies.
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Affiliation(s)
- Qiqi Yang
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
| | - Ben Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Phillipe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Lu Dong
- College of Life Sciences, Beijing Normal University, Beijing, China
| | - Tong Mu
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - R Alex Wiebe
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Fengyi Guo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Nicola Lewis
- Animal and Plant Health Agency-Weybridge, OIE/FAO International Reference Laboratory for Avian Influenza, Swine Influenza and Newcastle Disease Virus, Department of Virology, Addlestone, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | | | - Sumali Bajaj
- Department of Biology, University of Oxford, Oxford, UK
| | - Yachang Cheng
- State Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Luojun Yang
- Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA
| | - Yuki Haba
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Bingying Li
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Guogang Zhang
- Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, National Bird Banding Center of China, Beijing, China
| | - Oliver G Pybus
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
- Department of Biology, University of Oxford, Oxford, UK
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
| | - Bryan Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA.
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10
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Goldberg EE, Lundgren EJ, Romero-Severson EO, Leitner T. Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs. Mol Biol Evol 2024; 41:msad282. [PMID: 38149995 PMCID: PMC10776241 DOI: 10.1093/molbev/msad282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 12/28/2023] Open
Abstract
When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.
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Affiliation(s)
- Emma E Goldberg
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Erik J Lundgren
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | | | - Thomas Leitner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
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11
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Guimarães Fabreti L, Höhna S. Nucleotide Substitution Model Selection Is Not Necessary for Bayesian Inference of Phylogeny With Well-Behaved Priors. Syst Biol 2023; 72:1418-1432. [PMID: 37455495 DOI: 10.1093/sysbio/syad041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/02/2023] [Accepted: 07/02/2023] [Indexed: 07/18/2023] Open
Abstract
Model selection aims to choose the most adequate model for the statistical analysis at hand. The model must be complex enough to capture the complexity of the data but should be simple enough not to overfit. In phylogenetics, the most common model selection scenario concerns selecting an adequate substitution and partition model for sequence evolution to infer a phylogenetic tree. Previously, several studies showed that substitution model under-parameterization can bias phylogenetic studies. Here, we explored the impact of substitution model over-parameterization in a Bayesian statistical framework. We performed simulations under the simplest substitution model, the Jukes-Cantor model, and compare posterior estimates of phylogenetic tree topologies and tree length under the true model to the most complex model, the $\text{GTR}+\Gamma+\text{I}$ substitution model, including over-splitting the data into additional subsets (i.e., applying partitioned models). We explored 4 choices of prior distributions: the default substitution model priors of MrBayes, BEAST2, and RevBayes and a newly devised prior choice (Tame). Our results show that Bayesian inference of phylogeny is robust to substitution model over-parameterization and over-partitioning but only under our new prior settings. All 3 current default priors introduced biases for the estimated tree length. We conclude that substitution and partition model selection are superfluous steps in Bayesian phylogenetic inference pipelines if well-behaved prior distributions are applied and more effort should focus on more complex and biologically realistic substitution models.
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Affiliation(s)
- Luiza Guimarães Fabreti
- GeoBio-Center, Ludwig-Maximilians-Universität München, 80333 Munich, Germany
- Department of Earth and Environmental Sciences, Paleontology & Geobiology, Ludwig-Maximilians-Universität München, 80333 Munich, Germany
| | - Sebastian Höhna
- GeoBio-Center, Ludwig-Maximilians-Universität München, 80333 Munich, Germany
- Department of Earth and Environmental Sciences, Paleontology & Geobiology, Ludwig-Maximilians-Universität München, 80333 Munich, Germany
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12
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Goldberg EE, Lundgren EJ, Romero-Severson EO, Leitner T. Inferring viral transmission time from phylogenies for known transmission pairs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557404. [PMID: 37745490 PMCID: PMC10515827 DOI: 10.1101/2023.09.12.557404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously-described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time-calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.
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Affiliation(s)
- Emma E. Goldberg
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos NM, USA
| | - Erik J. Lundgren
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos NM, USA
| | | | - Thomas Leitner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos NM, USA
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13
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Ramirez-Mata AS, Ostrov D, Salemi M, Marini S, Magalis BR. Machine Learning Prediction and Phyloanatomic Modeling of Viral Neuroadaptive Signatures in the Macaque Model of HIV-Mediated Neuropathology. Microbiol Spectr 2023; 11:e0308622. [PMID: 36847516 PMCID: PMC10100676 DOI: 10.1128/spectrum.03086-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/06/2023] [Indexed: 03/01/2023] Open
Abstract
In human immunodeficiency virus (HIV) infection, virus replication in and adaptation to the central nervous system (CNS) can result in neurocognitive deficits in approximately 25% of patients with unsuppressed viremia. While no single viral mutation can be agreed upon as distinguishing the neuroadapted population, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the virus envelope glycoprotein (Gp120) predictive of disease. The S[imian]IV-infected macaque is a widely used animal model of HIV neuropathology, allowing in-depth tissue sampling infeasible for human patients. Yet, translational impact of the ML approach within the context of the macaque model has not been tested, much less the capacity for early prediction in other, noninvasive tissues. We applied the previously described ML approach to prediction of SIV-mediated encephalitis (SIVE) using gp120 sequences obtained from the CNS of animals with and without SIVE with 97% accuracy. The presence of SIVE signatures at earlier time points of infection in non-CNS tissues indicated these signatures cannot be used in a clinical setting; however, combined with protein structural mapping and statistical phylogenetic inference, results revealed common denominators associated with these signatures, including 2-acetamido-2-deoxy-beta-d-glucopyranose structural interactions and high rate of alveolar macrophage (AM) infection. AMs were also determined to be the phyloanatomic source of cranial virus in SIVE animals, but not in animals that did not develop SIVE, implicating a role for these cells in the evolution of the signatures identified as predictive of both HIV and SIV neuropathology. IMPORTANCE HIV-associated neurocognitive disorders remain prevalent among persons living with HIV (PLWH) owing to our limited understanding of the contributing viral mechanisms and ability to predict disease onset. We have expanded on a machine learning method previously used on HIV genetic sequence data to predict neurocognitive impairment in PLWH to the more extensively sampled SIV-infected macaque model in order to (i) determine the translatability of the animal model and (ii) more accurately characterize the predictive capacity of the method. We identified eight amino acid and/or biochemical signatures in the SIV envelope glycoprotein, the most predominant of which demonstrated the potential for aminoglycan interaction characteristic of previously identified HIV signatures. These signatures were not isolated to specific points in time or to the central nervous system, limiting their use as an accurate clinical predictor of neuropathogenesis; however, statistical phylogenetic and signature pattern analyses implicate the lungs as a key player in the emergence of neuroadapted viruses.
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Affiliation(s)
- Andrea S. Ramirez-Mata
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - David Ostrov
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Marco Salemi
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Simone Marini
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
- Department of Epidemiology, University of Florida, Gainesville, Florida, USA
| | - Brittany Rife Magalis
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
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14
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Zhang G, Li B, Raghwani J, Vrancken B, Jia R, Hill SC, Fournié G, Cheng Y, Yang Q, Wang Y, Wang Z, Dong L, Pybus OG, Tian H. Bidirectional Movement of Emerging H5N8 Avian Influenza Viruses Between Europe and Asia via Migratory Birds Since Early 2020. Mol Biol Evol 2023; 40:msad019. [PMID: 36703230 PMCID: PMC9922686 DOI: 10.1093/molbev/msad019] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/22/2022] [Accepted: 11/28/2022] [Indexed: 01/28/2023] Open
Abstract
Migratory birds play a critical role in the rapid spread of highly pathogenic avian influenza (HPAI) H5N8 virus clade 2.3.4.4 across Eurasia. Elucidating the timing and pattern of virus transmission is essential therefore for understanding the spatial dissemination of these viruses. In this study, we surveyed >27,000 wild birds in China, tracked the year-round migration patterns of 20 bird species across China since 2006, and generated new HPAI H5N8 virus genomic data. Using this new data set, we investigated the seasonal transmission dynamics of HPAI H5N8 viruses across Eurasia. We found that introductions of HPAI H5N8 viruses to different Eurasian regions were associated with the seasonal migration of wild birds. Moreover, we report a backflow of HPAI H5N8 virus lineages from Europe to Asia, suggesting that Europe acts as both a source and a sink in the global HPAI virus transmission network.
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Affiliation(s)
- Guogang Zhang
- Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, National Bird Banding Center of China, Beijing, China
| | - Bingying Li
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Jayna Raghwani
- Department of Biology, University of Oxford, Oxford, United Kingdom
- Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, United Kingdom
| | - Bram Vrancken
- Department of Microbiology and Immunology, Rega Institute, Laboratory of Evolutionary and Computational Virology, KU Leuven, Leuven, Belgium
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
| | - Ru Jia
- Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, National Bird Banding Center of China, Beijing, China
| | - Sarah C Hill
- Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, United Kingdom
| | - Guillaume Fournié
- Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, United Kingdom
| | - Yanchao Cheng
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Qiqi Yang
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Yuxin Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Lu Dong
- Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China
| | - Oliver G Pybus
- Department of Biology, University of Oxford, Oxford, United Kingdom
- Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, United Kingdom
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
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15
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Salis AT, Bray SCE, Lee MSY, Heiniger H, Barnett R, Burns JA, Doronichev V, Fedje D, Golovanova L, Harington CR, Hockett B, Kosintsev P, Lai X, Mackie Q, Vasiliev S, Weinstock J, Yamaguchi N, Meachen JA, Cooper A, Mitchell KJ. Lions and brown bears colonized North America in multiple synchronous waves of dispersal across the Bering Land Bridge. Mol Ecol 2022; 31:6407-6421. [PMID: 34748674 DOI: 10.1111/mec.16267] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 10/15/2021] [Accepted: 10/25/2021] [Indexed: 01/13/2023]
Abstract
The Bering Land Bridge connecting North America and Eurasia was periodically exposed and inundated by oscillating sea levels during the Pleistocene glacial cycles. This land connection allowed the intermittent dispersal of animals, including humans, between Western Beringia (far northeast Asia) and Eastern Beringia (northwest North America), changing the faunal community composition of both continents. The Pleistocene glacial cycles also had profound impacts on temperature, precipitation and vegetation, impacting faunal community structure and demography. While these palaeoenvironmental impacts have been studied in many large herbivores from Beringia (e.g., bison, mammoths, horses), the Pleistocene population dynamics of the diverse guild of carnivorans present in the region are less well understood, due to their lower abundances. In this study, we analyse mitochondrial genome data from ancient brown bears (Ursus arctos; n = 103) and lions (Panthera spp.; n = 39), two megafaunal carnivorans that dispersed into North America during the Pleistocene. Our results reveal striking synchronicity in the population dynamics of Beringian lions and brown bears, with multiple waves of dispersal across the Bering Land Bridge coinciding with glacial periods of low sea levels, as well as synchronous local extinctions in Eastern Beringia during Marine Isotope Stage 3. The evolutionary histories of these two taxa underline the crucial biogeographical role of the Bering Land Bridge in the distribution, turnover and maintenance of megafaunal populations in North America.
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Affiliation(s)
- Alexander T Salis
- Australian Centre for Ancient DNA (ACAD), School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.,Division of Vertebrate Zoology, American Museum of Natural History, New York, New York, USA
| | - Sarah C E Bray
- Australian Centre for Ancient DNA (ACAD), School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.,Registry of Senior Australians (ROSA), South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Michael S Y Lee
- College of Science and Engineering, Flinders University, Bedford Park, South Australia, Australia.,South Australian Museum, Adelaide, South Australia, Australia
| | - Holly Heiniger
- Australian Centre for Ancient DNA (ACAD), School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Ross Barnett
- Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
| | - James A Burns
- Curator Emeritus, Royal Alberta Museum, Edmonton, Alberta, Canada
| | | | - Daryl Fedje
- Department of Anthropology, University of Victoria, Victoria, B.C, Canada
| | | | - C Richard Harington
- Curator Emeritus and Research Associate, Research Division (Paleobiology), Canadian Museum of Nature, Ottawa, Canada
| | - Bryan Hockett
- US Department of Interior, Bureau of Land Management, Nevada State Office, Reno, Nevada, USA
| | - Pavel Kosintsev
- Institute of Plant and Animal Ecology, Ural Branch of the Russian Academy of Sciences, Yekaterinburg, Russia.,Department of History, Ural Federal University, Yekaterinburg, Russia
| | - Xulong Lai
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, Hubei, China
| | - Quentin Mackie
- Department of Anthropology, University of Victoria, Victoria, B.C, Canada
| | - Sergei Vasiliev
- Institute of Archaeology and Ethnography, Russian Academy of Sciences, Russia
| | - Jacobo Weinstock
- Faculty of Humanities (Archaeology), University of Southampton, UK
| | - Nobuyuki Yamaguchi
- Institute of Tropical Biodiversity and Sustainable Development, University Malaysia Terengganu, Kuala Nerus, Malaysia
| | - Julie A Meachen
- Anatomy Department, Des Moines University, Des Moines, Iowa, USA
| | - Alan Cooper
- South Australian Museum, Adelaide, South Australia, Australia
| | - Kieren J Mitchell
- Australian Centre for Ancient DNA (ACAD), School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia.,Department of Zoology, Otago Palaeogenetics Laboratory, University of Otago, Dunedin, New Zealand
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16
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Rahman S, Hasan M, Alam MS, Uddin KMM, Moni S, Rahman M. The evolutionary footprint of influenza A subtype H3N2 strains in Bangladesh: implication of vaccine strain selection. Sci Rep 2022; 12:16186. [PMID: 36171388 PMCID: PMC9519982 DOI: 10.1038/s41598-022-20179-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
In February each year, World Health Organization (WHO) recommends candidate vaccine viruses for the forthcoming northern hemisphere (NH) season; however, the influenza season in the temperate zone of NH begins in October. During egg- or cell culture-propagation, the vaccine viruses become too old to confer the highest match with the latest strains, impacting vaccine effectiveness. Therefore, an alternative strategy like mRNA-based vaccine using the most recent strains should be considered. We analyzed influenza A subtype H3N2 strains circulating in NH during the last 10 years (2009-2020). Phylogenetic analysis revealed multiple clades of influenza strains circulating every season, which had substantial mismatches with WHO-recommended vaccine strains. The clustering pattern suggests that influenza A subtype H3N2 strains are not fixed to the specific geographical region but circulate globally in the same season. By analyzing 39 seasons from eight NH countries with the highest vaccine coverage, we also provide evidence that the influenza A, subtype H3N2 strains from South and Southeast Asia, including Bangladesh, had the highest genetic proximity to the NH strains. Furthermore, insilico analysis showed minimal effect on the Bangladeshi HA protein structure, indicating the stability of Bangladeshi strains. Therefore, we propose that Bangladeshi influenza strains represent genetic makeup that may better fit and serve as the most suitable candidate vaccine viruses for the forthcoming NH season.
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Affiliation(s)
- Sezanur Rahman
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Mehedi Hasan
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Md Shaheen Alam
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - K M Main Uddin
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Sayra Moni
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh
| | - Mustafizur Rahman
- Virology Laboratory, Infectious Diseases Division, icddr,b, Mohakhali, 68 Shaheed Tajuddin Ahmed Sarani, Dhaka, 1212, Bangladesh.
- Genomics Centre, icddr,b, Mohakhali, Dhaka, 1212, Bangladesh.
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17
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Gao J, May MR, Rannala B, Moore BR. New Phylogenetic Models Incorporating Interval-Specific Dispersal Dynamics Improve Inference of Disease Spread. Mol Biol Evol 2022; 39:msac159. [PMID: 35861314 PMCID: PMC9384482 DOI: 10.1093/molbev/msac159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Phylodynamic methods reveal the spatial and temporal dynamics of viral geographic spread, and have featured prominently in studies of the COVID-19 pandemic. Virtually all such studies are based on phylodynamic models that assume-despite direct and compelling evidence to the contrary-that rates of viral geographic dispersal are constant through time. Here, we: (1) extend phylodynamic models to allow both the average and relative rates of viral dispersal to vary independently between pre-specified time intervals; (2) implement methods to infer the number and timing of viral dispersal events between areas; and (3) develop statistics to assess the absolute fit of discrete-geographic phylodynamic models to empirical datasets. We first validate our new methods using simulations, and then apply them to a SARS-CoV-2 dataset from the early phase of the COVID-19 pandemic. We show that: (1) under simulation, failure to accommodate interval-specific variation in the study data will severely bias parameter estimates; (2) in practice, our interval-specific discrete-geographic phylodynamic models can significantly improve the relative and absolute fit to empirical data; and (3) the increased realism of our interval-specific models provides qualitatively different inferences regarding key aspects of the COVID-19 pandemic-revealing significant temporal variation in global viral dispersal rates, viral dispersal routes, and the number of viral dispersal events between areas-and alters interpretations regarding the efficacy of intervention measures to mitigate the pandemic.
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Affiliation(s)
- Jiansi Gao
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
| | - Michael R May
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
- Department of Integrative Biology, University of California, 3060 VLSB, Berkeley, CA 94720-3140, USA
| | - Bruce Rannala
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
| | - Brian R Moore
- Department of Evolution and Ecology, University of California, Storer Hall, Davis, CA 95616, USA
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18
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Cross-reactive immunity potentially drives global oscillation and opposed alternation patterns of seasonal influenza A viruses. Sci Rep 2022; 12:8883. [PMID: 35614123 PMCID: PMC9131982 DOI: 10.1038/s41598-022-08233-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 03/02/2022] [Indexed: 11/08/2022] Open
Abstract
Several human pathogens exhibit distinct patterns of seasonality and circulate as pairs. For instance, influenza A virus subtypes oscillate and peak during winter seasons of the world’s temperate climate zones. Alternation of dominant strains in successive influenza seasons makes epidemic forecasting a major challenge. From the start of the 2009 influenza pandemic we enrolled influenza A virus infected patients (n = 2980) in a global prospective clinical study. Complete hemagglutinin sequences were obtained from 1078 A/H1N1 and 1033 A/H3N2 viruses. We used phylodynamics to construct high resolution spatio-temporal phylogenetic hemagglutinin trees and estimated global influenza A effective reproductive numbers (R) over time (2009–2013). We demonstrate that R oscillates around R = 1 with a clear opposed alternation pattern between phases of the A/H1N1 and A/H3N2 subtypes. Moreover, we find a similar alternation pattern for the number of global viral spread between the sampled geographical locations. Both observations suggest a between-strain competition for susceptible hosts on a global level. Extrinsic factors that affect person-to-person transmission are a major driver of influenza seasonality. The data presented here indicate that cross-reactive host immunity is also a key intrinsic driver of influenza seasonality, which determines the influenza A virus strain at the onset of each epidemic season.
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19
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Dellicour S, Lemey P, Suchard MA, Gilbert M, Baele G. Accommodating sampling location uncertainty in continuous phylogeography. Virus Evol 2022; 8:veac041. [PMID: 35795297 PMCID: PMC9248920 DOI: 10.1093/ve/veac041] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/22/2022] [Accepted: 05/18/2022] [Indexed: 02/01/2023] Open
Abstract
Phylogeographic inference of the dispersal history of viral lineages offers key opportunities to tackle epidemiological questions about the spread of fast-evolving pathogens across human, animal and plant populations. In continuous space, i.e. when locations are specified by longitude and latitude, these reconstructions are however often limited by the availability or accessibility of precise sampling locations required for such spatially explicit analyses. We here review the different approaches that can be considered when genomic sequences are associated with a geographic area of sampling instead of precise coordinates. In particular, we describe and compare the approaches to define homogeneous and heterogeneous prior ranges of sampling coordinates.
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Affiliation(s)
- Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 av. FD Roosevelt, Bruxelles 1050, Belgium
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, and Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095-1766, USA
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 av. FD Roosevelt, Bruxelles 1050, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Herestraat 49, Leuven 3000, Belgium
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20
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Cornuault J, Sanmartín I. A road map for phylogenetic models of species trees. Mol Phylogenet Evol 2022; 173:107483. [DOI: 10.1016/j.ympev.2022.107483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/09/2022] [Accepted: 04/05/2022] [Indexed: 10/18/2022]
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21
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He WT, Bollen N, Xu Y, Zhao J, Dellicour S, Yan Z, Gong W, Zhang C, Zhang L, Lu M, Lai A, Suchard MA, Ji X, Tu C, Lemey P, Baele G, Su S. Phylogeography reveals association between swine trade and the spread of porcine epidemic diarrhea virus in China and across the world. Mol Biol Evol 2021; 39:6482749. [PMID: 34951645 PMCID: PMC8826572 DOI: 10.1093/molbev/msab364] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The ongoing SARS (severe acute respiratory syndrome)-CoV (coronavirus)-2 pandemic has exposed major gaps in our knowledge on the origin, ecology, evolution, and spread of animal coronaviruses. Porcine epidemic diarrhea virus (PEDV) is a member of the genus Alphacoronavirus in the family Coronaviridae that may have originated from bats and leads to significant hazards and widespread epidemics in the swine population. The role of local and global trade of live swine and swine-related products in disseminating PEDV remains unclear, especially in developing countries with complex swine production systems. Here, we undertake an in-depth phylogeographic analysis of PEDV sequence data (including 247 newly sequenced samples) and employ an extension of this inference framework that enables formally testing the contribution of a range of predictor variables to the geographic spread of PEDV. Within China, the provinces of Guangdong and Henan were identified as primary hubs for the spread of PEDV, for which we estimate live swine trade to play a very important role. On a global scale, the United States and China maintain the highest number of PEDV lineages. We estimate that, after an initial introduction out of China, the United States acted as an important source of PEDV introductions into Japan, Korea, China, and Mexico. Live swine trade also explains the dispersal of PEDV on a global scale. Given the increasingly global trade of live swine, our findings have important implications for designing prevention and containment measures to combat a wide range of livestock coronaviruses.
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Affiliation(s)
- Wan-Ting He
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, China Nanjing
| | - Nena Bollen
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Belgium Leuven
| | - Yi Xu
- China animal disease control center, Ministry of Agriculture, China Beijing
| | - Jin Zhao
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, China Nanjing
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Belgium Leuven.,Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Belgium CP160/12 50, av. FD Roosevelt, 1050 Bruxelles
| | - Ziqing Yan
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, China Nanjing
| | - Wenjie Gong
- Key Laboratory of Jilin Province for Zoonosis Prevention and Control, Institute of Military Veterinary, Academy of Military Medical Sciences, China Changchun, Jilin
| | - Cheng Zhang
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, China Nanjing
| | - Letian Zhang
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, China Nanjing
| | - Meng Lu
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, China Nanjing
| | - Alexander Lai
- School of Science, Technology, Engineering, and Mathematics, Kentucky State University, United States Frankfort, Kentucky
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, and Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California Los Angeles Los Angeles, CA
| | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University New Orleans, LA
| | - Changchun Tu
- Key Laboratory of Jilin Province for Zoonosis Prevention and Control, Institute of Military Veterinary, Academy of Military Medical Sciences, China Changchun, Jilin
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Belgium Leuven
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Belgium Leuven
| | - Shuo Su
- Jiangsu Engineering Laboratory of Animal Immunology, Institute of Immunology, College of Veterinary Medicine, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, China Nanjing
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22
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Hackel J, Sanmartín I. Modelling the tempo and mode of lineage dispersal. Trends Ecol Evol 2021; 36:1102-1112. [PMID: 34462154 DOI: 10.1016/j.tree.2021.07.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 07/09/2021] [Accepted: 07/19/2021] [Indexed: 11/16/2022]
Abstract
Lineage dispersal is a basic macroevolutionary process shaping the distribution of biodiversity. Probabilistic approaches in biogeography, epidemiology, and macroecology often model dispersal as a background process to explain extant or infer past distributions. We propose framing questions around the mode, timing, rate, and direction of lineage dispersal itself, from a lineage- or geography-centric perspective. We review available methods for modelling lineage dispersal. Likelihood- and simulation-based approaches to modelling dispersal have made progress in accounting for the variation of lineage dispersal over space, time, and branches of a phylogeny and its interaction with diversification. Methodological improvements, guided by a focus on model adequacy, will lead to more realistic models that can answer fundamental questions about the tempo and mode of lineage dispersal.
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Affiliation(s)
- Jan Hackel
- Comparative Plant and Fungal Biology, Royal Botanic Gardens, Kew, Richmond, UK.
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23
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Lemey P, Ruktanonchai N, Hong SL, Colizza V, Poletto C, Van den Broeck F, Gill MS, Ji X, Levasseur A, Oude Munnink BB, Koopmans M, Sadilek A, Lai S, Tatem AJ, Baele G, Suchard MA, Dellicour S. Untangling introductions and persistence in COVID-19 resurgence in Europe. Nature 2021; 595:713-717. [PMID: 34192736 PMCID: PMC8324533 DOI: 10.1038/s41586-021-03754-2] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/22/2021] [Indexed: 11/09/2022]
Abstract
After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain1. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave2. Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden.
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Affiliation(s)
- Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
- Global Virus Network (GVN), Baltimore, MD, USA.
| | - Nick Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Population Health Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Samuel L Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Paris, France
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Paris, France
| | - Frederik Van den Broeck
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
- Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
| | - Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, New Orleans, LA, USA
| | - Anthony Levasseur
- UMR MEPHI (Microbes, Evolution, Phylogeny and Infections), Aix-Marseille Université (AMU) and Institut Universitaire de France (IUF), Marseille, France
| | - Bas B Oude Munnink
- Department of Viroscience, WHO Collaborating Centre for Arbovirus and Viral Hemorrhagic Fever Reference and Research, Erasmus MC, Rotterdam, The Netherlands
| | - Marion Koopmans
- Department of Viroscience, WHO Collaborating Centre for Arbovirus and Viral Hemorrhagic Fever Reference and Research, Erasmus MC, Rotterdam, The Netherlands
| | | | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium.
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24
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Lemey P, Ruktanonchai N, Hong SL, Colizza V, Poletto C, Van den Broeck F, Gill MS, Ji X, Levasseur A, Sadilek A, Lai S, Tatem AJ, Baele G, Suchard MA, Dellicour S. SARS-CoV-2 European resurgence foretold: interplay of introductions and persistence by leveraging genomic and mobility data. RESEARCH SQUARE 2021:rs.3.rs-208849. [PMID: 33594355 PMCID: PMC7885927 DOI: 10.21203/rs.3.rs-208849/v1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Following the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting late summer that was deadlier and more difficult to contain. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave. Here, we build a phylogeographic model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the COVID-19 resurgence in Europe. We inform this model using genomic, mobility and epidemiological data from 10 West European countries and estimate that in many countries more than 50% of the lineages circulating in late summer resulted from new introductions since June 15th. The success in onwards transmission of these lineages is predicted by SARS-CoV-2 incidence during this period. Relatively early introductions from Spain into the United Kingdom contributed to the successful spread of the 20A.EU1/B.1.177 variant. The pervasive spread of variants that have not been associated with an advantage in transmissibility highlights the threat of novel variants of concern that emerged more recently and have been disseminated by holiday travel. Our findings indicate that more effective and coordinated measures are required to contain spread through cross-border travel.
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Affiliation(s)
- Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
- Global Virus Network (GVN), Baltimore, MD, USA
| | - Nick Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
- Population Health Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Samuel L Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France
| | - Frederik Van den Broeck
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
- Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
| | - Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Xiang Ji
- Department of Mathematics, School of Science & Engineering, Tulane University, New Orleans, LA, USA
| | - Anthony Levasseur
- Microbes, Evolution, Phylogeny and Infection, Aix-Marseille Université and Marseille Institut Universitaire de France, Marseille, France
| | | | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12, 50 av. FD Roosevelt, 1050 Bruxelles, Belgium
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25
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Worobey M, Pekar J, Larsen BB, Nelson MI, Hill V, Joy JB, Rambaut A, Suchard MA, Wertheim JO, Lemey P. The emergence of SARS-CoV-2 in Europe and North America. Science 2020; 370:564-570. [PMID: 32912998 PMCID: PMC7810038 DOI: 10.1126/science.abc8169] [Citation(s) in RCA: 278] [Impact Index Per Article: 55.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 09/03/2020] [Indexed: 12/16/2022]
Abstract
Accurate understanding of the global spread of emerging viruses is critical for public health responses and for anticipating and preventing future outbreaks. Here we elucidate when, where, and how the earliest sustained severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission networks became established in Europe and North America. Our results suggest that rapid early interventions successfully prevented early introductions of the virus from taking hold in Germany and the United States. Other, later introductions of the virus from China to both Italy and Washington state, United States, founded the earliest sustained European and North America transmission networks. Our analyses demonstrate the effectiveness of public health measures in preventing onward transmission and show that intensive testing and contact tracing could have prevented SARS-CoV-2 outbreaks from becoming established in these regions.
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Affiliation(s)
- Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA.
| | - Jonathan Pekar
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093, USA
| | - Brendan B Larsen
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
| | - Martha I Nelson
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Verity Hill
- Institute of Evolutionary Biology, University of Edinburgh, King's Buildings, Edinburgh EH9 3FL, UK
| | - Jeffrey B Joy
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
- BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
- Bioinformatics Programme, University of British Columbia, Vancouver, BC, Canada
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, King's Buildings, Edinburgh EH9 3FL, UK
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Joel O Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
| | - Philippe Lemey
- KU Leuven Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Epidemiological Virology, Leuven, Belgium.
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26
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Lemey P, Hong SL, Hill V, Baele G, Poletto C, Colizza V, O'Toole Á, McCrone JT, Andersen KG, Worobey M, Nelson MI, Rambaut A, Suchard MA. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2. Nat Commun 2020; 11:5110. [PMID: 33037213 PMCID: PMC7547076 DOI: 10.1038/s41467-020-18877-9] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 09/17/2020] [Indexed: 12/21/2022] Open
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.
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Affiliation(s)
- Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Laboratory of Clinical and Evolutionary Virology, Leuven, Belgium.
| | - Samuel L Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Laboratory of Clinical and Evolutionary Virology, Leuven, Belgium
| | - Verity Hill
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Laboratory of Clinical and Evolutionary Virology, Leuven, Belgium
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012, Paris, France
| | - Áine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | - John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | - Kristian G Andersen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - Martha I Nelson
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA.
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27
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Landis M, Edwards EJ, Donoghue MJ. Modeling Phylogenetic Biome Shifts on a Planet with a Past. Syst Biol 2020; 70:86-107. [PMID: 32514540 DOI: 10.1093/sysbio/syaa045] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 05/27/2020] [Indexed: 12/30/2022] Open
Abstract
The spatial distribution of biomes has changed considerably over deep time, so the geographical opportunity for an evolutionary lineage to shift into a new biome may depend on how the availability and connectivity of biomes has varied temporally. To better understand how lineages shift between biomes in space and time, we developed a phylogenetic biome shift model in which each lineage shifts between biomes and disperses between regions at rates that depend on the lineage's biome affinity and location relative to the spatial distribution of biomes at any given time. To study the behavior of the biome shift model in an empirical setting, we developed a literature-based representation of paleobiome structure for three mesic forest biomes, six regions, and eight time strata, ranging from the Late Cretaceous (100 Ma) through the present. We then fitted the model to a time-calibrated phylogeny of 119 Viburnum species to compare how the results responded to various realistic or unrealistic assumptions about paleobiome structure. Ancestral biome estimates that account for paleobiome dynamics reconstructed a warm temperate (or tropical) origin of Viburnum, which is consistent with previous fossil-based estimates of ancestral biomes. Imposing unrealistic paleobiome distributions led to ancestral biome estimates that eliminated support for tropical origins, and instead inflated support for cold temperate ancestry throughout the warmer Paleocene and Eocene. The biome shift model we describe is applicable to the study of evolutionary systems beyond Viburnum, and the core mechanisms of our model are extensible to the design of richer phylogenetic models of historical biogeography and/or lineage diversification. We conclude that biome shift models that account for dynamic geographical opportunities are important for inferring ancestral biomes that are compatible with our understanding of Earth history.[Ancestral states; biome shifts; historical biogeography; niche conservatism; phylogenetics].
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Affiliation(s)
- Michael Landis
- Department of Biology, Washington University in St. Louis, One Brookings Drive, St. Louis, MI 63130, USA.,Department of Ecology & Evolutionary Biology, Yale University, PO Box 208106, New Haven, CT 06520, USA
| | - Erika J Edwards
- Department of Ecology & Evolutionary Biology, Yale University, PO Box 208106, New Haven, CT 06520, USA.,Division of Botany, Yale Peabody Museum of Natural History, P.O. Box 208118, New Haven, CT 06520, USA
| | - Michael J Donoghue
- Department of Ecology & Evolutionary Biology, Yale University, PO Box 208106, New Haven, CT 06520, USA.,Division of Botany, Yale Peabody Museum of Natural History, P.O. Box 208118, New Haven, CT 06520, USA
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28
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Abstract
Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.
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Affiliation(s)
- Peter M. Kasson
- Department of Biomedical Engineering and Department of Molecular Physiology, University of Virginia, Charlottesville, Virginia 22908, USA
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, 752 37 Uppsala, Sweden
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29
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Allicock OM, Sahadeo N, Lemey P, Auguste AJ, Suchard MA, Rambaut A, Carrington CVF. Determinants of dengue virus dispersal in the Americas. Virus Evol 2020; 6:veaa074. [PMID: 33408877 PMCID: PMC7772473 DOI: 10.1093/ve/veaa074] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Dengue viruses (DENVs) are classified into four serotypes, each of which contains multiple genotypes. DENV genotypes introduced into the Americas over the past five decades have exhibited different rates and patterns of spatial dispersal. In order to understand factors underlying these patterns, we utilized a statistical framework that allows for the integration of ecological, socioeconomic, and air transport mobility data as predictors of viral diffusion while inferring the phylogeographic history. Predictors describing spatial diffusion based on several covariates were compared using a generalized linear model approach, where the support for each scenario and its contribution is estimated simultaneously from the data set. Although different predictors were identified for different serotypes, our analysis suggests that overall diffusion of DENV-1, -2, and -3 in the Americas was associated with airline traffic. The other significant predictors included human population size, the geographical distance between countries and between urban centers and the density of people living in urban environments.
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Affiliation(s)
- Orchid M Allicock
- Department of Preclinical Sciences, Faculty of Medical Sciences, University of the West Indies, St. Augustine, Trinidad and Tobago
- Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Nikita Sahadeo
- Department of Preclinical Sciences, Faculty of Medical Sciences, University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Philippe Lemey
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory for Clinical and Epidemiological Virology, Leuven, Belgium
| | - Albert J Auguste
- Department of Entomology, Fralin Life Science Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Charlotte Auerbach Road, The Kings Buildings, Edinburgh, EH9 3FL, UK
| | - Christine V F Carrington
- Department of Preclinical Sciences, Faculty of Medical Sciences, University of the West Indies, St. Augustine, Trinidad and Tobago
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30
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Lemey P, Hong S, Hill V, Baele G, Poletto C, Colizza V, O’Toole Á, McCrone JT, Andersen KG, Worobey M, Nelson MI, Rambaut A, Suchard MA. Accommodating individual travel history, global mobility, and unsampled diversity in phylogeography: a SARS-CoV-2 case study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.22.165464. [PMID: 32596695 PMCID: PMC7315996 DOI: 10.1101/2020.06.22.165464] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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, which is the virus responsible for the COVID-19 pandemic. Despite the availability of staggering numbers of genomes on a global scale, evolutionary reconstructions of SARS-CoV-2 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 the impact of sampling bias by incorporating unsampled sequences 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 new data integration approaches 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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Affiliation(s)
- Philippe Lemey
- KU Leuven Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary Virology, Leuven, Belgium
| | - Samuel Hong
- KU Leuven Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary Virology, Leuven, Belgium
| | - Verity Hill
- Centre for Immunology, Infection and Evolution, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
| | - Guy Baele
- KU Leuven Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary Virology, Leuven, Belgium
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France
| | - Áine O’Toole
- Centre for Immunology, Infection and Evolution, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
| | - John T. McCrone
- Centre for Immunology, Infection and Evolution, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
| | - Kristian G. Andersen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA 92037, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
| | - Martha I. Nelson
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Andrew Rambaut
- Centre for Immunology, Infection and Evolution, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
| | - Marc A. Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
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31
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Membrebe JV, Suchard MA, Rambaut A, Baele G, Lemey P. Bayesian Inference of Evolutionary Histories under Time-Dependent Substitution Rates. Mol Biol Evol 2020; 36:1793-1803. [PMID: 31004175 PMCID: PMC6657730 DOI: 10.1093/molbev/msz094] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Many factors complicate the estimation of time scales for phylogenetic histories, requiring increasingly complex evolutionary models and inference procedures. The widespread application of molecular clock dating has led to the insight that evolutionary rate estimates may vary with the time frame of measurement. This is particularly well established for rapidly evolving viruses that can accumulate sequence divergence over years or even months. However, this rapid evolution stands at odds with a relatively high degree of conservation of viruses or endogenous virus elements over much longer time scales. Building on recent insights into time-dependent evolutionary rates, we develop a formal and flexible Bayesian statistical inference approach that accommodates rate variation through time. We evaluate the novel molecular clock model on a foamy virus cospeciation history and a lentivirus evolutionary history and compare the performance to other molecular clock models. For both virus examples, we estimate a similarly strong time-dependent effect that implies rates varying over four orders of magnitude. The application of an analogous codon substitution model does not implicate long-term purifying selection as the cause of this effect. However, selection does appear to affect divergence time estimates for the less deep evolutionary history of the Ebolavirus genus. Finally, we explore the application of our approach on woolly mammoth ancient DNA data, which shows a much weaker, but still important, time-dependent rate effect that has a noticeable impact on node age estimates. Future developments aimed at incorporating more complex evolutionary processes will further add to the broad applicability of our approach.
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Affiliation(s)
- Jade Vincent Membrebe
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA.,Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA.,Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.,Fogarty International Center, National Institutes of Health, Bethesda, MD
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium
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32
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Hicks JT, Lee DH, Duvvuri VR, Kim Torchetti M, Swayne DE, Bahl J. Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak. PLoS Pathog 2020; 16:e1007857. [PMID: 31961906 PMCID: PMC7004387 DOI: 10.1371/journal.ppat.1007857] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 02/06/2020] [Accepted: 01/04/2020] [Indexed: 11/18/2022] Open
Abstract
The 2014-2015 highly pathogenic avian influenza (HPAI) H5NX outbreak represents the largest and most expensive HPAI outbreak in the United States to date. Despite extensive traditional and molecular epidemiological studies, factors associated with the spread of HPAI among midwestern poultry premises remain unclear. To better understand the dynamics of this outbreak, 182 full genome HPAI H5N2 sequences isolated from commercial layer chicken and turkey production premises were analyzed using evolutionary models able to accommodate epidemiological and geographic information. Epidemiological compartmental models embedded in a phylogenetic framework provided evidence that poultry type acted as a barrier to the transmission of virus among midwestern poultry farms. Furthermore, after initial introduction, the propagation of HPAI cases was self-sustainable within the commercial poultry industries. Discrete trait diffusion models indicated that within state viral transitions occurred more frequently than inter-state transitions. Distance and sample size were very strongly supported as associated with viral transition between county groups (Bayes Factor > 30.0). Together these findings indicate that the different types of midwestern poultry industries were not a single homogenous population, but rather, the outbreak was shaped by poultry industries and geographic factors.
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Affiliation(s)
- Joseph T. Hicks
- Center for Ecology of Infectious Diseases, Department of Infectious Diseases, Department of Ecology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
| | - Dong-Hun Lee
- Department of Pathobiology and Veterinary Science, the University of Connecticut, Storrs, Connecticut, United States of America
| | - Venkata R. Duvvuri
- Center for Ecology of Infectious Diseases, Department of Infectious Diseases, Department of Ecology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
| | - Mia Kim Torchetti
- U.S. Department of Agriculture, Ames, Iowa, United States of America
| | - David E. Swayne
- Exotic and Emerging Avian Viral Diseases Research Unit, U.S. National Poultry Research Center, Agricultural Research Service, U.S. Department of Agriculture, Athens, Georgia, United States of America
| | - Justin Bahl
- Center for Ecology of Infectious Diseases, Department of Infectious Diseases, Department of Ecology and Biostatistics, Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Duke-NUS Graduate Medical School, Singapore
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33
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Müller NF, Dudas G, Stadler T. Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations. Virus Evol 2019; 5:vez030. [PMID: 31428459 PMCID: PMC6693038 DOI: 10.1093/ve/vez030] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Population dynamics can be inferred from genetic sequence data by using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume migration rates and effective population sizes to be constant through time in structured populations. When considering rates to vary through time in structured populations, the number of parameters to infer increases rapidly and the available data might not be sufficient to inform these. Additionally, it is often of interest to know what predicts these parameters rather than knowing the parameters themselves. Here, we introduce a method to infer the predictors for time-varying migration rates and effective population sizes by using a generalized linear model (GLM) approach under the marginal approximation of the structured coalescent. Using simulations, we show that our approach is able to reliably infer the model parameters and its predictors from phylogenetic trees. Furthermore, when simulating trees under the structured coalescent, we show that our new approach outperforms the discrete trait GLM model. We then apply our framework to a previously described Ebola virus dataset, where we infer the parameters and its predictors from genome sequences while accounting for phylogenetic uncertainty. We infer weekly cases to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This approach is implemented as part of the BEAST2 package MASCOT, which allows us to jointly infer population dynamics, i.e. the parameters and predictors, within structured populations, the phylogenetic tree, and evolutionary parameters.
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Affiliation(s)
- Nicola F Müller
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Gytis Dudas
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Gothenburg Global Biodiversity Centre, Gothenburg, Sweden
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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34
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Yang J, Müller NF, Bouckaert R, Xu B, Drummond AJ. Bayesian phylodynamics of avian influenza A virus H9N2 in Asia with time-dependent predictors of migration. PLoS Comput Biol 2019; 15:e1007189. [PMID: 31386651 PMCID: PMC6684064 DOI: 10.1371/journal.pcbi.1007189] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 06/17/2019] [Indexed: 11/25/2022] Open
Abstract
Model-based phylodynamic approaches recently employed generalized linear models (GLMs) to uncover potential predictors of viral spread. Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent. However, these studies mainly focused on predictors that are assumed to be constant through time. Here we inferred the phylodynamics of avian influenza A virus H9N2 isolated in 12 Asian countries and regions under both discrete trait analysis (DTA) and structured coalescent (MASCOT) approaches. Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread. We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures. This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives. Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time, and these effective population dynamics within each country were predicted by a GLM. International annual poultry trade is a strongly supported predictor of virus migration rates. There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated. In time-dependent MASCOT models, national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China. Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction. We demonstrated the contribution of poultry trade and geographic proximity (potentially unheralded wild bird movements) to avian influenza spread in Asia. To gain a better understanding of the drivers of H9N2 spread, we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries.
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Affiliation(s)
- Jing Yang
- College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
- School of Computer Science, University of Auckland, Auckland, New Zealand
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
| | - Nicola F. Müller
- Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Remco Bouckaert
- School of Computer Science, University of Auckland, Auckland, New Zealand
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
- Max Planck Institute for the Science of Human History, Jena, Germany
| | - Bing Xu
- College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Alexei J. Drummond
- School of Computer Science, University of Auckland, Auckland, New Zealand
- Centre for Computational Evolution, University of Auckland, Auckland, New Zealand
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35
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Reconstruction of the Genetic History and the Current Spread of HIV-1 Subtype A in Germany. J Virol 2019; 93:JVI.02238-18. [PMID: 30944175 DOI: 10.1128/jvi.02238-18] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Accepted: 03/13/2019] [Indexed: 12/15/2022] Open
Abstract
HIV-1 non-B infections have been increasing in Europe for several years. In Germany, subtype A belongs to the most abundant non-B subtypes showing an increasing prevalence of 8.3% among new infections in 2016. Here we trace the origin and examine the current spread of the German HIV-1 subtype A epidemic. Bayesian coalescence and birth-death analyses were performed with 180 German HIV-1 pol sequences and 528 related and publicly available sequences to reconstruct the population dynamics and fluctuations for each of the transmission groups. Our reconstructions indicate two distinct sources of the German subtype A epidemic, with an Eastern European and an Eastern African lineage both cocirculating in the country. A total of 13 German-origin clusters were identified; among these, 6 clusters showed recent activity. Introductions leading to further countrywide spread originated predominantly from Eastern Africa when introduced before 2005. Since 2005, however, spreading introductions have occurred exclusively within the Eastern European clade. Moreover, we observed changes in the main route of subtype A transmission. The beginning of the German epidemic (1985 to 1995) was dominated by heterosexual transmission of the Eastern African lineage. Since 2005, transmissions among German men who have sex with men (MSM) have been increasing and have been associated with the Eastern European lineage. Infections among people who inject drugs dominated between 1998 and 2005. Our findings on HIV-1 subtype A infections provide new insights into the spread of this virus and extend the understanding of the HIV epidemic in Germany.IMPORTANCE HIV-1 subtype A is the second most prevalent subtype worldwide, with a high prevalence in Eastern Africa and Eastern Europe. However, an increase of non-B infections, including subtype A infections, has been observed in Germany and other European countries. There has simultaneously been an increased flow of refugees into Europe and especially into Germany, raising the question of whether the surge in non-B infections resulted from this increased immigration or whether German transmission chains are mainly involved. This study is the first comprehensive subtype A study from a western European country analyzing in detail its phylogenetic origin, the impact of various transmission routes, and its current spread. The results and conclusions presented provide new and substantial insights for virologists, epidemiologists, and the general public health sector. In this regard, they should be useful to those authorities responsible for developing public health intervention strategies to combat the further spread of HIV/AIDS.
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36
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Ali A, Melcher U. Modeling of Mutational Events in the Evolution of Viruses. Viruses 2019; 11:v11050418. [PMID: 31060293 PMCID: PMC6563203 DOI: 10.3390/v11050418] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/27/2019] [Accepted: 05/02/2019] [Indexed: 11/24/2022] Open
Abstract
Diverse studies of viral evolution have led to the recognition that the evolutionary rates of viral taxa observed are dependent on the time scale being investigated—with short-term studies giving fast substitution rates, and orders of magnitude lower rates for deep calibrations. Although each of these factors may contribute to this time dependent rate phenomenon, a more fundamental cause should be considered. We sought to test computationally whether the basic phenomena of virus evolution (mutation, replication, and selection) can explain the relationships between the evolutionary and phylogenetic distances. We tested, by computational inference, the hypothesis that the phylogenetic distances between the pairs of sequences are functions of the evolutionary path lengths between them. A Basic simulation revealed that the relationship between simulated genetic and mutational distances is non-linear, and can be consistent with different rates of nucleotide substitution at different depths of branches in phylogenetic trees.
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Affiliation(s)
- Akhtar Ali
- Department of Biological Sciences, University of Tulsa, Tulsa, OK 74104, USA.
| | - Ulrich Melcher
- Department of Biochemistry & Molecular Biology, Oklahoma State University, Stillwater, OK 74078-3035, USA.
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37
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Dellicour S, Vrancken B, Trovão NS, Fargette D, Lemey P. On the importance of negative controls in viral landscape phylogeography. Virus Evol 2018; 4:vey023. [PMID: 30151241 PMCID: PMC6101606 DOI: 10.1093/ve/vey023] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Phylogeographic reconstructions are becoming an established procedure to evaluate the factors that could impact virus spread. While a discrete phylogeographic approach can be used to test predictors of transition rates among discrete locations, alternative continuous phylogeographic reconstructions can also be exploited to investigate the impact of underlying environmental layers on the dispersal velocity of a virus. The two approaches are complementary tools for studying pathogens' spread, but in both cases, care must be taken to avoid misinterpretations. Here, we analyse rice yellow mottle virus (RYMV) sequence data from West and East Africa to illustrate how both approaches can be used to study the impact of environmental factors on the virus’ dispersal frequency and velocity. While it was previously reported that host connectivity was a major determinant of RYMV spread, we show that this was a false positive result due to the lack of appropriate negative controls. We also discuss and compare the phylodynamic tools currently available for investigating the impact of environmental factors on virus spread.
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Affiliation(s)
- Simon Dellicour
- Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Leuven, Belgium.,Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, CP160/12 50, av. FD Roosevelt, 1050 Bruxelles, Belgium
| | - Bram Vrancken
- Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Leuven, Belgium
| | - Nídia S Trovão
- Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Leuven, Belgium
| | - Denis Fargette
- Institut de Recherche pour le Développement (IRD), UMR IPME (IRD, CIRAD, Université de Montpellier), BP 64051 34394 Montpellier cedex 5, France
| | - Philippe Lemey
- Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven, Leuven, Belgium
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38
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Dolan PT, Whitfield ZJ, Andino R. Mechanisms and Concepts in RNA Virus Population Dynamics and Evolution. Annu Rev Virol 2018; 5:69-92. [PMID: 30048219 DOI: 10.1146/annurev-virology-101416-041718] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
RNA viruses are unique in their evolutionary capacity, exhibiting high mutation rates and frequent recombination. They rapidly adapt to environmental changes, such as shifts in immune pressure or pharmacological challenge. The evolution of RNA viruses has been brought into new focus with the recent developments of genetic and experimental tools to explore and manipulate the evolutionary dynamics of viral populations. These studies have uncovered new mechanisms that enable viruses to overcome evolutionary challenges in the environment and have emphasized the intimate relationship of viral populations with evolution. Here, we review some of the emerging viral and host mechanisms that underlie the evolution of RNA viruses. We also discuss new studies that demonstrate that the relationship between evolutionary dynamics and virus biology spans many spatial and temporal scales, affecting transmission dynamics within and between hosts as well as pathogenesis.
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Affiliation(s)
- Patrick T Dolan
- Department of Biology, Stanford University, Stanford, California 94305, USA.,Department of Microbiology and Immunology, University of California, San Francisco, California 94143, USA;
| | - Zachary J Whitfield
- Department of Microbiology and Immunology, University of California, San Francisco, California 94143, USA;
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, California 94143, USA;
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39
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Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaut A, Lemey P. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. Nat Commun 2018; 9:2222. [PMID: 29884821 PMCID: PMC5993714 DOI: 10.1038/s41467-018-03763-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 03/12/2018] [Indexed: 01/25/2023] Open
Abstract
Genetic analyses have provided important insights into Ebola virus spread during the recent West African outbreak, but their implications for specific intervention scenarios remain unclear. Here, we address this issue using a collection of phylodynamic approaches. We show that long-distance dispersal events were not crucial for epidemic expansion and that preventing viral lineage movement to any given administrative area would, in most cases, have had little impact. However, major urban areas were critical in attracting and disseminating the virus: preventing viral lineage movement to all three capitals simultaneously would have contained epidemic size to one-third. We also show that announcements of border closures were followed by a significant but transient effect on international virus dispersal. By quantifying the hypothetical impact of different intervention strategies, as well as the impact of barriers on dispersal frequency, our study illustrates how phylodynamic analyses can help to address specific epidemiological and outbreak control questions. During the last Ebola virus outbreak in West Africa, a large amount of viral genomic data was obtained. Here, Dellicour et al. use phylodynamic approaches to assess effect of intervention strategies such as border closures.
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Affiliation(s)
- Simon Dellicour
- Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Gytis Dudas
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, 98109, Seattle, WA, USA
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, OX1 3PS, United Kingdom
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, OX1 3PS, United Kingdom
| | - Marc A Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA.,Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, 90095, USA.,Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, 90095, USA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, King's Buildings, Edinburgh, EH9 3FL, UK.,Fogarty International Center, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven - University of Leuven, Herestraat 49, 3000, Leuven, Belgium
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40
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Brunker K, Lemey P, Marston DA, Fooks AR, Lugelo A, Ngeleja C, Hampson K, Biek R. Landscape attributes governing local transmission of an endemic zoonosis: Rabies virus in domestic dogs. Mol Ecol 2018; 27:773-788. [PMID: 29274171 PMCID: PMC5900915 DOI: 10.1111/mec.14470] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 11/15/2017] [Accepted: 11/20/2017] [Indexed: 12/24/2022]
Abstract
Landscape heterogeneity plays an important role in disease spread and persistence, but quantifying landscape influences and their scale dependence is challenging. Studies have focused on how environmental features or global transport networks influence pathogen invasion and spread, but their influence on local transmission dynamics that underpin the persistence of endemic diseases remains unexplored. Bayesian phylogeographic frameworks that incorporate spatial heterogeneities are promising tools for analysing linked epidemiological, environmental and genetic data. Here, we extend these methodological approaches to decipher the relative contribution and scale-dependent effects of landscape influences on the transmission of endemic rabies virus in Serengeti district, Tanzania (area ~4,900 km2 ). Utilizing detailed epidemiological data and 152 complete viral genomes collected between 2004 and 2013, we show that the localized presence of dogs but not their density is the most important determinant of diffusion, implying that culling will be ineffective for rabies control. Rivers and roads acted as barriers and facilitators to viral spread, respectively, and vaccination impeded diffusion despite variable annual coverage. Notably, we found that landscape effects were scale-dependent: rivers were barriers and roads facilitators on larger scales, whereas the distribution of dogs was important for rabies dispersal across multiple scales. This nuanced understanding of the spatial processes that underpin rabies transmission can be exploited for targeted control at the scale where it will have the greatest impact. Moreover, this research demonstrates how current phylogeographic frameworks can be adapted to improve our understanding of endemic disease dynamics at different spatial scales.
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Affiliation(s)
- Kirstyn Brunker
- Institute of Biodiversity, Animal Health and Comparative MedicineUniversity of GlasgowGlasgowUK
- The Boyd Orr Centre for Population and Ecosystem HealthUniversity of GlasgowGlasgowUK
- Animal and Plant Health AgencyAddlestoneUK
| | - Philippe Lemey
- Department of Microbiology and ImmunologyKU Leuven – University of LeuvenLeuvenBelgium
| | | | | | - Ahmed Lugelo
- Department of Veterinary Medicine and Public HealthSokoine University of AgricultureMorogoroUnited Republic of Tanzania
| | - Chanasa Ngeleja
- Tanzania Veterinary Laboratory AgencyDar es SalaamUnited Republic of Tanzania
| | - Katie Hampson
- Institute of Biodiversity, Animal Health and Comparative MedicineUniversity of GlasgowGlasgowUK
- The Boyd Orr Centre for Population and Ecosystem HealthUniversity of GlasgowGlasgowUK
| | - Roman Biek
- Institute of Biodiversity, Animal Health and Comparative MedicineUniversity of GlasgowGlasgowUK
- The Boyd Orr Centre for Population and Ecosystem HealthUniversity of GlasgowGlasgowUK
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41
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King B, Qiao T, Lee MSY, Zhu M, Long JA. Bayesian Morphological Clock Methods Resurrect Placoderm Monophyly and Reveal Rapid Early Evolution in Jawed Vertebrates. Syst Biol 2018; 66:499-516. [PMID: 27920231 DOI: 10.1093/sysbio/syw107] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 11/18/2016] [Indexed: 11/13/2022] Open
Abstract
The phylogeny of early gnathostomes provides an important framework for understanding one of the most significant evolutionary events, the origin and diversification of jawed vertebrates. A series of recent cladistic analyses have suggested that the placoderms, an extinct group of armoured fish, form a paraphyletic group basal to all other jawed vertebrates. We revised and expanded this morphological data set, most notably by sampling autapomorphies in a similar way to parsimony-informative traits, thus ensuring this data (unlike most existing morphological data sets) satisfied an important assumption of Bayesian tip-dated morphological clock approaches. We also found problems with characters supporting placoderm paraphyly, including character correlation and incorrect codings. Analysis of this data set reveals that paraphyly and monophyly of core placoderms (excluding maxillate forms) are essentially equally parsimonious. The two alternative topologies have different root positions for the jawed vertebrates but are otherwise similar. However, analysis using tip-dated clock methods reveals strong support for placoderm monophyly, due to this analysis favoring trees with more balanced rates of evolution. Furthermore, enforcing placoderm paraphyly results in higher levels and unusual patterns of rate heterogeneity among branches, similar to that generated from simulated trees reconstructed with incorrect root positions. These simulations also show that Bayesian tip-dated clock methods outperform parsimony when the outgroup is largely uninformative (e.g., due to inapplicable characters), as might be the case here. The analysis also reveals that gnathostomes underwent a rapid burst of evolution during the Silurian period which declined during the Early Devonian. This rapid evolution during a period with few articulated fossils might partly explain the difficulty in ascertaining the root position of jawed vertebrates.
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Affiliation(s)
- Benedict King
- School of Biological Sciences, Flinders University, PO Box 2100, Adelaide, South Australia 5001, Australia
| | - Tuo Qiao
- Key Laboratory of Vertebrate Evolution and Human Origins of Chinese Academy of Sciences, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, PO Box 643, Beijing 100044, China
| | - Michael S Y Lee
- School of Biological Sciences, Flinders University, PO Box 2100, Adelaide, South Australia 5001, Australia.,Earth Sciences Section, South Australian Museum, North Terrace, Adelaide 5000, Australia
| | - Min Zhu
- Key Laboratory of Vertebrate Evolution and Human Origins of Chinese Academy of Sciences, Institute of Vertebrate Paleontology and Paleoanthropology, Chinese Academy of Sciences, PO Box 643, Beijing 100044, China
| | - John A Long
- School of Biological Sciences, Flinders University, PO Box 2100, Adelaide, South Australia 5001, Australia
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42
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Molecular epidemiology reveals the role of war in the spread of HIV in Ukraine. Proc Natl Acad Sci U S A 2018; 115:1051-1056. [PMID: 29339468 DOI: 10.1073/pnas.1701447115] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Ukraine has one of the largest HIV epidemics in Europe, historically driven by people who inject drugs (PWID). The epidemic showed signs of stabilization in 2012, but the recent war in eastern Ukraine may be reigniting virus spread. We investigated the movement of HIV-infected people within Ukraine before and during the conflict. We analyzed HIV-1 subtype-A pol nucleotide sequences sampled during 2012-2015 from 427 patients of 24 regional AIDS centers and used phylogeographic analysis to reconstruct virus movement among different locations in Ukraine. We then tested for correlations between reported PWID behaviors and reconstructed patterns of virus spread. Our analyses suggest that Donetsk and Lugansk, two cities not controlled by the Ukrainian government in eastern Ukraine, were significant exporters of the virus to the rest of the country. Additional analyses showed that viral dissemination within the country changed after 2013. Spearman correlation analysis showed that incoming virus flow was correlated with the number of HIV-infected internally displaced people. Additionally, there was a correlation between more intensive virus movement and locations with a higher proportion of PWID practicing risky sexual behaviors. Our findings suggest that effective prevention responses should involve internally displaced people and people who frequently travel to war-affected regions. Scale-up of harm reduction services for PWID will be an important factor in preventing new local HIV outbreaks in Ukraine.
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43
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Troupin C, Picard-Meyer E, Dellicour S, Casademont I, Kergoat L, Lepelletier A, Dacheux L, Baele G, Monchâtre-Leroy E, Cliquet F, Lemey P, Bourhy H. Host Genetic Variation Does Not Determine Spatio-Temporal Patterns of European Bat 1 Lyssavirus. Genome Biol Evol 2017; 9:3202-3213. [PMID: 29165566 PMCID: PMC5721339 DOI: 10.1093/gbe/evx236] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2017] [Indexed: 12/22/2022] Open
Abstract
The majority of bat rabies cases in Europe are attributed to European bat 1 lyssavirus (EBLV-1), circulating mainly in serotine bats (Eptesicus serotinus). Two subtypes have been defined (EBLV-1a and EBLV-1b), each associated with a different geographical distribution. In this study, we undertake a comprehensive sequence analysis based on 80 newly obtained EBLV-1 nearly complete genome sequences from nine European countries over a 45-year period to infer selection pressures, rates of nucleotide substitution, and evolutionary time scale of these two subtypes in Europe. Our results suggest that the current lineage of EBLV-1 arose in Europe ∼600 years ago and the virus has evolved at an estimated average substitution rate of ∼4.19×10-5 subs/site/year, which is among the lowest recorded for RNA viruses. In parallel, we investigate the genetic structure of French serotine bats at both the nuclear and mitochondrial level and find that they constitute a single genetic cluster. Furthermore, Mantel tests based on interindividual distances reveal the absence of correlation between genetic distances estimated between viruses and between host individuals. Taken together, this indicates that the genetic diversity observed in our E. serotinus samples does not account for EBLV-1a and -1b segregation and dispersal in Europe.
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Affiliation(s)
- Cécile Troupin
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
| | - Evelyne Picard-Meyer
- Laboratory for Rabies and Wildlife ANSES, Nancy, OIE Reference Laboratory for Rabies, European Union Reference Laboratory for Rabies, European Union Reference Laboratory for Rabies Serology, WHO Collaborating Centre for Research and Management on Zoonoses, Malzeville, France
| | - Simon Dellicour
- Institut Pasteur, Laboratory for Clinical and Epidemiological Virology, Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Belgium
| | - Isabelle Casademont
- Unité de la Génétique Fonctionnelle des Maladies Infectieuses, Paris, France
| | - Lauriane Kergoat
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
| | - Anthony Lepelletier
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
| | - Laurent Dacheux
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
| | - Guy Baele
- Institut Pasteur, Laboratory for Clinical and Epidemiological Virology, Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Belgium
| | - Elodie Monchâtre-Leroy
- Laboratory for Rabies and Wildlife ANSES, Nancy, OIE Reference Laboratory for Rabies, European Union Reference Laboratory for Rabies, European Union Reference Laboratory for Rabies Serology, WHO Collaborating Centre for Research and Management on Zoonoses, Malzeville, France
| | - Florence Cliquet
- Laboratory for Rabies and Wildlife ANSES, Nancy, OIE Reference Laboratory for Rabies, European Union Reference Laboratory for Rabies, European Union Reference Laboratory for Rabies Serology, WHO Collaborating Centre for Research and Management on Zoonoses, Malzeville, France
| | - Philippe Lemey
- Institut Pasteur, Laboratory for Clinical and Epidemiological Virology, Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Belgium
| | - Hervé Bourhy
- Institut Pasteur, Unit Lyssavirus Dynamics and Host Adaptation, WHO Collaborating Centre for Reference and Research on Rabies, Paris, France
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44
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45
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Jacquot M, Nomikou K, Palmarini M, Mertens P, Biek R. Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference. Proc Biol Sci 2017; 284:20170919. [PMID: 29021180 PMCID: PMC5647287 DOI: 10.1098/rspb.2017.0919] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 09/08/2017] [Indexed: 01/13/2023] Open
Abstract
Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies.
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Affiliation(s)
- Maude Jacquot
- College of Medical, Veterinary and Life Sciences, Institute of Biodiversity, Animal Health and Comparative Medicine, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, UK
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Kyriaki Nomikou
- The Pirbright Institute, Pirbright, Woking, UK
- The School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, UK
| | | | - Peter Mertens
- The Pirbright Institute, Pirbright, Woking, UK
- The School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, UK
| | - Roman Biek
- College of Medical, Veterinary and Life Sciences, Institute of Biodiversity, Animal Health and Comparative Medicine, Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, UK
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
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46
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Abstract
Computer-assisted technologies of the genomic structure, biological function, and evolution of viruses remain a largely neglected area of research. The attention of bioinformaticians to this challenging field is currently unsatisfying in respect to its medical and biological importance. The power of new genome sequencing technologies, associated with new tools to handle "big data", provides unprecedented opportunities to address fundamental questions in virology. Here, we present an overview of the current technologies, challenges, and advantages of Next-Generation Sequencing (NGS) in relation to the field of virology. We present how viral sequences can be detected de novo out of current short-read NGS data. Furthermore, we discuss the challenges and applications of viral quasispecies and how secondary structures, commonly shaped by RNA viruses, can be computationally predicted. The phylogenetic analysis of viruses, as another ubiquitous field in virology, forms an essential element of describing viral epidemics and challenges current algorithms. Recently, the first specialized virus-bioinformatic organizations have been established. We need to bring together virologists and bioinformaticians and provide a platform for the implementation of interdisciplinary collaborative projects at local and international scales. Above all, there is an urgent need for dedicated software tools to tackle various challenges in virology.
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Affiliation(s)
- Martin Hölzer
- RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany; European Virus Bioinformatics Center (EVBC), Jena, Germany
| | - Manja Marz
- RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany; European Virus Bioinformatics Center (EVBC), Jena, Germany; FLI Leibniz Institute for Age Research, Jena, Germany.
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47
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Abstract
The human immunodeficiency virus (HIV) evolves rapidly owing to the combined activity of error-prone reverse transcriptase, recombination, and short generation times, leading to extensive viral diversity both within and between hosts. This diversity is a major contributing factor in the failure of the immune system to eradicate the virus and has important implications for the development of suitable drugs and vaccines to combat infection. This review will discuss the recent technological advances that have shed light on HIV evolution and will summarise emerging concepts in this field.
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Affiliation(s)
- Sophie M Andrews
- Nuffield Department of Clinical Medicine, University of Oxford, NDMRB, Oxford, UK
| | - Sarah Rowland-Jones
- Nuffield Department of Clinical Medicine, University of Oxford, NDMRB, Oxford, UK
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48
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Dudas G, Carvalho LM, Bedford T, Tatem AJ, Baele G, Faria NR, Park DJ, Ladner JT, Arias A, Asogun D, Bielejec F, Caddy SL, Cotten M, D’Ambrozio J, Dellicour S, Di Caro A, Diclaro J, Duraffour S, Elmore MJ, Fakoli LS, Faye O, Gilbert ML, Gevao SM, Gire S, Gladden-Young A, Gnirke A, Goba A, Grant DS, Haagmans BL, Hiscox JA, Jah U, Kargbo B, Kugelman JR, Liu D, Lu J, Malboeuf CM, Mate S, Matthews DA, Matranga CB, Meredith LW, Qu J, Quick J, Pas SD, Phan MVT, Pollakis G, Reusken CB, Sanchez-Lockhart M, Schaffner SF, Schieffelin JS, Sealfon RS, Simon-Loriere E, Smits SL, Stoecker K, Thorne L, Tobin EA, Vandi MA, Watson SJ, West K, Whitmer S, Wiley MR, Winnicki SM, Wohl S, Wölfel R, Yozwiak NL, Andersen KG, Blyden SO, Bolay F, Carroll M, Dahn B, Diallo B, Formenty P, Fraser C, Gao GF, Garry RF, Goodfellow I, Günther S, Happi CT, Holmes EC, Kargbo B, Keïta S, Kellam P, Koopmans MPG, Kuhn JH, Loman NJ, Magassouba N, Naidoo D, Nichol ST, Nyenswah T, Palacios G, Pybus OG, Sabeti PC, Sall A, Ströher U, Wurie I, Suchard MA, Lemey P, Rambaut A. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 2017; 544:309-315. [PMID: 28405027 PMCID: PMC5712493 DOI: 10.1038/nature22040] [Citation(s) in RCA: 273] [Impact Index Per Article: 34.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 03/02/2017] [Indexed: 01/03/2023]
Abstract
The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.
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Affiliation(s)
- Gytis Dudas
- Institute of Evolutionary Biology, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Luiz Max Carvalho
- Institute of Evolutionary Biology, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Andrew J. Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK
- Flowminder Foundation, Stockholm, Sweden
| | - Guy Baele
- Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Nuno R. Faria
- Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK
| | - Daniel J. Park
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Jason T. Ladner
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
| | - Armando Arias
- Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
- National Veterinary Institute, Technical University of Denmark, Bülowsvej 27, 1870, Frederiksberg C, Denmark
| | - Danny Asogun
- Institute of Lassa Fever Research and Control, Irrua Specialist Teaching Hospital, Irrua, Nigeria
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
| | - Filip Bielejec
- Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Sarah L. Caddy
- Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
| | - Matthew Cotten
- Virus Genomics, Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA Rotterdam, the Netherlands
| | - Jonathan D’Ambrozio
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
| | - Simon Dellicour
- Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Antonino Di Caro
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
- National Institute for Infectious Diseases ”L. Spallanzani” – IRCCS, Via Portuense 292, 00149 Rome, Italy
| | - JosephW. Diclaro
- Naval Medical Research Unit 3, 3A Imtidad Ramses Street, Cairo, 11517, Egypt
| | - Sophie Duraffour
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
- Bernhard Nocht Institute for Tropical Medicine, 20359 Hamburg, Germany
| | - Michael J. Elmore
- National Infections Service, Public Health England, Porton Down, Salisbury, Wilts SP4 0JG, UK
| | | | - Ousmane Faye
- Institut Pasteur de Dakar, Arbovirus and Viral Hemorrhagic Fever Unit, 36 Avenue Pasteur, BP 220, Dakar, Sénégal
| | - Merle L. Gilbert
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
| | | | - Stephen Gire
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | | | - Andreas Gnirke
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Augustine Goba
- Viral Hemorrhagic Fever Program, Kenema Government Hospital, 1 Combema Road, Kenema, Sierra Leone
- Ministry of Health and Sanitation, 4th Floor Youyi Building, Freetown, Sierra Leone
| | - Donald S. Grant
- Viral Hemorrhagic Fever Program, Kenema Government Hospital, 1 Combema Road, Kenema, Sierra Leone
- Ministry of Health and Sanitation, 4th Floor Youyi Building, Freetown, Sierra Leone
| | - Bart L. Haagmans
- Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA Rotterdam, the Netherlands
| | - Julian A. Hiscox
- Institute of Infection and Global Health, University of Liverpool, Liverpool L69 2BE, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, UK
| | - Umaru Jah
- University of Makeni, Makeni, Sierra Leone
| | - Brima Kargbo
- Ministry of Health and Sanitation, 4th Floor Youyi Building, Freetown, Sierra Leone
| | - Jeffrey R. Kugelman
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
| | - Di Liu
- Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jia Lu
- Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
| | | | - Suzanne Mate
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
| | | | | | - Luke W. Meredith
- Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
- University of Makeni, Makeni, Sierra Leone
| | - James Qu
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Joshua Quick
- Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK
| | - Suzan D. Pas
- Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA Rotterdam, the Netherlands
| | - My VT Phan
- Virus Genomics, Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA Rotterdam, the Netherlands
| | - Georgios Pollakis
- Institute of Infection and Global Health, University of Liverpool, Liverpool L69 2BE, UK
| | - Chantal B. Reusken
- Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA Rotterdam, the Netherlands
| | - Mariano Sanchez-Lockhart
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
- University of Nebraska Medical Center, Omaha, NE, USA
| | | | - John S. Schieffelin
- Department of Pediatrics, Section of Infectious Diseases, New Orleans, LA 70112, USA
| | - Rachel S. Sealfon
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Etienne Simon-Loriere
- Institut Pasteur, Functional Genetics of Infectious Diseases Unit, 28 rue du Docteur Roux, 75724 Paris Cedex 15, France
- Génétique Fonctionelle des Maladies Infectieuses, CNRS URA3012, Paris 75015, France
| | - Saskia L. Smits
- Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA Rotterdam, the Netherlands
| | - Kilian Stoecker
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
- Bundeswehr Institute of Microbiology, Neuherbergstrasse 11, 80937 Munich, Germany
| | - Lucy Thorne
- Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
| | - Ekaete Alice Tobin
- Institute of Lassa Fever Research and Control, Irrua Specialist Teaching Hospital, Irrua, Nigeria
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
| | - Mohamed A. Vandi
- Viral Hemorrhagic Fever Program, Kenema Government Hospital, 1 Combema Road, Kenema, Sierra Leone
- Ministry of Health and Sanitation, 4th Floor Youyi Building, Freetown, Sierra Leone
| | - Simon J. Watson
- Virus Genomics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Kendra West
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Shannon Whitmer
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, Georgia, USA
| | - Michael R. Wiley
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
- University of Nebraska Medical Center, Omaha, NE, USA
| | - Sarah M. Winnicki
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Shirlee Wohl
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Roman Wölfel
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
- Bundeswehr Institute of Microbiology, Neuherbergstrasse 11, 80937 Munich, Germany
| | - Nathan L. Yozwiak
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Kristian G. Andersen
- The Scripps Research Institute, Department of Immunology and Microbial Science, La Jolla, CA 92037, USA
- Scripps Translational Science Institute, La Jolla, CA 92037, USA
| | - Sylvia O. Blyden
- Ministry of Social Welfare, Gender and Children’s Affairs, New Englandville, Freetown, Sierra Leone
| | - Fatorma Bolay
- Liberian Institute for Biomedical Research, Charlesville, Liberia
| | - MilesW. Carroll
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
- National Infections Service, Public Health England, Porton Down, Salisbury, Wilts SP4 0JG, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, UK
- University of Southampton, South General Hospital, Southampton SO16 6YD, UK
| | | | | | | | - Christophe Fraser
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - George F. Gao
- Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
| | - Robert F. Garry
- Department of Microbiology and Immunology, New Orleans, LA 70112, USA
| | - Ian Goodfellow
- Department of Pathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
- University of Makeni, Makeni, Sierra Leone
| | - Stephan Günther
- The European Mobile Laboratory Consortium, 20359 Hamburg, Germany
- Bernhard Nocht Institute for Tropical Medicine, 20359 Hamburg, Germany
| | - Christian T. Happi
- Department of Biological Sciences, Redeemer’s University, Ede, Osun State, Nigeria
- African Center of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Ede, Osun State, Nigeria
| | - Edward C. Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, the University of Sydney, Sydney, NSW 2006, Australia
| | - Brima Kargbo
- Ministry of Health and Sanitation, 4th Floor Youyi Building, Freetown, Sierra Leone
| | | | - Paul Kellam
- Virus Genomics, Wellcome Trust Sanger Institute, Hinxton, UK
- Division of Infectious Diseases, Imperial College Faculty of Medicine, London W2 1PG, UK
| | - Marion P. G. Koopmans
- Department of Viroscience, Erasmus University Medical Centre, P.O. Box 2040, 300 CA Rotterdam, the Netherlands
| | - Jens H. Kuhn
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, B-8200 Research Plaza, Fort Detrick, Frederick, MD 21702, USA
| | - Nicholas J. Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK
| | - N’Faly Magassouba
- Université Gamal Abdel Nasser de Conakry, Laboratoire des Fièvres Hémorragiques en Guinée, Conakry, Guinea
| | | | - Stuart T. Nichol
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, Georgia, USA
| | | | - Gustavo Palacios
- Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD 21702, USA
| | - Oliver G. Pybus
- Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK
| | - Pardis C. Sabeti
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
| | - Amadou Sall
- Institut Pasteur de Dakar, Arbovirus and Viral Hemorrhagic Fever Unit, 36 Avenue Pasteur, BP 220, Dakar, Sénégal
| | - Ute Ströher
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, Georgia, USA
| | - Isatta Wurie
- University of Sierra Leone, Freetown, Sierra Leone
| | - Marc A. Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA
- Department of Biomathematics David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology and Immunology, Rega Institute, KU Leuven – University of Leuven, Leuven, Belgium
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
- Centre for Immunology, Infection and Evolution, University of Edinburgh, King’s Buildings, Edinburgh, EH9 3FL, UK
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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49
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Landis MJ. Biogeographic Dating of Speciation Times Using Paleogeographically Informed Processes. Syst Biol 2017; 66:128-144. [PMID: 27155009 PMCID: PMC5837510 DOI: 10.1093/sysbio/syw040] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 04/28/2016] [Indexed: 11/13/2022] Open
Abstract
Standard models of molecular evolution cannot estimate absolute speciation times alone, and require external calibrations to do so, such as fossils. Because fossil calibration methods rely on the incomplete fossil record, a great number of nodes in the tree of life cannot be dated precisely. However, many major paleogeographical events are dated, and since biogeographic processes depend on paleogeographical conditions, biogeographic dating may be used as an alternative or complementary method to fossil dating. I demonstrate how a time-stratified biogeographic stochastic process may be used to estimate absolute divergence times by conditioning on dated paleogeographical events. Informed by the current paleogeographical literature, I construct an empirical dispersal graph using 25 areas and 26 epochs for the past 540 Ma of Earth's history. Simulations indicate biogeographic dating performs well so long as paleogeography imposes constraint on biogeographic character evolution. To gauge whether biogeographic dating may be of practical use, I analyzed the well-studied turtle clade (Testudines) to assess how well biogeographic dating fares when compared to fossil-calibrated dating estimates reported in the literature. Fossil-free biogeographic dating estimated the age of the most recent common ancestor of extant turtles to be from the Late Triassic, which is consistent with fossil-based estimates. Dating precision improves further when including a root node fossil calibration. The described model, paleogeographical dispersal graph, and analysis scripts are available for use with RevBayes.
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Affiliation(s)
- Michael J. Landis
- Department of Integrative Biology, University of California, Berkeley, CA 94720, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
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50
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Silvestro D, Zizka A, Bacon CD, Cascales-Miñana B, Salamin N, Antonelli A. Fossil biogeography: a new model to infer dispersal, extinction and sampling from palaeontological data. Philos Trans R Soc Lond B Biol Sci 2016; 371:20150225. [PMID: 26977065 PMCID: PMC4810818 DOI: 10.1098/rstb.2015.0225] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Methods in historical biogeography have revolutionized our ability to infer the evolution of ancestral geographical ranges from phylogenies of extant taxa, the rates of dispersals, and biotic connectivity among areas. However, extant taxa are likely to provide limited and potentially biased information about past biogeographic processes, due to extinction, asymmetrical dispersals and variable connectivity among areas. Fossil data hold considerable information about past distribution of lineages, but suffer from largely incomplete sampling. Here we present a new dispersal–extinction–sampling (DES) model, which estimates biogeographic parameters using fossil occurrences instead of phylogenetic trees. The model estimates dispersal and extinction rates while explicitly accounting for the incompleteness of the fossil record. Rates can vary between areas and through time, thus providing the opportunity to assess complex scenarios of biogeographic evolution. We implement the DES model in a Bayesian framework and demonstrate through simulations that it can accurately infer all the relevant parameters. We demonstrate the use of our model by analysing the Cenozoic fossil record of land plants and inferring dispersal and extinction rates across Eurasia and North America. Our results show that biogeographic range evolution is not a time-homogeneous process, as assumed in most phylogenetic analyses, but varies through time and between areas. In our empirical assessment, this is shown by the striking predominance of plant dispersals from Eurasia into North America during the Eocene climatic cooling, followed by a shift in the opposite direction, and finally, a balance in biotic interchange since the middle Miocene. We conclude by discussing the potential of fossil-based analyses to test biogeographic hypotheses and improve phylogenetic methods in historical biogeography.
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Affiliation(s)
- Daniele Silvestro
- Department of Biological and Environmental Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, Gothenburg 413 19, Sweden Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
| | - Alexander Zizka
- Department of Biological and Environmental Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, Gothenburg 413 19, Sweden
| | - Christine D Bacon
- Department of Biological and Environmental Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, Gothenburg 413 19, Sweden
| | | | - Nicolas Salamin
- Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
| | - Alexandre Antonelli
- Department of Biological and Environmental Sciences, University of Gothenburg, Carl Skottsbergs gata 22B, Gothenburg 413 19, Sweden Gothenburg Botanical Garden, Carl Skottsbergs gata 22A, Gothenburg 413 19, Sweden
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