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Henríquez-Piskulich P, Hugall AF, Stuart-Fox D. A supermatrix phylogeny of the world's bees (Hymenoptera: Anthophila). Mol Phylogenet Evol 2024; 190:107963. [PMID: 37967640 DOI: 10.1016/j.ympev.2023.107963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/28/2023] [Accepted: 11/04/2023] [Indexed: 11/17/2023]
Abstract
The increasing availability of large molecular phylogenies has provided new opportunities to study the evolution of species traits, their origins and diversification, and biogeography; yet there are limited attempts to synthesise existing phylogenetic information for major insect groups. Bees (Hymenoptera: Anthophila) are a large group of insect pollinators that have a worldwide distribution, and a wide variation in ecology, morphology, and life-history traits, including sociality. For these reasons, as well as their major economic importance as pollinators, numerous molecular phylogenetic studies of family and genus-level relationships have been published, providing an opportunity to assemble a bee 'tree-of-life'. We used publicly available genetic sequence data, including phylogenomic data, reconciled to a taxonomic database, to produce a concatenated supermatrix phylogeny for the Anthophila comprising 4,586 bee species, representing 23% of species and 82% of genera. At family, subfamily, and tribe levels, support for expected relationships was robust, but between and within some genera relationships remain uncertain. Within families, sampling of genera ranged from 67 to 100% but species coverage was lower (17-41%). Our phylogeny mostly reproduces the relationships found in recent phylogenomic studies with a few exceptions. We provide a summary of these differences and the current state of molecular data available and its gaps. We discuss the advantages and limitations of this bee supermatrix phylogeny (available online at beetreeoflife.org), which may enable new insights into long standing questions about evolutionary drivers in bees, and potentially insects more generally.
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Affiliation(s)
| | - Andrew F Hugall
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia; Department of Sciences, Museums Victoria, Melbourne, Victoria, Australia.
| | - Devi Stuart-Fox
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
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Cintron R, Whitmer SLM, Moscoso E, Campbell EM, Kelly R, Talundzic E, Mobley M, Chiu KW, Shedroff E, Shankar A, Montgomery JM, Klena JD, Switzer WM. HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations. Viruses 2023; 15:2208. [PMID: 38005885 PMCID: PMC10675615 DOI: 10.3390/v15112208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
Hantaviruses zoonotically infect humans worldwide with pathogenic consequences and are mainly spread by rodents that shed aerosolized virus particles in urine and feces. Bioinformatics methods for hantavirus diagnostics, genomic surveillance and epidemiology are currently lacking a comprehensive approach for data sharing, integration, visualization, analytics and reporting. With the possibility of hantavirus cases going undetected and spreading over international borders, a significant reporting delay can miss linked transmission events and impedes timely, targeted public health interventions. To overcome these challenges, we built HantaNet, a standalone visualization engine for hantavirus genomes that facilitates viral surveillance and classification for early outbreak detection and response. HantaNet is powered by MicrobeTrace, a browser-based multitool originally developed at the Centers for Disease Control and Prevention (CDC) to visualize HIV clusters and transmission networks. HantaNet integrates coding gene sequences and standardized metadata from hantavirus reference genomes into three separate gene modules for dashboard visualization of phylogenetic trees, viral strain clusters for classification, epidemiological networks and spatiotemporal analysis. We used 85 hantavirus reference datasets from GenBank to validate HantaNet as a classification and enhanced visualization tool, and as a public repository to download standardized sequence data and metadata for building analytic datasets. HantaNet is a model on how to deploy MicrobeTrace-specific tools to advance pathogen surveillance, epidemiology and public health globally.
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Affiliation(s)
- Roxana Cintron
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
| | - Shannon L. M. Whitmer
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Evan Moscoso
- General Dynamics Information Technology, Atlanta, GA 30329, USA; (E.M.); (R.K.)
| | - Ellsworth M. Campbell
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
| | - Reagan Kelly
- General Dynamics Information Technology, Atlanta, GA 30329, USA; (E.M.); (R.K.)
| | - Emir Talundzic
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Melissa Mobley
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Kuo Wei Chiu
- General Dynamics Information Technology, Atlanta, GA 30329, USA; (E.M.); (R.K.)
| | - Elizabeth Shedroff
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - Anupama Shankar
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
| | - Joel M. Montgomery
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - John D. Klena
- Viral Special Pathogens Branch, Division of High Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (M.M.); (E.S.); (J.D.K.)
| | - William M. Switzer
- Laboratory Branch, Division of HIV Prevention, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA (A.S.); (W.M.S.)
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Truszkowski J, Perrigo A, Broman D, Ronquist F, Antonelli A. Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics. Syst Biol 2023; 72:1199-1206. [PMID: 37498209 PMCID: PMC10627553 DOI: 10.1093/sysbio/syad045] [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: 10/20/2022] [Revised: 06/22/2023] [Accepted: 07/11/2023] [Indexed: 07/28/2023] Open
Abstract
Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncertainty of inferences and the possibility of accommodating multiple aspects of evolutionary processes in the models that are used. Unfortunately, Bayesian methods are computationally expensive, and typical applications involve at most a few hundred sequences. This is problematic in the age of rapidly expanding genomic data and increasing scope of evolutionary analyses, forcing researchers to resort to less accurate but faster methods, such as maximum parsimony and maximum likelihood. Does this spell doom for Bayesian methods? Not necessarily. Here, we discuss some recently proposed approaches that could help scale up Bayesian analyses of evolutionary problems considerably. We focus on two particular aspects: online phylogenetics, where new data sequences are added to existing analyses, and alternatives to Markov chain Monte Carlo (MCMC) for scalable Bayesian inference. We identify 5 specific challenges and discuss how they might be overcome. We believe that online phylogenetic approaches and Sequential Monte Carlo hold great promise and could potentially speed up tree inference by orders of magnitude. We call for collaborative efforts to speed up the development of methods for real-time tree expansion through online phylogenetics.
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Affiliation(s)
- Jakub Truszkowski
- Department of Biological and Environmental Sciences, University of Gothenburg, P. O. Box 461, SE.405 30 Gothenburg, Sweden
- Gothenburg Global Biodiversity Centre, Box 461, 405 30 Gothenburg, Sweden
| | - Allison Perrigo
- Department of Biological and Environmental Sciences, University of Gothenburg, P. O. Box 461, SE.405 30 Gothenburg, Sweden
- Gothenburg Global Biodiversity Centre, Box 461, 405 30 Gothenburg, Sweden
| | - David Broman
- Department of Computer Science and Digital Futures, KTH Royal Institute of Technology, SE.100 44 Stockholm, Sweden
| | - Fredrik Ronquist
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, P. O. Box 50007, SE.104 05 Stockholm, Sweden
| | - Alexandre Antonelli
- Department of Biological and Environmental Sciences, University of Gothenburg, P. O. Box 461, SE.405 30 Gothenburg, Sweden
- Gothenburg Global Biodiversity Centre, Box 461, 405 30 Gothenburg, Sweden
- Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AE, UK
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3 RB, UK
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Gonzalez-Reiche AS. SARS-CoV-2 in low-income countries: the need for sustained genomic surveillance. Lancet Glob Health 2023; 11:e815-e816. [PMID: 37202013 DOI: 10.1016/s2214-109x(23)00197-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/13/2023] [Indexed: 05/20/2023]
Affiliation(s)
- Ana S Gonzalez-Reiche
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Didelot X, Franceschi V, Frost SDW, Dennis A, Volz EM. Model design for nonparametric phylodynamic inference and applications to pathogen surveillance. Virus Evol 2023; 9:vead028. [PMID: 37229349 PMCID: PMC10205094 DOI: 10.1093/ve/vead028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 04/17/2023] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, United Kingdom
| | - Vinicius Franceschi
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | | | - Ann Dennis
- Department of Medicine, University of North Carolina, USA
| | - Erik M Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
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Holt KE, Aanensen DM, Achtman M. Genomic population structures of microbial pathogens. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210230. [PMID: 35989608 PMCID: PMC9393556 DOI: 10.1098/rstb.2021.0230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/28/2022] [Indexed: 11/12/2022] Open
Affiliation(s)
- Kathryn E. Holt
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Mark Achtman
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
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