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Middendorf PS, Zomer AL, Bergval IL, Jacobs-Reitsma WF, den Besten HMW, Abee T. Host associations of Campylobacter jejuni and Campylobacter coli isolates carrying the L-fucose or d-glucose utilization cluster. Int J Food Microbiol 2024; 425:110855. [PMID: 39191191 DOI: 10.1016/j.ijfoodmicro.2024.110855] [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: 03/16/2024] [Revised: 07/06/2024] [Accepted: 08/03/2024] [Indexed: 08/29/2024]
Abstract
Campylobacter was considered asaccharolytic, but is now known to carry saccharide metabolization pathways for L-fucose and d-glucose. We hypothesized that these clusters are beneficial for Campylobacter niche adaptation and may help establish human infection. We investigated the distribution of d-glucose and L-fucose clusters among ∼9600 C. jejuni and C. coli genomes of different isolation sources in the Netherlands, the United Kingdom, the United States of America and Finland. The L-fucose utilization cluster was integrated at the same location in all C. jejuni and C. coli genomes, and was flanked by the genes rpoB, rpoC, rspL, repsG and fusA, which are associated with functions in transcription as well as translation and in acquired drug resistance. In contrast, the flanking regions of the d-glucose utilization cluster were variable among the isolates, and integration sites were located within one of the three different 16S23S ribosomal RNA areas of the C. jejuni and C. coli genomes. In addition, we investigated whether acquisition of the L-fucose utilization cluster could be due to horizontal gene transfer between the two species and found three isolates for which this was the case: one C. jejuni isolate carrying a C. coli L-fucose cluster, and two C. coli isolates which carried a C. jejuni L-fucose cluster. Furthermore, L-fucose utilization cluster alignments revealed multiple frameshift mutations, most of which were commonly found in the non-essential genes for L-fucose metabolism, namely, Cj0484 and Cj0489. These findings support our hypothesis that the L-fucose cluster was integrated multiple times across the C. coli/C. jejuni phylogeny. Notably, association analysis using the C. jejuni isolates from the Netherlands showed a significant correlation between human C. jejuni isolates and C. jejuni isolates carrying the L-fucose utilization cluster. This correlation was even stronger when the Dutch isolates were combined with the isolates from the UK, the USA and Finland. No such correlations were observed for C. coli or for the d-glucose cluster for both species. This research provides insight into the spread and host associations of the L-fucose and d-glucose utilization clusters in C. jejuni and C. coli, and the potential benefits in human infection and/or proliferation in humans, conceivably after transmission from any reservoir.
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Affiliation(s)
- Pjotr S Middendorf
- Food Microbiology, Wageningen University and Research, Wageningen, Netherlands; National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Aldert L Zomer
- Faculty of Veterinary Medicine, Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, Netherlands; WHO Collaborating Center for Campylobacter/OIE Reference Laboratory for Campylobacteriosis, Utrecht, Netherlands
| | - Indra L Bergval
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | | | | | - Tjakko Abee
- Food Microbiology, Wageningen University and Research, Wageningen, Netherlands.
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Campbell AM, Gavilan RG, Abanto Marin M, Yang C, Hauton C, van Aerle R, Martinez-Urtaza J. Evolutionary dynamics of the successful expansion of pandemic Vibrio parahaemolyticus ST3 in Latin America. Nat Commun 2024; 15:7828. [PMID: 39244587 PMCID: PMC11380683 DOI: 10.1038/s41467-024-52159-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 08/27/2024] [Indexed: 09/09/2024] Open
Abstract
The underlying evolutionary mechanisms driving global expansions of pathogen strains are poorly understood. Vibrio parahaemolyticus is one of only two marine pathogens where variants have emerged in distinct climates globally. The success of a Vibrio parahaemolyticus clone (VpST3) in Latin America- the first spread identified outside its endemic region of tropical Asia- provided an invaluable opportunity to investigate mechanisms of VpST3 expansion into a distinct marine climate. A global collection of VpST3 isolates and novel Latin American isolates were used for evolutionary population genomics, pangenome analysis and combined with oceanic climate data. We found a VpST3 population (LatAm-VpST3) introduced in Latin America well before the emergence of this clone in India, previously considered the onset of the VpST3 epidemic. LatAm-VpST3 underwent successful adaptation to local conditions over its evolutionary divergence from Asian VpST3 isolates, to become dominant in Latin America. Selection signatures were found in genes providing resilience to the distinct marine climate. Core genome mutations and accessory gene presences that promoted survival over long dispersals or increased environmental fitness were associated with environmental conditions. These results provide novel insights into the global expansion of this successful V. parahaemolyticus clone into regions with different climate scenarios.
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Affiliation(s)
- Amy Marie Campbell
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Ronnie G Gavilan
- Centro Nacional de Salud Pública, Instituto Nacional de Salud, Lima, Peru
- Department of Genetics and Microbiology, Autonomous University of Barcelona, Barcelona, Spain
| | - Michel Abanto Marin
- Genomics and Bioinformatics Unit, Scientific and Technological Bioresource Nucleus (BIOREN), Universidad de La Frontera, Temuco, Chile
| | - Chao Yang
- The Center for Microbes, Development and Health, CAS Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Chris Hauton
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK
| | - Ronny van Aerle
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Jaime Martinez-Urtaza
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK.
- Department of Genetics and Microbiology, Autonomous University of Barcelona, Barcelona, Spain.
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3
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Bonetti Franceschi V, Volz E. Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2. Wellcome Open Res 2024; 9:85. [PMID: 39132669 PMCID: PMC11316176 DOI: 10.12688/wellcomeopenres.20704.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission. Methods We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics. Results We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein. Conclusions This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.
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Affiliation(s)
- Vinicius Bonetti Franceschi
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, England, W2 1PG, UK
| | - Erik Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, England, W2 1PG, UK
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4
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Chitwood MH, Colijn C, Yang C, Crudu V, Ciobanu N, Codreanu A, Kim J, Rancu I, Rhee K, Cohen T, Sobkowiak B. The recent rapid expansion of multidrug resistant Ural lineage Mycobacterium tuberculosis in Moldova. Nat Commun 2024; 15:2962. [PMID: 38580642 PMCID: PMC10997638 DOI: 10.1038/s41467-024-47282-9] [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: 11/29/2023] [Accepted: 03/26/2024] [Indexed: 04/07/2024] Open
Abstract
The projected trajectory of multidrug resistant tuberculosis (MDR-TB) epidemics depends on the reproductive fitness of circulating strains of MDR M. tuberculosis (Mtb). Previous efforts to characterize the fitness of MDR Mtb have found that Mtb strains of the Beijing sublineage (Lineage 2.2.1) may be more prone to develop resistance and retain fitness in the presence of resistance-conferring mutations than other lineages. Using Mtb genome sequences from all culture-positive cases collected over two years in Moldova, we estimate the fitness of Ural (Lineage 4.2) and Beijing strains, the two lineages in which MDR is concentrated in the country. We estimate that the fitness of MDR Ural strains substantially exceeds that of other susceptible and MDR strains, and we identify several mutations specific to these MDR Ural strains. Our findings suggest that MDR Ural Mtb has been transmitting efficiently in Moldova and poses a substantial risk of spreading further in the region.
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Affiliation(s)
- Melanie H Chitwood
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA.
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, 8888 University Drive West, Burnaby, BC, Canada
| | - Chongguang Yang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, No. 132 Outer Ring East Road, Guangzhou University Town Guangdong, Guangdong, PR China
| | - Valeriu Crudu
- Phthisiopneumology Institute, Strada Constantin Vârnav 13, Chisinau, Republic of Moldova
| | - Nelly Ciobanu
- Phthisiopneumology Institute, Strada Constantin Vârnav 13, Chisinau, Republic of Moldova
| | - Alexandru Codreanu
- Phthisiopneumology Institute, Strada Constantin Vârnav 13, Chisinau, Republic of Moldova
| | - Jaehee Kim
- Department of Computational Biology, Cornell University, 237 Tower Road, Ithaca, NY, USA
| | - Isabel Rancu
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Kyu Rhee
- Department of Medicine, Weill Cornell Medicine, 1300 York Ave, New York, NY, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA.
| | - Benjamin Sobkowiak
- Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College Street, New Haven, CT, USA
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5
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Volz E. Fitness, growth and transmissibility of SARS-CoV-2 genetic variants. Nat Rev Genet 2023; 24:724-734. [PMID: 37328556 DOI: 10.1038/s41576-023-00610-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2023] [Indexed: 06/18/2023]
Abstract
The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.
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Affiliation(s)
- Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
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6
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Helekal D, Keeling M, Grad YH, Didelot X. Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data. J R Soc Interface 2023; 20:20230074. [PMID: 37312496 PMCID: PMC10265023 DOI: 10.1098/rsif.2023.0074] [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: 02/15/2023] [Accepted: 05/22/2023] [Indexed: 06/15/2023] Open
Abstract
Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.
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Affiliation(s)
- David Helekal
- Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry, UK
| | - Matt Keeling
- Mathematics Institute and School of Life Sciences, University of Warwick, Coventry, UK
| | - Yonatan H. Grad
- Department of Immunology and Infectious Diseases, TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry, UK
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7
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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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8
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Rich SN, Richards V, Mavian C, Rife Magalis B, Grubaugh N, Rasmussen SA, Dellicour S, Vrancken B, Carrington C, Fisk-Hoffman R, Danso-Odei D, Chacreton D, Shapiro J, Seraphin MN, Hepp C, Black A, Dennis A, Trovão NS, Vandamme AM, Rasmussen A, Lauzardo M, Dean N, Salemi M, Prosperi M. Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions. JMIR Form Res 2023; 7:e39409. [PMID: 36848460 PMCID: PMC10131930 DOI: 10.2196/39409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/26/2022] [Accepted: 12/27/2022] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored. OBJECTIVE The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics-many of whom were actively engaged in the COVID-19 response-to discuss and report on the application of phylodynamic tools to inform pandemic responses. METHODS In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis. RESULTS We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks. CONCLUSIONS This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses.
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Affiliation(s)
- Shannan N Rich
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Veronica Richards
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Carla Mavian
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Brittany Rife Magalis
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Nathan Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
| | - Sonja A Rasmussen
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Simon Dellicour
- Spatial Epidemiology Lab, Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Bruxelles, Belgium
| | - Bram Vrancken
- Spatial Epidemiology Lab, Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Bruxelles, Belgium
| | - Christine Carrington
- Department of Preclinical Sciences, University of the West Indies, St Augustine, Trinidad and Tobago
| | - Rebecca Fisk-Hoffman
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Demi Danso-Odei
- Florida Department of Health in Alachua County, Gainesville, FL, United States
| | - Daniel Chacreton
- Division of Disease Control and Health Protection, Florida Department of Health, Tallahassee, FL, United States
| | - Jerne Shapiro
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
- Florida Department of Health in Alachua County, Gainesville, FL, United States
| | - Marie Nancy Seraphin
- Division of Infectious Diseases and Global Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Crystal Hepp
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, United States
- School of Informatics, Computing, and Cyber Systems, College of Engineering, Informatics, and Applied Sciences, Northern Arizona University, Flagstaff, AZ, United States
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, United States
| | - Allison Black
- Chan Zuckerberg Initiative, Redwood City, CA, United States
| | - Ann Dennis
- Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Nídia Sequeira Trovão
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, United States
| | - Anne-Mieke Vandamme
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
- Center for Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Angela Rasmussen
- Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, SK, Canada
| | - Michael Lauzardo
- Division of Infectious Diseases and Global Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Natalie Dean
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Biostatistics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Marco Salemi
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
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9
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Liu M, Chato C, Poon AFY. From components to communities: bringing network science to clustering for molecular epidemiology. Virus Evol 2023; 9:vead026. [PMID: 37187604 PMCID: PMC10175948 DOI: 10.1093/ve/vead026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 01/30/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Defining clusters of epidemiologically related infections is a common problem in the surveillance of infectious disease. A popular method for generating clusters is pairwise distance clustering, which assigns pairs of sequences to the same cluster if their genetic distance falls below some threshold. The result is often represented as a network or graph of nodes. A connected component is a set of interconnected nodes in a graph that are not connected to any other node. The prevailing approach to pairwise clustering is to map clusters to the connected components of the graph on a one-to-one basis. We propose that this definition of clusters is unnecessarily rigid. For instance, the connected components can collapse into one cluster by the addition of a single sequence that bridges nodes in the respective components. Moreover, the distance thresholds typically used for viruses like HIV-1 tend to exclude a large proportion of new sequences, making it difficult to train models for predicting cluster growth. These issues may be resolved by revisiting how we define clusters from genetic distances. Community detection is a promising class of clustering methods from the field of network science. A community is a set of nodes that are more densely inter-connected relative to the number of their connections to external nodes. Thus, a connected component may be partitioned into two or more communities. Here we describe community detection methods in the context of genetic clustering for epidemiology, demonstrate how a popular method (Markov clustering) enables us to resolve variation in transmission rates within a giant connected component of HIV-1 sequences, and identify current challenges and directions for further work.
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Affiliation(s)
- Molly Liu
- Department of Pathology and Laboratory Medicine, Western University, Dental Sciences Building, Rm. 4044, London, ON N6A 5C1, Canada
| | - Connor Chato
- Department of Pathology and Laboratory Medicine, Western University, Dental Sciences Building, Rm. 4044, London, ON N6A 5C1, Canada
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10
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A deep learning approach to real-time HIV outbreak detection using genetic data. PLoS Comput Biol 2022; 18:e1010598. [PMID: 36240224 PMCID: PMC9604978 DOI: 10.1371/journal.pcbi.1010598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 10/26/2022] [Accepted: 09/23/2022] [Indexed: 12/15/2022] Open
Abstract
Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.
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Didelot X, Parkhill J. A scalable analytical approach from bacterial genomes to epidemiology. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210246. [PMID: 35989600 PMCID: PMC9393561 DOI: 10.1098/rstb.2021.0246] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/17/2022] [Indexed: 12/21/2022] Open
Abstract
Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
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Affiliation(s)
- Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry CV4 7AL, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK
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12
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Nascimento FF, Ragonnet-Cronin M, Golubchik T, Danaviah S, Derache A, Fraser C, Volz E. Evaluating whole HIV-1 genome sequence for estimation of incidence and migration in a rural South African community. Wellcome Open Res 2022; 7:174. [PMID: 37333843 PMCID: PMC10276198 DOI: 10.12688/wellcomeopenres.17891.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2022] [Indexed: 09/21/2024] Open
Abstract
Background: South Africa has the largest number of people living with HIV (PLWHIV) in the world, with HIV prevalence and transmission patterns varying greatly between provinces. Transmission between regions is still poorly understood, but phylodynamics of HIV-1 evolution can reveal how many infections are attributable to contacts outside a given community. We analysed whole genome HIV-1 genetic sequences to estimate incidence and the proportion of transmissions between communities in Hlabisa, a rural South African community. Methods: We separately analysed HIV-1 for gag, pol, and env genes sampled from 2,503 PLWHIV. We estimated time-scaled phylogenies by maximum likelihood under a molecular clock model. Phylodynamic models were fitted to time-scaled trees to estimate transmission rates, effective number of infections, incidence through time, and the proportion of infections imported to Hlabisa. We also partitioned time-scaled phylogenies with significantly different distributions of coalescent times. Results: Phylodynamic analyses showed similar trends in epidemic growth rates between 1980 and 1990. Model-based estimates of incidence and effective number of infections were consistent across genes. Parameter estimates with gag were generally smaller than those estimated with pol and env. When estimating the proportions of new infections in Hlabisa from immigration or transmission from external sources, our posterior median estimates were 85% (95% credible interval (CI) = 78%-92%) for gag, 62% (CI = 40%-78%) for pol, and 77% (CI = 58%-90%) for env in 2015. Analysis of phylogenetic partitions by gene showed that most close global reference sequences clustered within a single partition. This suggests local evolving epidemics or potential unmeasured heterogeneity in the population. Conclusions: We estimated consistent epidemic dynamic trends for gag, pol and env genes using phylodynamic models. There was a high probability that new infections were not attributable to endogenous transmission within Hlabisa, suggesting high inter-connectedness between communities in rural South Africa.
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Affiliation(s)
| | | | | | - Siva Danaviah
- Africa Health Research Institute, Durban, South Africa
| | - Anne Derache
- Africa Health Research Institute, Durban, South Africa
| | | | - Erik Volz
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
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13
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Joseph SJ, Thomas Iv JC, Schmerer MW, Cartee J, St Cyr S, Schlanger K, Kersh EN, Raphael BH, Gernert KM. Global emergence and dissemination of Neisseria gonorrhoeae ST-9363 isolates with reduced susceptibility to azithromycin. Genome Biol Evol 2021; 14:6486421. [PMID: 34962987 DOI: 10.1093/gbe/evab287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2021] [Indexed: 11/12/2022] Open
Abstract
Neisseria gonorrhoeae multi-locus sequence type (ST) 9363 core-genogroup isolates have been associated with reduced azithromycin susceptibility (AZMrs) and show evidence of clonal expansion in the U.S. Here we analyze a global collection of ST-9363 core-genogroup genomes to shed light on the emergence and dissemination of this strain. The global population structure of ST-9363 core-genogroup falls into three lineages: Basal, European, and North American; with 32 clades within all lineages. Although, ST-9363 core-genogroup is inferred to have originated from Asia in the mid-19th century; we estimate the three modern lineages emerged from Europe in the late 1970s to early 1980s. The European lineage appears to have emerged and expanded from around 1986 to 1998, spreading into North America and Oceania in the mid-2000s with multiple introductions, along with multiple secondary reintroductions into Europe. Our results suggest two separate acquisition events of mosaic mtrR and mtrR promoter alleles: first during 2009-2011 and again during the 2012-2013 time, facilitating the clonal expansion of this core-genogroup with AZMrs in the U.S. By tracking phylodynamic evolutionary trajectories of clades that share distinct demography as well as population-based genomic statistics, we demonstrate how recombination and selective pressures in the mtrCDE efflux operon granted a fitness advantage to establish ST-9363 as a successful gonococcal lineage in the U.S. and elsewhere. Although it is difficult to pinpoint the exact timing and emergence of this young core-genogroup, it remains critically important to continue monitoring it, as it could acquire additional resistance markers.
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Affiliation(s)
- Sandeep J Joseph
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Jesse C Thomas Iv
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Matthew W Schmerer
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Jack Cartee
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Sancta St Cyr
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Karen Schlanger
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Ellen N Kersh
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Brian H Raphael
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
| | - Kim M Gernert
- Division of STD Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia-30329, USA
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14
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Helekal D, Ledda A, Volz E, Wyllie D, Didelot X. Bayesian inference of clonal expansions in a dated phylogeny. Syst Biol 2021; 71:1073-1087. [PMID: 34893904 PMCID: PMC9366454 DOI: 10.1093/sysbio/syab095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/23/2021] [Accepted: 11/29/2021] [Indexed: 11/16/2022] Open
Abstract
Microbial population genetics models often assume that all lineages are constrained by the same population size dynamics over time. However, many neutral and selective events can invalidate this assumption and can contribute to the clonal expansion of a specific lineage relative to the rest of the population. Such differential phylodynamic properties between lineages result in asymmetries and imbalances in phylogenetic trees that are sometimes described informally but which are difficult to analyze formally. To this end, we developed a model of how clonal expansions occur and affect the branching patterns of a phylogeny. We show how the parameters of this model can be inferred from a given dated phylogeny using Bayesian statistics, which allows us to assess the probability that one or more clonal expansion events occurred. For each putative clonal expansion event, we estimate its date of emergence and subsequent phylodynamic trajectory, including its long-term evolutionary potential which is important to determine how much effort should be placed on specific control measures. We demonstrate the applicability of our methodology on simulated and real data sets. Inference under our clonal expansion model can reveal important features in the evolution and epidemiology of infectious disease pathogens. [Clonal expansion; genomic epidemiology; microbial population genomics; phylodynamics.]
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Affiliation(s)
- David Helekal
- Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, United Kingdom
| | - Alice Ledda
- Healthcare Associated Infections and Antimicrobial Resistance Division, National Infection Service, Public Health England, United Kingdom
| | - Erik Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | - David Wyllie
- Field Service, East of England, National Infection Service, Public Health England, Cambridge, United Kingdom
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, United Kingdom
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15
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Large Evolutionary Rate Heterogeneity among and within HIV-1 Subtypes and CRFs. Viruses 2021; 13:v13091689. [PMID: 34578270 PMCID: PMC8473000 DOI: 10.3390/v13091689] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/19/2021] [Accepted: 08/23/2021] [Indexed: 01/06/2023] Open
Abstract
HIV-1 is a fast-evolving, genetically diverse virus presently classified into several groups and subtypes. The virus evolves rapidly because of an error-prone polymerase, high rates of recombination, and selection in response to the host immune system and clinical management of the infection. The rate of evolution is also influenced by the rate of virus spread in a population and nature of the outbreak, among other factors. HIV-1 evolution is thus driven by a range of complex genetic, social, and epidemiological factors that complicates disease management and prevention. Here, we quantify the evolutionary (substitution) rate heterogeneity among major HIV-1 subtypes and recombinants by analyzing the largest collection of HIV-1 genetic data spanning the widest possible geographical (100 countries) and temporal (1981–2019) spread. We show that HIV-1 substitution rates vary substantially, sometimes by several folds, both across the virus genome and between major subtypes and recombinants, but also within a subtype. Across subtypes, rates ranged 3.5-fold from 1.34 × 10−3 to 4.72 × 10−3 in env and 2.3-fold from 0.95 × 10−3 to 2.18 × 10−3 substitutions site−1 year−1 in pol. Within the subtype, 3-fold rate variation was observed in env in different human populations. It is possible that HIV-1 lineages in different parts of the world are operating under different selection pressures leading to substantial rate heterogeneity within and between subtypes. We further highlight how such rate heterogeneity can complicate HIV-1 phylodynamic studies, specifically, inferences on epidemiological linkage of transmission clusters based on genetic distance or phylogenetic data, and can mislead estimates about the timing of HIV-1 lineages.
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Didelot X, Geidelberg L, Volz EM. Model design for non-parametric phylodynamic inference and applications to pathogen surveillance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.01.18.427056. [PMID: 34426812 PMCID: PMC8382123 DOI: 10.1101/2021.01.18.427056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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 non-parametric 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 non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric 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. We demonstrate the flexibility and speed of this approach in a series of simulation experiments, and apply the methodology to reconstruct the previously described waves in the seventh pandemic of cholera. 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
| | - Lily Geidelberg
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
| | | | - Erik M Volz
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
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17
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Retchless AC, Chen A, Chang HY, Blain AE, McNamara LA, Mustapha MM, Harrison LH, Wang X. Using Neisseria meningitidis genomic diversity to inform outbreak strain identification. PLoS Pathog 2021; 17:e1009586. [PMID: 34003852 PMCID: PMC8177650 DOI: 10.1371/journal.ppat.1009586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 06/04/2021] [Accepted: 04/26/2021] [Indexed: 11/18/2022] Open
Abstract
Meningococcal disease is a life-threatening illness caused by the human-restricted bacterium Neisseria meningitidis. Outbreaks in the USA involve at least two cases in an organization or community caused by the same serogroup within three months. Genome comparisons, including phylogenetic analysis and quantification of genome distances can provide confirmatory evidence of pathogen transmission during an outbreak. Interpreting genome distances depends on understanding their distribution both among isolates from outbreaks and among those not from outbreaks. Here, we identify outbreak strains based on phylogenetic relationships among 141 N. meningitidis isolates collected from 28 outbreaks in the USA during 2010-2017 and 1516 non-outbreak isolates collected through contemporaneous meningococcal surveillance. We show that genome distance thresholds based on the maximum SNPs and allele distances among isolates in the phylogenetically defined outbreak strains are sufficient to separate most pairs of non-outbreak isolates into separate strains. Non-outbreak isolate pairs that could not be distinguished from each other based on genetic distances were concentrated in the clonal complexes CC11, CC103, and CC32. Within each of these clonal complexes, phylodynamic analysis identified a group of isolates with extremely low diversity, collected over several years and multiple states. Clusters of isolates with low genetic diversity could indicate increased pathogen transmission, potentially resulting in local outbreaks or nationwide clonal expansions.
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Affiliation(s)
- Adam C. Retchless
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Alex Chen
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - How-Yi Chang
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Amy E. Blain
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Lucy A. McNamara
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Mustapha M. Mustapha
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Lee H. Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Xin Wang
- Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
- * E-mail:
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18
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Host relatedness and landscape connectivity shape pathogen spread in the puma, a large secretive carnivore. Commun Biol 2021; 4:12. [PMID: 33398025 PMCID: PMC7782801 DOI: 10.1038/s42003-020-01548-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 11/25/2020] [Indexed: 01/29/2023] Open
Abstract
Urban expansion can fundamentally alter wildlife movement and gene flow, but how urbanization alters pathogen spread is poorly understood. Here, we combine high resolution host and viral genomic data with landscape variables to examine the context of viral spread in puma (Puma concolor) from two contrasting regions: one bounded by the wildland urban interface (WUI) and one unbounded with minimal anthropogenic development (UB). We found landscape variables and host gene flow explained significant amounts of variation of feline immunodeficiency virus (FIV) spread in the WUI, but not in the unbounded region. The most important predictors of viral spread also differed; host spatial proximity, host relatedness, and mountain ranges played a role in FIV spread in the WUI, whereas roads might have facilitated viral spread in the unbounded region. Our research demonstrates how anthropogenic landscapes can alter pathogen spread, providing a more nuanced understanding of host-pathogen relationships to inform disease ecology in free-ranging species.
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19
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Fountain-Jones NM, Appaw RC, Carver S, Didelot X, Volz E, Charleston M. Emerging phylogenetic structure of the SARS-CoV-2 pandemic. Virus Evol 2020; 6:veaa082. [PMID: 33335743 PMCID: PMC7717445 DOI: 10.1093/ve/veaa082] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Since spilling over into humans, SARS-CoV-2 has rapidly spread across the globe, accumulating significant genetic diversity. The structure of this genetic diversity and whether it reveals epidemiological insights are fundamental questions for understanding the evolutionary trajectory of this virus. Here, we use a recently developed phylodynamic approach to uncover phylogenetic structures underlying the SARS-CoV-2 pandemic. We find support for three SARS-CoV-2 lineages co-circulating, each with significantly different demographic dynamics concordant with known epidemiological factors. For example, Lineage C emerged in Europe with a high growth rate in late February, just prior to the exponential increase in cases in several European countries. Non-synonymous mutations that characterize Lineage C occur in functionally important gene regions responsible for viral replication and cell entry. Even though Lineages A and B had distinct demographic patterns, they were much more difficult to distinguish. Continuous application of phylogenetic approaches to track the evolutionary epidemiology of SARS-CoV-2 lineages will be increasingly important to validate the efficacy of control efforts and monitor significant evolutionary events in the future.
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Affiliation(s)
| | - Raima Carol Appaw
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
| | - Scott Carver
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry CV47AL, UK
| | - Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London W2 1PG, UK
| | - Michael Charleston
- School of Natural Sciences, University of Tasmania, Hobart, 7001, Australia
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