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Folkerts ML, Lemmer D, Pfeiffer A, Vasquez D, French C, Jones A, Nguyen M, Larsen B, Porter WT, Sheridan K, Bowers JR, Engelthaler DM. Methods for sequencing the pandemic: benefits of rapid or high-throughput processing. F1000Res 2021; 10:ISCB Comm J-48. [PMID: 35342619 PMCID: PMC8921685 DOI: 10.12688/f1000research.28352.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/11/2022] [Indexed: 12/21/2022] Open
Abstract
Genomic epidemiology has proven successful for real-time and retrospective monitoring of small and large-scale outbreaks. Here, we report two genomic sequencing and analysis strategies for rapid-turnaround or high-throughput processing of metagenomic samples. The rapid-turnaround method was designed to provide a quick phylogenetic snapshot of samples at the heart of active outbreaks, and has a total turnaround time of <48 hours from raw sample to analyzed data. The high-throughput method, first reported here for SARS-CoV2, was designed for semi-retrospective data analysis, and is both cost effective and highly scalable. Though these methods were developed and utilized for the SARS-CoV-2 pandemic response in Arizona, U.S, we envision their use for infectious disease epidemiology in the 21 st Century.
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Affiliation(s)
- Megan L. Folkerts
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Darrin Lemmer
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Ashlyn Pfeiffer
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Danielle Vasquez
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Chris French
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Amber Jones
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Marjorie Nguyen
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Brendan Larsen
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - W. Tanner Porter
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Krystal Sheridan
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Jolene R. Bowers
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - David M. Engelthaler
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
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102
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Folkerts ML, Lemmer D, Pfeiffer A, Vasquez D, French C, Jones A, Nguyen M, Larsen B, Porter WT, Sheridan K, Bowers JR, Engelthaler DM. Sequencing the pandemic: rapid and high-throughput processing and analysis of COVID-19 clinical samples for 21 st century public health. F1000Res 2021; 10:ISCB Comm J-48. [PMID: 35342619 PMCID: PMC8921685 DOI: 10.12688/f1000research.28352.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/20/2021] [Indexed: 11/04/2023] Open
Abstract
Genomic epidemiology has proven successful for real-time and retrospective monitoring of small and large-scale outbreaks. Here, we report two genomic sequencing and analysis strategies for rapid-turnaround or high-throughput processing of metagenomic samples. The rapid-turnaround method was designed to provide a quick phylogenetic snapshot of samples at the heart of active outbreaks, and has a total turnaround time of <48 hours from raw sample to analyzed data. The high-throughput method was designed for semi-retrospective data analysis, and is both cost effective and highly scalable. Though these methods were developed and utilized for the SARS-CoV-2 pandemic response in Arizona, U.S, and we envision their use for infectious disease epidemiology in the 21 st Century.
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Affiliation(s)
- Megan L. Folkerts
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Darrin Lemmer
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Ashlyn Pfeiffer
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Danielle Vasquez
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Chris French
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Amber Jones
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Marjorie Nguyen
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Brendan Larsen
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA
| | - W. Tanner Porter
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Krystal Sheridan
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - Jolene R. Bowers
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
| | - David M. Engelthaler
- Pathogen Genomics Division, Translational Genomics Research Institute, Flagstaff, AZ, 86005, USA
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103
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Bosworth A, Rickett NY, Dong X, Ng LFP, García-Dorival I, Matthews DA, Fletcher T, Jacobs M, Thomson EC, Carroll MW, Hiscox JA. Analysis of an Ebola virus disease survivor whose host and viral markers were predictive of death indicates the effectiveness of medical countermeasures and supportive care. Genome Med 2021; 13:5. [PMID: 33430949 PMCID: PMC7798020 DOI: 10.1186/s13073-020-00811-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 11/12/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Ebola virus disease (EVD) is an often-fatal infection where the effectiveness of medical countermeasures is uncertain. During the West African outbreak (2013-2016), several patients were treated with different types of anti-viral therapies including monoclonal antibody-based cocktails that had the potential to neutralise Ebola virus (EBOV). However, at the time, the efficacy of these therapies was uncertain. Given the scale of the outbreak, several clinical phenotypes came to the forefront including the ability of the same virus to cause recrudescence in the same patient-perhaps through persisting in immune privileged sites. Several key questions remained including establishing if monoclonal antibody therapy was effective in humans with severe EVD, whether virus escape mutants were selected during treatment, and what is the potential mechanism(s) of persistence. This was made possible through longitudinal samples taken from a UK patient with EVD. METHODS Several different sample types, plasma and cerebrospinal fluid, were collected and sequenced using Illumina-based RNAseq. Sequence reads were mapped both to EBOV and the human genome and differential gene expression analysis used to identify changes in the abundance of gene transcripts as infection progressed. Digital Cell Quantitation analysis was used to predict the immune phenotype in samples derived from blood. RESULTS The findings were compared to equivalent data from West African patients. The study found that both virus and host markers were predictive of a fatal outcome. This suggested that the extensive supportive care, and most likely the application of the medical countermeasure ZMab (a monoclonal antibody cocktail), contributed to survival of the UK patient. The switch from progression to a 'fatal' outcome to a 'survival' outcome could be seen in both the viral and host markers. The UK patient also suffered a recrudescence infection 10 months after the initial infection. Analysis of the sequencing data indicated that the virus entered a period of reduced or minimal replication, rather than other potential mechanisms of persistence-such as defective interfering genomes. CONCLUSIONS The data showed that comprehensive supportive care and the application of medical countermeasures are worth pursuing despite an initial unfavourable prognosis.
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Affiliation(s)
- Andrew Bosworth
- Public Health England, Manor Farm Road, Porton Down, Salisbury, UK
- Clinical Virology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
| | - Natasha Y Rickett
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Xiaofeng Dong
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Lisa F P Ng
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- Infectious Disease Horizontal Technology Centre (ID HTC), A*STAR, Singapore, Singapore
| | - Isabel García-Dorival
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - David A Matthews
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Tom Fletcher
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Michael Jacobs
- Department of Infection, Royal Free London NHS Foundation Trust, London, UK
| | - Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK.
| | - Miles W Carroll
- Public Health England, Manor Farm Road, Porton Down, Salisbury, UK.
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK.
- Nufield Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Julian A Hiscox
- Health Protection Research Unit in Emerging and Zoonotic Infections, National Institute for Health Research, Liverpool, UK.
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
- Infectious Disease Horizontal Technology Centre (ID HTC), A*STAR, Singapore, Singapore.
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104
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Volz EM, Carsten W, Grad YH, Frost SDW, Dennis AM, Didelot X. Identification of Hidden Population Structure in Time-Scaled Phylogenies. Syst Biol 2021; 69:884-896. [PMID: 32049340 PMCID: PMC8559910 DOI: 10.1093/sysbio/syaa009] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/09/2020] [Accepted: 01/23/2020] [Indexed: 11/13/2022] Open
Abstract
Population structure influences genealogical patterns, however, data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealized genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with the appearance and expansion of mutations conferring antimicrobial resistance. [Antimicrobial resistance; coalescent; HIV; population structure.].
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Affiliation(s)
- Erik M Volz
- Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place, W2 1PG London, UK
| | - Wiuf Carsten
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark
| | - Yonatan H Grad
- Department of Immunology and Infectious Diseases, TH Chan School of Public Health, Harvard University, 677 Huntington Ave, Boston, MA 02115, USA
| | - Simon D W Frost
- Department of Veterinary Medicine, University of Cambridge, Madingley Rd, Cambridge CB3 0ES, UK.,The Alan Turing Institute, 96 Euston Rd, London NW1 2DB, London, UK
| | - Ann M Dennis
- Department of Medicine, University of North Carolina Chapel Hill, 321 S Columbia St, Chapel Hill, NC 27516, USA
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
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105
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Harvey WT, Mulatti P, Fusaro A, Scolamacchia F, Zecchin B, Monne I, Marangon S. Spatiotemporal reconstruction and transmission dynamics during the 2016-17 H5N8 highly pathogenic avian influenza epidemic in Italy. Transbound Emerg Dis 2021; 68:37-50. [PMID: 31788978 PMCID: PMC8048528 DOI: 10.1111/tbed.13420] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/03/2019] [Accepted: 10/29/2019] [Indexed: 11/29/2022]
Abstract
Effective control of avian diseases in domestic populations requires understanding of the transmission dynamics facilitating viral emergence and spread. In 2016-17, Italy experienced a significant avian influenza epidemic caused by a highly pathogenic A(H5N8) virus, which affected domestic premises housing around 2.7 million birds, primarily in the north-eastern regions with the highest density of poultry farms (Lombardy, Emilia-Romagna and Veneto). We perform integrated analyses of genetic, spatiotemporal and host data within a Bayesian phylogenetic framework. Using continuous and discrete phylogeography, we estimate the locations of movements responsible for the spread and persistence of the epidemic. The information derived from these analyses on rates of transmission between regions through time can be used to assess the success of control measures. Using an approach based on phylogenetic-temporal distances between domestic cases, we infer the presence of cryptic wild bird-mediated transmission, information that can be used to complement existing epidemiological methods for distinguishing transmission within the domestic population from incursions across the wildlife-domestic interface, a common challenge in veterinary epidemiology. Spatiotemporal reconstruction of the epidemic reveals a highly skewed distribution of virus movements with a high proportion of shorter distance local movements interspersed with occasional long-distance dispersal events associated with wild birds. We also show how such inference be used to identify possible instances of human-mediated movements where distances between phylogenetically linked domestic cases are unusually high.
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Affiliation(s)
- William T. Harvey
- Boyd Orr Centre for Population and Ecosystem HealthInstitute of Biodiversity, Animal Health and Comparative MedicineCollege of Medical, Veterinary and Life SciencesUniversity of GlasgowGlasgowUK
| | - Paolo Mulatti
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | - Alice Fusaro
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | | | - Bianca Zecchin
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | - Isabella Monne
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
| | - Stefano Marangon
- Istituto Zooprofilattico Sperimentale delle VenezieLegnaro (Padua)Italy
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106
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Whitmer SLM, Lo MK, Sazzad HMS, Zufan S, Gurley ES, Sultana S, Amman B, Ladner JT, Rahman MZ, Doan S, Satter SM, Flora MS, Montgomery JM, Nichol ST, Spiropoulou CF, Klena JD. Inference of Nipah virus evolution, 1999-2015. Virus Evol 2021; 7:veaa062. [PMID: 34422315 PMCID: PMC7947586 DOI: 10.1093/ve/veaa062] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Despite near-annual human outbreaks of Nipah virus (NiV) disease in Bangladesh, typically due to individual spillover events from the local bat population, only twenty whole-genome NiV sequences exist from humans and ten from bats. NiV whole-genome sequences from annual outbreaks have been challenging to generate, primarily due to the low viral load in human throat swab and serum specimens. Here, we used targeted enrichment with custom NiV-specific probes and generated thirty-five additional unique full-length genomic sequences directly from human specimens and viral isolates. We inferred the temporal and geographic evolutionary history of NiV in Bangladesh and expanded a tool to visualize NiV spatio-temporal spread from a Bayesian continuous diffusion analysis. We observed that strains from Bangladesh segregated into two distinct clades that have intermingled geographically in Bangladesh over time and space. As these clades expanded geographically and temporally, we did not observe evidence for significant branch and site-specific selection, except for a single site in the Henipavirus L polymerase. However, the Bangladesh 1 and 2 clades are differentiated by mutations initially occurring in the polymerase, with additional mutations accumulating in the N, G, F, P, and L genes on external branches. Modeling the historic geographical and temporal spread demonstrates that while widespread, NiV does not exhibit significant genetic variation in Bangladesh. Thus, future public health measures should address whether NiV within in the bat population also exhibits comparable genetic variation, if zoonotic transmission results in a genetic bottleneck and if surveillance techniques are detecting only a subset of NiV.
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Affiliation(s)
- Shannon L M Whitmer
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
| | - Michael K Lo
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
| | - Hossain M S Sazzad
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
- Kirby Institute, University of New South Wales, Sydney, NSW, Australia
| | - Sara Zufan
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
| | - Emily S Gurley
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Sharmin Sultana
- Institute of Epidemiology, Disease Control and Research, Bangladesh
| | - Brian Amman
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
| | - Jason T Ladner
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Mohammed Ziaur Rahman
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Stephanie Doan
- The Center for Global Health, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329
| | - Syed M Satter
- Institute of Epidemiology, Disease Control and Research, Bangladesh
| | - Meerjady S Flora
- Institute of Epidemiology, Disease Control and Research, Bangladesh
| | - Joel M Montgomery
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
| | - Stuart T Nichol
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
| | - Christina F Spiropoulou
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
| | - John D Klena
- Viral Special Pathogens Branch, Centers for Disease Control and Prevention, 1600 Clifton Rd. NE, Atlanta, GA 30329, USA
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107
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Eden JS. Genome sequencing and its use in public health responses to COVID-19. MICROBIOLOGY AUSTRALIA 2021. [DOI: 10.1071/ma21012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Human history has been shaped by the heavy burden of infectious disease pandemics. Yet, despite the bitter lessons learned from history, even those in living memory such as the 1918 influenza pandemic and HIV/AIDS epidemic, COVID-19 stands unique in the sudden, immense health and economic impacts to the global human population. While the costs have been great, and the long-term consequences are still being revealed, the urgent need for action has also brought forward rapid developments and innovations to combat COVID-19 and better prepare us for future infectious disease outbreaks. One such area has been the widespread adoption of whole genome sequencing to inform public health responses. Genome sequencing during the COVID-19 pandemic has become key to tracking the spread of SARS-CoV-2 at all scales, to such a degree that terms such as genomics, mutations, variants and clusters are now common vernacular to politicians, health officials and the general public. This article provides a commentary on the genesis and evolution of SARS-CoV-2 genome sequencing, and its critical on-going role in the public health response to the COVID-19 pandemic.
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108
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Merrill RD, Bah Chabi AI, McIntyre E, Kouassi JV, Alleby MM, Codja C, Tante O, Primous Martial GT, Kone I, Ward S, Agbeko TT, Kakaı CG. An approach to integrate population mobility patterns and sociocultural factors in communicable disease preparedness and response. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2021; 8:1-11. [PMID: 38617731 PMCID: PMC11010577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Complex human movement patterns driven by a range of economic, health, social, and environmental factors influence communicable disease spread. Further, cross-border movement impacts disparate public health systems of neighboring countries, making an effective response to disease importation or exportation more challenging. Despite the array of quantitative techniques and social science approaches available to analyze movement patterns, there continues to be a dearth of methods within the applied public health setting to gather and use information about community-level mobility dynamics. Population Connectivity Across Borders (PopCAB) is a rapidly-deployable toolkit to characterize multisectoral movement patterns through community engagement using focus group discussions or key informant interviews, each with participatory mapping, and apply the results to tailor preparedness and response strategies. The Togo and Benin Ministries of Health (MOH), in collaboration with the Abidjan Lagos Corridor Organization and the US Centers for Disease Control and Prevention, adapted and applied PopCAB to inform cross-border preparedness and response strategies for multinational Lassa fever outbreaks. Initially, the team implemented binational, national-level PopCAB activities in March 2017, highlighting details about a circular migration pathway across northern Togo, Benin, and Nigeria. After applying those results to respond to a cross-border Lassa fever outbreak in February 2018, the team designed an expanded PopCAB initiative in April 2018. In eight days, they trained 54 MOH staff who implemented 21 PopCAB focus group discussions in 14 cities with 224 community-level participants representing six stakeholder groups. Using the newly-identified 167 points of interest and 176 routes associated with a circular migration pathway across Togo, Benin, and Nigeria, the Togo and Benin MOH refined their cross-border information sharing and collaboration processes for Lassa fever and other communicable diseases, selected health facilities with increased community connectivity for enhanced training, and identified techniques to better integrate traditional healers in surveillance and community education strategies. They also integrated the final toolkit in national- and district-level public health preparedness plans. Integrating PopCAB in public health practice to better understand and accommodate population movement patterns can help countries mitigate the international spread of disease in support of improved global health security and International Health Regulations requirements.
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Affiliation(s)
| | | | - Elvira McIntyre
- Perspecta and US Centers for Disease Control and Prevention, Atlanta, USA
| | | | | | | | | | | | - Idriss Kone
- Abidjan Lagos Corridor Organization, Benin, Nigeria
| | - Sarah Ward
- US Centers for Disease Control and Prevention, Atlanta, USA
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109
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Afolaranmi O, Salako O, Okunade K, James A, Fagbenro G. Integrating genomics education into Nigerian undergraduate medical training - A narrative review. JOURNAL OF CLINICAL SCIENCES 2021. [DOI: 10.4103/jcls.jcls_6_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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110
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O'Toole Á, Scher E, Underwood A, Jackson B, Hill V, McCrone JT, Colquhoun R, Ruis C, Abu-Dahab K, Taylor B, Yeats C, du Plessis L, Maloney D, Medd N, Attwood SW, Aanensen DM, Holmes EC, Pybus OG, Rambaut A. Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool. Virus Evol 2021; 7:veab064. [PMID: 34527285 DOI: 10.1093/ve/veab064/6315289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/09/2021] [Accepted: 07/05/2021] [Indexed: 05/21/2023] Open
Abstract
The response of the global virus genomics community to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been unprecedented, with significant advances made towards the 'real-time' generation and sharing of SARS-CoV-2 genomic data. The rapid growth in virus genome data production has necessitated the development of new analytical methods that can deal with orders of magnitude of more genomes than previously available. Here, we present and describe Phylogenetic Assignment of Named Global Outbreak Lineages (pangolin), a computational tool that has been developed to assign the most likely lineage to a given SARS-CoV-2 genome sequence according to the Pango dynamic lineage nomenclature scheme. To date, nearly two million virus genomes have been submitted to the web-application implementation of pangolin, which has facilitated the SARS-CoV-2 genomic epidemiology and provided researchers with access to actionable information about the pandemic's transmission lineages.
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Affiliation(s)
- Áine O'Toole
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - Emily Scher
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - Anthony Underwood
- The Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - Verity Hill
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - Rachel Colquhoun
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - Chris Ruis
- Department of Medicine, University of Cambridge, Cambridge CB2 0SP, UK
| | - Khalil Abu-Dahab
- The Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - Ben Taylor
- The Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - Corin Yeats
- The Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - Louis du Plessis
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SZ, UK
| | - Daniel Maloney
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - Nathan Medd
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
| | - Stephen W Attwood
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SZ, UK
| | - David M Aanensen
- The Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, Oxfordshire OX3 7LF, UK
| | - Edward C Holmes
- School of Life and Environmental Sciences and School of Medical Sciences, University of Sydney, Sydney, NSW 2006, Australia
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SZ, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93FL, UK
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111
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Abstract
The risk of emergence and spread of novel human pathogens originating from an animal reservoir has increased in the past decades. However, the unpredictable nature of disease emergence makes surveillance and preparedness challenging. Knowledge of general risk factors for emergence and spread, combined with local level data is needed to develop a risk-based methodology for early detection. This involves the implementation of the One Health approach, integrating human, animal and environmental health sectors, as well as social sciences, bioinformatics and more. Recent technical advances, such as metagenomic sequencing, will aid the rapid detection of novel pathogens on the human-animal interface.
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112
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Gascuel O, Steel M. A Darwinian Uncertainty Principle. Syst Biol 2020; 69:521-529. [PMID: 31432087 PMCID: PMC7188465 DOI: 10.1093/sysbio/syz054] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 08/15/2019] [Indexed: 02/04/2023] Open
Abstract
Reconstructing ancestral characters and traits along a phylogenetic tree is central to evolutionary biology. It is the key to understanding morphology changes among species, inferring ancestral biochemical properties of life, or recovering migration routes in phylogeography. The goal is 2-fold: to reconstruct the character state at the tree root (e.g., the region of origin of some species) and to understand the process of state changes along the tree (e.g., species flow between countries). We deal here with discrete characters, which are “unique,” as opposed to sequence characters (nucleotides or amino-acids), where we assume the same model for all the characters (or for large classes of characters with site-dependent models) and thus benefit from multiple information sources. In this framework, we use mathematics and simulations to demonstrate that although each goal can be achieved with high accuracy individually, it is generally impossible to accurately estimate both the root state and the rates of state changes along the tree branches, from the observed data at the tips of the tree. This is because the global rates of state changes along the branches that are optimal for the two estimation tasks have opposite trends, leading to a fundamental trade-off in accuracy. This inherent “Darwinian uncertainty principle” concerning the simultaneous estimation of “patterns” and “processes” governs ancestral reconstructions in biology. For certain tree shapes (typically speciation trees) the uncertainty of simultaneous estimation is reduced when more tips are present; however, for other tree shapes it does not (e.g., coalescent trees used in population genetics).
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Affiliation(s)
- Olivier Gascuel
- Unité Bioinformatique Evolutive, C3BI USR 3756, Institut Pasteur & CNRS, Paris, France
| | - Mike Steel
- Biomathematics Research Centre, University of Canterbury, Christchurch, New Zealand
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113
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Wells K, Lurgi M, Collins B, Lucini B, Kao RR, Lloyd AL, Frost SDW, Gravenor MB. Disease control across urban-rural gradients. J R Soc Interface 2020; 17:20200775. [PMID: 33292095 PMCID: PMC7811581 DOI: 10.1098/rsif.2020.0775] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 11/12/2020] [Indexed: 12/13/2022] Open
Abstract
Controlling the regional re-emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after its initial spread in ever-changing personal contact networks and disease landscapes is a challenging task. In a landscape context, contact opportunities within and between populations are changing rapidly as lockdown measures are relaxed and a number of social activities re-activated. Using an individual-based metapopulation model, we explored the efficacy of different control strategies across an urban-rural gradient in Wales, UK. Our model shows that isolation of symptomatic cases or regional lockdowns in response to local outbreaks have limited efficacy unless the overall transmission rate is kept persistently low. Additional isolation of non-symptomatic infected individuals, who may be detected by effective test-and-trace strategies, is pivotal to reducing the overall epidemic size over a wider range of transmission scenarios. We define an 'urban-rural gradient in epidemic size' as a correlation between regional epidemic size and connectivity within the region, with more highly connected urban populations experiencing relatively larger outbreaks. For interventions focused on regional lockdowns, the strength of such gradients in epidemic size increased with higher travel frequencies, indicating a reduced efficacy of the control measure in the urban regions under these conditions. When both non-symptomatic and symptomatic individuals are isolated or regional lockdown strategies are enforced, we further found the strongest urban-rural epidemic gradients at high transmission rates. This effect was reversed for strategies targeted at symptomatic individuals only. Our results emphasize the importance of test-and-trace strategies and maintaining low transmission rates for efficiently controlling SARS-CoV-2 spread, both at landscape scale and in urban areas.
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Affiliation(s)
- Konstans Wells
- Department of Biosciences, Swansea University, Swansea SA2 8PP, UK
| | - Miguel Lurgi
- Department of Biosciences, Swansea University, Swansea SA2 8PP, UK
| | - Brendan Collins
- Department of Public Health and Policy, University of Liverpool, Liverpool L69 3GB, UK
- Health and Social Services Group, Welsh Government, Cardiff CF10 3NQ, UK
| | - Biagio Lucini
- Department of Mathematics, Swansea University, Swansea SA2 8PP, UK
| | - Rowland R. Kao
- Royal (Dick) Veterinary School of Veterinary Studies, University of Edinburgh, Edinburgh EH25 9RG, UK
| | - Alun L. Lloyd
- Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA
| | - Simon D. W. Frost
- Microsoft Research Lab, Redmond, Washington, WA 98052, USA
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Mike B. Gravenor
- Swansea University Medical School, Swansea University, Swansea SA2 8PP, UK
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114
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Telionis PA, Corbett P, Venkatramanan S, Lewis B. Methods for Rapid Mobility Estimation to Support Outbreak Response. Health Secur 2020; 18:1-15. [PMID: 32078419 DOI: 10.1089/hs.2019.0101] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
When pressed for time, outbreak investigators often use homogeneous mixing models to model infectious diseases in data-poor regions. But recent outbreaks such as the 2014 Ebola outbreak in West Africa have shown the limitations of this approach in an era of increasing urbanization and connectivity. Both outbreak detection and predictive modeling depend on realistic estimates of human and disease mobility, but these data are difficult to acquire in a timely manner. This is especially true when dealing with an emerging outbreak in an under-resourced nation. Weighted travel networks with realistic estimates for population flows are often proprietary, expensive, or nonexistent. Here we propose a method for rapidly generating a mobility model from open-source data. As an example, we use road and river network data, along with population estimates, to construct a realistic model of human movement between health zones in the Democratic Republic of the Congo (DRC). Using these mobility data, we then fit an epidemic model to real-world surveillance data from the recent Ebola outbreak in the Nord Kivu region of the DRC to illustrate a potential use of the generated mobility estimation. In addition to providing a way for rapid risk estimation, this approach brings together novel techniques to merge diverse GIS datasets that can then be used to address issues that pertain to public health and global health security.
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Affiliation(s)
- Pyrros A Telionis
- Pyrros A. Telionis, PhD, is a postdoctoral research assistant, Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, and Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA. Patrick Corbett is an undergraduate research assistant; Srinivasan Venkatramanan, PhD, is a Research Scientist; and Bryan Lewis, PhD, is a Research Associate Professor; all in Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA
| | - Patrick Corbett
- Pyrros A. Telionis, PhD, is a postdoctoral research assistant, Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, and Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA. Patrick Corbett is an undergraduate research assistant; Srinivasan Venkatramanan, PhD, is a Research Scientist; and Bryan Lewis, PhD, is a Research Associate Professor; all in Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA
| | - Srinivasan Venkatramanan
- Pyrros A. Telionis, PhD, is a postdoctoral research assistant, Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, and Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA. Patrick Corbett is an undergraduate research assistant; Srinivasan Venkatramanan, PhD, is a Research Scientist; and Bryan Lewis, PhD, is a Research Associate Professor; all in Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA
| | - Bryan Lewis
- Pyrros A. Telionis, PhD, is a postdoctoral research assistant, Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA, and Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA. Patrick Corbett is an undergraduate research assistant; Srinivasan Venkatramanan, PhD, is a Research Scientist; and Bryan Lewis, PhD, is a Research Associate Professor; all in Population Health Sciences, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA
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115
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Deshpande JN, Kaltz O, Fronhofer EA. Host–parasite dynamics set the ecological theatre for the evolution of state‐ and context‐dependent dispersal in hosts. OIKOS 2020. [DOI: 10.1111/oik.07512] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Jhelam N. Deshpande
- ISEM, Univ. de Montpellier, CNRS, EPHE, IRD Montpellier France
- Indian Inst. of Science Education and Research (IISER) Pune Pune Maharashtra India
| | - Oliver Kaltz
- ISEM, Univ. de Montpellier, CNRS, EPHE, IRD Montpellier France
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116
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Vaiente MA, Scotch M. Going back to the roots: Evaluating Bayesian phylogeographic models with discrete trait uncertainty. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2020; 85:104501. [PMID: 32798768 PMCID: PMC7686256 DOI: 10.1016/j.meegid.2020.104501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/06/2020] [Accepted: 08/09/2020] [Indexed: 01/14/2023]
Abstract
Phylogeography is a popular way to analyze virus sequences annotated with discrete, epidemiologically-relevant, trait data. For applied public health surveillance, a key quantity of interest is often the state at the root of the inferred phylogeny. In epidemiological terms, this represents the geographic origin of the observed outbreak. Since determining the origin of an outbreak is often critical for public health intervention, it is prudent to understand how well phylogeographic models perform this root state classification task under various analytical scenarios. Specifically, we investigate how discrete state space and sequence data set influence the root state classification accuracy. We performed phylogeographic inference on several simulated DNA data sets while i) increasing the number of sequences and ii) increasing the total number of possible discrete trait values. We show that phylogeographic models tend to perform best at intermediate sequence data set sizes. Further, we demonstrate that a popular metric used for evaluation of phylogeographic models, the Kullback-Leibler (KL) divergence, both increases with discrete state space and data set sizes. Further, by modeling phylogeographic root state classification accuracy using logistic regression, we show that KL is not supported as a predictor of model accuracy, indicating its limited utility for assessing phylogeographic model performance on empirical data. These results suggest that relying solely on the KL metric may lead to artificially inflated support for models with finer discretization schemes and larger data set sizes. These results will be important for public health practitioners seeking to use phylogeographic models for applied infectious disease surveillance.
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Affiliation(s)
- Matteo A Vaiente
- Biodesign Center for Environmental Health Engineering, Arizona State University, 727 E. Tyler St, Tempe, AZ 85281, USA; College of Health Solutions, Arizona State University, 500 N 3rd St, Phoenix, AZ 85004, USA
| | - Matthew Scotch
- Biodesign Center for Environmental Health Engineering, Arizona State University, 727 E. Tyler St, Tempe, AZ 85281, USA; College of Health Solutions, Arizona State University, 500 N 3rd St, Phoenix, AZ 85004, USA.
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117
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Meredith LW, Hamilton WL, Warne B, Houldcroft CJ, Hosmillo M, Jahun AS, Curran MD, Parmar S, Caller LG, Caddy SL, Khokhar FA, Yakovleva A, Hall G, Feltwell T, Forrest S, Sridhar S, Weekes MP, Baker S, Brown N, Moore E, Popay A, Roddick I, Reacher M, Gouliouris T, Peacock SJ, Dougan G, Török ME, Goodfellow I. Rapid implementation of SARS-CoV-2 sequencing to investigate cases of health-care associated COVID-19: a prospective genomic surveillance study. THE LANCET. INFECTIOUS DISEASES 2020; 20:1263-1272. [PMID: 32679081 PMCID: PMC7806511 DOI: 10.1016/s1473-3099(20)30562-4] [Citation(s) in RCA: 280] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/16/2020] [Accepted: 06/22/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND The burden and influence of health-care associated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is unknown. We aimed to examine the use of rapid SARS-CoV-2 sequencing combined with detailed epidemiological analysis to investigate health-care associated SARS-CoV-2 infections and inform infection control measures. METHODS In this prospective surveillance study, we set up rapid SARS-CoV-2 nanopore sequencing from PCR-positive diagnostic samples collected from our hospital (Cambridge, UK) and a random selection from hospitals in the East of England, enabling sample-to-sequence in less than 24 h. We established a weekly review and reporting system with integration of genomic and epidemiological data to investigate suspected health-care associated COVID-19 cases. FINDINGS Between March 13 and April 24, 2020, we collected clinical data and samples from 5613 patients with COVID-19 from across the East of England. We sequenced 1000 samples producing 747 high-quality genomes. We combined epidemiological and genomic analysis of the 299 patients from our hospital and identified 35 clusters of identical viruses involving 159 patients. 92 (58%) of 159 patients had strong epidemiological links and 32 (20%) patients had plausible epidemiological links. These results were fed back to clinical, infection control, and hospital management teams, leading to infection-control interventions and informing patient safety reporting. INTERPRETATION We established real-time genomic surveillance of SARS-CoV-2 in a UK hospital and showed the benefit of combined genomic and epidemiological analysis for the investigation of health-care associated COVID-19. This approach enabled us to detect cryptic transmission events and identify opportunities to target infection-control interventions to further reduce health-care associated infections. Our findings have important implications for national public health policy as they enable rapid tracking and investigation of infections in hospital and community settings. FUNDING COVID-19 Genomics UK funded by the Department of Health and Social Care, UK Research and Innovation, and the Wellcome Sanger Institute.
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Affiliation(s)
- Luke W Meredith
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - William L Hamilton
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, UK
| | - Ben Warne
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, UK
| | | | - Myra Hosmillo
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Aminu S Jahun
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Martin D Curran
- Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge, UK
| | - Surendra Parmar
- Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge, UK
| | - Laura G Caller
- Department of Pathology, University of Cambridge, Cambridge, UK; Francis Crick Institute, London, UK
| | - Sarah L Caddy
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge Institute for Therapeutic Immunology and Infectious Disease, Cambridge, UK
| | - Fahad A Khokhar
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge Institute for Therapeutic Immunology and Infectious Disease, Cambridge, UK
| | - Anna Yakovleva
- Department of Pathology, University of Cambridge, Cambridge, UK
| | - Grant Hall
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | - Sally Forrest
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge Institute for Therapeutic Immunology and Infectious Disease, Cambridge, UK
| | - Sushmita Sridhar
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge Institute for Therapeutic Immunology and Infectious Disease, Cambridge, UK; Wellcome Sanger Institute, Hinxton, UK
| | - Michael P Weekes
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge Institute for Therapeutic Immunology and Infectious Disease, Cambridge, UK
| | - Stephen Baker
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge Institute for Therapeutic Immunology and Infectious Disease, Cambridge, UK
| | - Nicholas Brown
- Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge, UK
| | - Elinor Moore
- Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, UK
| | - Ashley Popay
- Field Epidemiology, Field Service, National Infection Service, Public Health England, Cambridge, UK
| | - Iain Roddick
- Field Epidemiology, Field Service, National Infection Service, Public Health England, Cambridge, UK
| | - Mark Reacher
- Field Epidemiology, Field Service, National Infection Service, Public Health England, Cambridge, UK
| | - Theodore Gouliouris
- Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, UK; Public Health England Clinical Microbiology and Public Health Laboratory, Cambridge, UK
| | - Sharon J Peacock
- Department of Medicine, University of Cambridge, Cambridge, UK; National Infection Service, Public Health England, London, UK
| | - Gordon Dougan
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge Institute for Therapeutic Immunology and Infectious Disease, Cambridge, UK
| | - M Estée Török
- Department of Medicine, University of Cambridge, Cambridge, UK; Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, UK.
| | - Ian Goodfellow
- Department of Pathology, University of Cambridge, Cambridge, UK.
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118
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Amplicon-Based Detection and Sequencing of SARS-CoV-2 in Nasopharyngeal Swabs from Patients With COVID-19 and Identification of Deletions in the Viral Genome That Encode Proteins Involved in Interferon Antagonism. Viruses 2020; 12:v12101164. [PMID: 33066701 PMCID: PMC7602519 DOI: 10.3390/v12101164] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 01/12/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Sequencing the viral genome as the outbreak progresses is important, particularly in the identification of emerging isolates with different pathogenic potential and to identify whether nucleotide changes in the genome will impair clinical diagnostic tools such as real-time PCR assays. Although single nucleotide polymorphisms and point mutations occur during the replication of coronaviruses, one of the biggest drivers in genetic change is recombination. This can manifest itself in insertions and/or deletions in the viral genome. Therefore, sequencing strategies that underpin molecular epidemiology and inform virus biology in patients should take these factors into account. A long amplicon/read length-based RT-PCR sequencing approach focused on the Oxford Nanopore MinION/GridION platforms was developed to identify and sequence the SARS-CoV-2 genome in samples from patients with or suspected of COVID-19. The protocol, termed Rapid Sequencing Long Amplicons (RSLAs) used random primers to generate cDNA from RNA purified from a sample from a patient, followed by single or multiplex PCRs to generate longer amplicons of the viral genome. The base protocol was used to identify SARS-CoV-2 in a variety of clinical samples and proved sensitive in identifying viral RNA in samples from patients that had been declared negative using other nucleic acid-based assays (false negative). Sequencing the amplicons revealed that a number of patients had a proportion of viral genomes with deletions.
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119
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Karcher MD, Carvalho LM, Suchard MA, Dudas G, Minin VN. Estimating effective population size changes from preferentially sampled genetic sequences. PLoS Comput Biol 2020; 16:e1007774. [PMID: 33044955 PMCID: PMC7580988 DOI: 10.1371/journal.pcbi.1007774] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 10/22/2020] [Accepted: 03/05/2020] [Indexed: 12/02/2022] Open
Abstract
Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa. Estimating changes in the number of individuals in a given population is a challenging problem in some settings. For example, estimating population size trajectories of the number of people infected by a pathogen (e.g., Influenza virus) is a difficult problem, because many infections in a large population remain unobserved/hidden. One indirect way of assessing population size changes is to take a sample of individuals from the population of interest and analyze genetic sequences from these individuals (e.g., Influenza virus genomes). Intuitively, genetic data is informative about population size changes, because genetic diversity increases/decreases together with the population size. However, if we sample more individuals when the population size increases and less when it decreases, this strategy produces biased results. To avoid this bias, we propose a method that explicitly and flexibly models potential dependency of genetic sequence sampling on the population size. An added bonus of this new modeling framework is more precise estimation of population size changes. We demonstrate strengths of our new methodology on simulated data and on genetic sequences of Influenza and Ebola viruses.
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Affiliation(s)
- Michael D. Karcher
- Department of Statistics, University of Washington, Seattle, Washington, U.S.A.
| | | | - Marc A. Suchard
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, U.S.A.
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, U.S.A.
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, California, U.S.A.
| | - Gytis Dudas
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
- Gothenburg Global Biodiversity Centre (GGBC), Gothenburg, Sweden
| | - Vladimir N. Minin
- Department of Statistics, University of California, Irvine, California, U.S.A.
- * E-mail:
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120
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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: 88] [Impact Index Per Article: 22.0] [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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121
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Puenpa J, Suwannakarn K, Chansaenroj J, Nilyanimit P, Yorsaeng R, Auphimai C, Kitphati R, Mungaomklang A, Kongklieng A, Chirathaworn C, Wanlapakorn N, Poovorawan Y. Molecular epidemiology of the first wave of severe acute respiratory syndrome coronavirus 2 infection in Thailand in 2020. Sci Rep 2020; 10:16602. [PMID: 33024144 PMCID: PMC7538975 DOI: 10.1038/s41598-020-73554-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 09/18/2020] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease 2019 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major global concern. Several SARS-CoV-2 gene mutations have been reported. In the current study associations between SARS-CoV-2 gene variation and exposure history during the first wave of the outbreak in Thailand between January and May 2020 were investigated. Forty samples were collected at different time points during the outbreak, and parts of the SARS-CoV-2 genome sequence were used to assess genomic variation patterns. The phylogenetics of the 40 samples were clustered into L, GH, GR, O and T types. T types were predominant in Bangkok during the first local outbreak centered at a boxing stadium and entertainment venues in March 2020. Imported cases were infected with various types, including L, GH, GR and O. In southern Thailand introductions of different genotypes were identified at different times. No clinical parameters were significantly associated with differences in genotype. The results indicated local transmission (type T, Spike protein (A829T)) and imported cases (types L, GH, GR and O) during the first wave in Thailand. Genetic and epidemiological data may contribute to national policy formulation, transmission tracking and the implementation of measures to control viral spread.
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Affiliation(s)
- Jiratchaya Puenpa
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Kamol Suwannakarn
- Department of Microbiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Jira Chansaenroj
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Pornjarim Nilyanimit
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Ritthideach Yorsaeng
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chompoonut Auphimai
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Rungrueng Kitphati
- Institute for Urban Disease Control and Prevention, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
| | - Anek Mungaomklang
- Institute for Urban Disease Control and Prevention, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
| | - Amornmas Kongklieng
- Institute for Urban Disease Control and Prevention, Department of Disease Control, Ministry of Public Health, Bangkok, Thailand
| | - Chintana Chirathaworn
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Nasamon Wanlapakorn
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Academic Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yong Poovorawan
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
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122
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Baele G, Gill MS, Lemey P, Suchard MA. Hamiltonian Monte Carlo sampling to estimate past population dynamics using the skygrid coalescent model in a Bayesian phylogenetics framework. Wellcome Open Res 2020; 5:53. [PMID: 32923688 PMCID: PMC7463299 DOI: 10.12688/wellcomeopenres.15770.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2020] [Indexed: 11/20/2022] Open
Abstract
Nonparametric coalescent-based models are often employed to infer past population dynamics over time. Several of these models, such as the skyride and skygrid models, are equipped with a block-updating Markov chain Monte Carlo sampling scheme to efficiently estimate model parameters. The advent of powerful computational hardware along with the use of high-performance libraries for statistical phylogenetics has, however, made the development of alternative estimation methods feasible. We here present the implementation and performance assessment of a Hamiltonian Monte Carlo gradient-based sampler to infer the parameters of the skygrid model. The skygrid is a popular and flexible coalescent-based model for estimating population dynamics over time and is available in BEAST 1.10.5, a widely-used software package for Bayesian pylogenetic and phylodynamic analysis. Taking into account the increased computational cost of gradient evaluation, we report substantial increases in effective sample size per time unit compared to the established block-updating sampler. We expect gradient-based samplers to assume an increasingly important role for different classes of parameters typically estimated in Bayesian phylogenetic and phylodynamic analyses.
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Affiliation(s)
- Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Marc A Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, 695 Charles E. Young Drive, Los Angeles, California, 90095-1766, USA
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123
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Dong X, Munoz-Basagoiti J, Rickett NY, Pollakis G, Paxton WA, Günther S, Kerber R, Ng LFP, Elmore MJ, Magassouba N, Carroll MW, Matthews DA, Hiscox JA. Variation around the dominant viral genome sequence contributes to viral load and outcome in patients with Ebola virus disease. Genome Biol 2020; 21:238. [PMID: 32894206 PMCID: PMC7475720 DOI: 10.1186/s13059-020-02148-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 08/17/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Viral load is a major contributor to outcome in patients with Ebola virus disease (EVD), with high values leading to a fatal outcome. Evidence from the 2013-2016 Ebola virus (EBOV) outbreak indicated that different genotypes of the virus can have different phenotypes in patients. Additionally, due to the error-prone nature of viral RNA synthesis in an individual patient, the EBOV genome exists around a dominant viral genome sequence. The minor variants within a patient may contribute to the overall phenotype in terms of viral protein function. To investigate the effects of these minor variants, blood samples from patients with acute EVD were deeply sequenced. RESULTS We examine the minor variant frequency between patients with acute EVD who survived infection with those who died. Non-synonymous differences in viral proteins were identified that have implications for viral protein function. The greatest frequency of substitution was identified at three codon sites in the L gene-which encodes the viral RNA-dependent RNA polymerase (RdRp). Recapitulating this in an assay for virus replication, these substitutions result in aberrant viral RNA synthesis and correlate with patient outcome. CONCLUSIONS Together, these findings support the notion that in patients who survived EVD, in some cases, the genetic variability of the virus resulted in deleterious mutations that affected viral protein function, leading to reduced viral load. Such mutations may also lead to persistent strains of the virus and be associated with recrudescent infections.
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Affiliation(s)
- Xiaofeng Dong
- Institute for Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Jordana Munoz-Basagoiti
- Institute for Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK
| | - Natasha Y. Rickett
- Institute for Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK
| | - Georgios Pollakis
- Institute for Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK
| | - William A. Paxton
- Institute for Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK
| | - Stephan Günther
- Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Romy Kerber
- Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Lisa F. P. Ng
- Institute for Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK
- Singapore Immunology Network, A*STAR, Singapore, Singapore
| | | | - N’faly Magassouba
- Laboratoire des fièvres hémorragiques en Guinée, Université Gamal Abdel Nasser de Conakry, Conakry, Guinea
| | - Miles W. Carroll
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK
- Public Health England, Salisbury, UK
| | - David A. Matthews
- School of Cellular and Molecular Medicine, University of Bristol, Singapore, Singapore
| | - Julian A. Hiscox
- Institute for Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK
- Singapore Immunology Network, A*STAR, Singapore, Singapore
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124
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Whitfield ZJ, Prasad AN, Ronk AJ, Kuzmin IV, Ilinykh PA, Andino R, Bukreyev A. Species-Specific Evolution of Ebola Virus during Replication in Human and Bat Cells. Cell Rep 2020; 32:108028. [PMID: 32814037 PMCID: PMC7434439 DOI: 10.1016/j.celrep.2020.108028] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 05/12/2020] [Accepted: 07/22/2020] [Indexed: 12/13/2022] Open
Abstract
Ebola virus (EBOV) causes a severe, often fatal disease in humans and nonhuman primates. Within the past decade, EBOV has caused two large and difficult-to-control outbreaks, one of which recently ended in the Democratic Republic of the Congo. Bats are the likely reservoir of EBOV, but little is known of their relationship with the virus. We perform serial passages of EBOV in human and bat cells and use circular sequencing to compare the short-term evolution of the virus. Virus populations passaged in bat cells have sequence markers indicative of host RNA editing enzyme activity, including evidence for ADAR editing of the EBOV glycoprotein. Multiple regions in the EBOV genome appear to have undergone adaptive evolution when passaged in bat and human cells. Individual mutated viruses are rescued and characterized. Our results provide insight into the host species-specific evolution of EBOV and highlight the adaptive flexibility of the virus.
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Affiliation(s)
- Zachary J Whitfield
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Abhishek N Prasad
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA; Galveston National Laboratory, University of Texas Medical Branch, Galveston, TX, USA
| | - Adam J Ronk
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA; Galveston National Laboratory, University of Texas Medical Branch, Galveston, TX, USA
| | - Ivan V Kuzmin
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA; Galveston National Laboratory, University of Texas Medical Branch, Galveston, TX, USA
| | - Philipp A Ilinykh
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA; Galveston National Laboratory, University of Texas Medical Branch, Galveston, TX, USA
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA.
| | - Alexander Bukreyev
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA; Department Microbiology & Immunology, University of Texas Medical Branch, Galveston, TX, USA; Galveston National Laboratory, University of Texas Medical Branch, Galveston, TX, USA.
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125
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Franz E, Rotariu O, Lopes BS, MacRae M, Bono JL, Laing C, Gannon V, Söderlund R, van Hoek AHAM, Friesema I, French NP, George T, Biggs PJ, Jaros P, Rivas M, Chinen I, Campos J, Jernberg C, Gobius K, Mellor GE, Chandry PS, Perez-Reche F, Forbes KJ, Strachan NJC. Phylogeographic Analysis Reveals Multiple International transmission Events Have Driven the Global Emergence of Escherichia coli O157:H7. Clin Infect Dis 2020; 69:428-437. [PMID: 30371758 DOI: 10.1093/cid/ciy919] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 10/28/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Shiga toxin-producing Escherchia coli (STEC) O157:H7 is a zoonotic pathogen that causes numerous food and waterborne disease outbreaks. It is globally distributed, but its origin and the temporal sequence of its geographical spread are unknown. METHODS We analyzed whole-genome sequencing data of 757 isolates from 4 continents, and performed a pan-genome analysis to identify the core genome and, from this, extracted single-nucleotide polymorphisms. A timed phylogeographic analysis was performed on a subset of the isolates to investigate its worldwide spread. RESULTS The common ancestor of this set of isolates occurred around 1890 (1845-1925) and originated from the Netherlands. Phylogeographic analysis identified 34 major transmission events. The earliest were predominantly intercontinental, moving from Europe to Australia around 1937 (1909-1958), to the United States in 1941 (1921-1962), to Canada in 1960 (1943-1979), and from Australia to New Zealand in 1966 (1943-1982). This pre-dates the first reported human case of E. coli O157:H7, which was in 1975 from the United States. CONCLUSIONS Inter- and intra-continental transmission events have resulted in the current international distribution of E. coli O157:H7, and it is likely that these events were facilitated by animal movements (eg, Holstein Friesian cattle). These findings will inform policy on action that is crucial to reduce the further spread of E. coli O157:H7 and other (emerging) STEC strains globally.
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Affiliation(s)
- Eelco Franz
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, The Netherlands
| | - Ovidiu Rotariu
- School of Biological Sciences, The University of Aberdeen, United Kingdom
| | - Bruno S Lopes
- School of Medicine, Medical Sciences & Nutrition, The University of Aberdeen, United Kingdom
| | - Marion MacRae
- School of Medicine, Medical Sciences & Nutrition, The University of Aberdeen, United Kingdom
| | - James L Bono
- United States Department of Agriculture, Agricultural Research Service, US Meat Animal Research Center, Clay Center, Nebraska
| | - Chad Laing
- National Microbiology Laboratory, Public Health Agency of Canada, Lethbridge, Alberta
| | - Victor Gannon
- National Microbiology Laboratory, Public Health Agency of Canada, Lethbridge, Alberta
| | | | - Angela H A M van Hoek
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, The Netherlands
| | - Ingrid Friesema
- National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, The Netherlands
| | - Nigel P French
- Molecular EpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - Tessy George
- Molecular EpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - Patrick J Biggs
- Molecular EpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - Patricia Jaros
- Molecular EpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | - Marta Rivas
- Instituto Nacional de Enfermedades Infecciosas, Administracion Nacional del Laboratorios et Institutos de Salud "Dr Carlos G. Malbrán," Buenos Aires, Argentina
| | - Isabel Chinen
- Instituto Nacional de Enfermedades Infecciosas, Administracion Nacional del Laboratorios et Institutos de Salud "Dr Carlos G. Malbrán," Buenos Aires, Argentina
| | - Josefina Campos
- Instituto Nacional de Enfermedades Infecciosas, Administracion Nacional del Laboratorios et Institutos de Salud "Dr Carlos G. Malbrán," Buenos Aires, Argentina
| | - Cecilia Jernberg
- Department of Microbiology, The Public Health Agency of Sweden, Stockholm
| | - Kari Gobius
- The Commonwealth Scientific and Industrial Research Organisation Agriculture and Food, Werribee, Victoria, Australia
| | - Glen E Mellor
- The Commonwealth Scientific and Industrial Research Organisation Agriculture and Food, Werribee, Victoria, Australia
| | - P Scott Chandry
- The Commonwealth Scientific and Industrial Research Organisation Agriculture and Food, Werribee, Victoria, Australia
| | - Francisco Perez-Reche
- Institute of Complex Systems and Mathematical Biology, SUPA, School of Natural and Computing Sciences, University of Aberdeen, United Kingdom
| | - Ken J Forbes
- School of Medicine, Medical Sciences & Nutrition, The University of Aberdeen, United Kingdom
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126
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Jara M, Rasmussen DA, Corzo CA, Machado G. Porcine reproductive and respiratory syndrome virus dissemination across pig production systems in the United States. Transbound Emerg Dis 2020; 68:667-683. [PMID: 32657491 DOI: 10.1111/tbed.13728] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/25/2020] [Accepted: 07/08/2020] [Indexed: 12/16/2022]
Abstract
Porcine reproductive and respiratory syndrome virus (PRRSV) remains widespread in the North American pig population. Despite improvements in virus characterization, it is unclear whether PRRSV infections are a product of viral circulation within production systems (local) or across production systems (external). Here, we examined the local and external dissemination dynamics of PRRSV and the processes facilitating its spread in three production systems. Overall, PRRSV genetic diversity has declined since 2018, while phylodynamic results support frequent external transmission. We found that PRRSV dissemination predominantly occurred mostly through transmission between farms of different production companies for several months, especially from November until May, a timeframe already established as PRRSV season. Although local PRRSV dissemination occurred mainly through regular pig flow (from sow to nursery and then to finisher farms), an important flux of PRRSV dissemination also occurred in the opposite direction, from finisher to sow and nursery farms, highlighting the importance of downstream farms as sources of the virus. Our results also showed that farms with pig densities of 500 to 1,000 pig/km2 and farms located at a range within 0.5 km and 0.7 km from major roads were more likely to be infected by PRRSV, whereas farms at an elevation of 41 to 61 meters and surrounded by denser vegetation were less likely to be infected, indicating their role as dissemination barriers. In conclusion, our results demonstrate that external dissemination was intense, and reinforce the importance of farm proximity on PRRSV spread. Thus, consideration of farm location, geographic characteristics and animal densities across production systems may help to forecast PRRSV collateral dissemination.
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Affiliation(s)
- Manuel Jara
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
| | - David A Rasmussen
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Cesar A Corzo
- Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, St Paul, MN, USA
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
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127
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Phan MV, Murad SD, van der Eijk AA, Metselaar HJ, Hartog H, Harinck F, GeurtsvanKessel CH, Molenkamp R, Cotten M, Koopmans MP. Genomic sequence of yellow fever virus from a Dutch traveller returning from the Gambia-Senegal region, the Netherlands, November 2018. ACTA ACUST UNITED AC 2020; 24. [PMID: 30696531 PMCID: PMC6351999 DOI: 10.2807/1560-7917.es.2019.24.4.1800684] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
In November 2018, yellow fever was diagnosed in a Dutch traveller returning from a bicycle tour in the Gambia-Senegal region. A complete genome sequence of yellow fever virus (YFV) from the case was generated and clustered phylogenetically with YFV from the Gambia and Senegal, ruling out importation into the Netherlands from recent outbreaks in Brazil or Angola. We emphasise the need for increased public awareness of YFV vaccination before travelling to endemic countries.
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Affiliation(s)
- My Vt Phan
- Department of Viroscience, Erasmus MC, Rotterdam, the Netherlands
| | - Sarwa Darwish Murad
- Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, the Netherlands
| | | | - Herold J Metselaar
- Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, the Netherlands
| | - Hermien Hartog
- Division of Hepato-Pancreato-Biliary and Transplant Surgery, Department of Surgery, Erasmus MC, Rotterdam, the Netherlands
| | - Femme Harinck
- Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, the Netherlands
| | | | | | - Matthew Cotten
- Department of Viroscience, Erasmus MC, Rotterdam, the Netherlands
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128
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Kamau E, Otieno JR, Murunga N, Oketch JW, Ngoi JM, de Laurent ZR, Mwema A, Nyiro JU, Agoti CN, Nokes DJ. Genomic epidemiology and evolutionary dynamics of respiratory syncytial virus group B in Kilifi, Kenya, 2015-17. Virus Evol 2020; 6:veaa050. [PMID: 32913665 PMCID: PMC7474930 DOI: 10.1093/ve/veaa050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Respiratory syncytial virus (RSV) circulates worldwide, occurring seasonally in communities, and is a leading cause of acute respiratory illness in young children. There is paucity of genomic data from purposively sampled populations by which to investigate evolutionary dynamics and transmission patterns of RSV. Here we present an analysis of 295 RSV group B (RSVB) genomes from Kilifi, coastal Kenya, sampled from individuals seeking outpatient care in nine health facilities across a defined geographical area (∼890 km2), over two RSV epidemics between 2015 and 2017. RSVB diversity was characterized by multiple virus introductions into the area and co-circulation of distinct genetic clusters, which transmitted and diversified locally with varying frequency. Increase in relative genetic diversity paralleled seasonal virus incidence. Importantly, we identified a cluster of viruses that emerged in the 2016/17 epidemic, carrying distinct amino-acid signatures including a novel nonsynonymous change (K68Q) in antigenic site ∅ in the Fusion protein. RSVB diversity was additionally marked by signature nonsynonymous substitutions that were unique to particular genomic clusters, some under diversifying selection. Our findings provide insights into recent evolutionary and epidemiological behaviors of RSVB, and highlight possible emergence of a novel antigenic variant, which has implications on current prophylactic strategies in development.
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Affiliation(s)
- Everlyn Kamau
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - James R Otieno
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Nickson Murunga
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - John W Oketch
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Joyce M Ngoi
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Zaydah R de Laurent
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Anthony Mwema
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Joyce U Nyiro
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Charles N Agoti
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.,School of Health and Human Sciences, Pwani University, Kilifi, Kenya
| | - D James Nokes
- Epidemiology and Demography Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.,School of Life Sciences and Zeeman Institute (SBIDER), University of Warwick, Coventry, UK
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129
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Zhang J, Liu H, Wang J, Wang J, Zhang J, Wang J, Zhang X, Ji H, Ding Z, Xia H, Zhang C, Zhao Q, Liang G. Origin and evolution of emerging Liao ning Virus (genus Seadornavirus, family Reoviridae). Virol J 2020; 17:105. [PMID: 32664965 PMCID: PMC7359424 DOI: 10.1186/s12985-020-01382-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 07/03/2020] [Indexed: 11/24/2022] Open
Abstract
Background Liao ning virus (LNV) is a member of the genus Seadornavirus, family Reoviridae and has been isolated from kinds of vectors in Asia and Australia. However, there are no systematic studies describe the molecular genetic evolution and migration of LNVs. With the development of bioinformatics, viral genetic data combining the information of virus isolation time and locations could be integrated to infer the virus evolution and spread in nature. Methods Here, a phylogenetic and phylogeographic analysis using Bayesian Markov chain Monte Carlo simulations was conducted on the LNVs isolated from a variety of vectors during 1990–2014 to identify the evolution and migration patterns of LNVs. Results The results demonstrated that the LNV could be divided into 3 genotypes, of which genotype 1 mainly composed of LNVs isolated from Australia during 1990 to 2014 and the original LNV strain (LNV-NE97–31) isolated from Liaoning province in northern China in 1997, genotype 2 comprised of the isolates all from Xinjiang province in western China and genotype 3 consisted the isolates from Qinghai and Shanxi province of central China. LNVs emerged about 272 years ago and gradually evolved into three lineages in the order genotype 1, genotype 2 and genotype 3. Following phylogeographic analysis, it shows genotype 1 LNVs transmitted from Australia (113°E-153°E,10°S-42°S) to Liaoning province (118°E-125°E,38°N-43°N) in Northeast Asian continent then further spread across the central part of China to western China (75°E-95°E,35°N-50°N). Conclusion LNVs were initially isolated from Liaoning province of China in the Northeast Asia, however, the present study revealed that LNVs were first appeared in Australia in the South Pacific region and transmitted to mainland China then rapidly spread across China and evolved three different genotypes. The above results suggested that LNV had the characteristics of long-distance transmission and there were great genetic diversity existed in the LNV population. Notably, current information of 80 strains of LNVs are limited. It is of great importance to strengthen the surveillance of LNVs to explore its real origin in nature and monitoring of the LNVs’ population variation and maintain vigilance to avoid LNV breaking through the species barrier and further clarify its relationship to human and animal infection.
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Affiliation(s)
- Jun Zhang
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Hong Liu
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China.
| | - Jiahui Wang
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Jiheng Wang
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Jianming Zhang
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Jiayue Wang
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Xin Zhang
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Hongfang Ji
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Zhongfeng Ding
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Han Xia
- Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China
| | - Chunyang Zhang
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Qian Zhao
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo, 255049, People's Republic of China
| | - Guodong Liang
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
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130
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Di Paola N, Sanchez-Lockhart M, Zeng X, Kuhn JH, Palacios G. Viral genomics in Ebola virus research. Nat Rev Microbiol 2020; 18:365-378. [PMID: 32367066 PMCID: PMC7223634 DOI: 10.1038/s41579-020-0354-7] [Citation(s) in RCA: 23] [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] [Accepted: 03/09/2020] [Indexed: 12/20/2022]
Abstract
Filoviruses such as Ebola virus continue to pose a substantial health risk to humans. Advances in the sequencing and functional characterization of both pathogen and host genomes have provided a wealth of knowledge to clinicians, epidemiologists and public health responders during outbreaks of high-consequence viral disease. Here, we describe how genomics has been historically used to investigate Ebola virus disease outbreaks and how new technologies allow for rapid, large-scale data generation at the point of care. We highlight how genomics extends beyond consensus-level sequencing of the virus to include intra-host viral transcriptomics and the characterization of host responses in acute and persistently infected patients. Similar genomics techniques can also be applied to the characterization of non-human primate animal models and to known natural reservoirs of filoviruses, and metagenomic sequencing can be the key to the discovery of novel filoviruses. Finally, we outline the importance of reverse genetics systems that can swiftly characterize filoviruses as soon as their genome sequences are available.
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Affiliation(s)
- Nicholas Di Paola
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Mariano Sanchez-Lockhart
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Xiankun Zeng
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Gustavo Palacios
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA.
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131
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Krieger MS, Denison CE, Anderson TL, Nowak MA, Hill AL. Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections. PLoS Comput Biol 2020; 16:e1008010. [PMID: 32628660 PMCID: PMC7365476 DOI: 10.1371/journal.pcbi.1008010] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 07/16/2020] [Accepted: 06/02/2020] [Indexed: 12/31/2022] Open
Abstract
Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.
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Affiliation(s)
- Madison S. Krieger
- Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Carson E. Denison
- Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Thayer L. Anderson
- Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Martin A. Nowak
- Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Alison L. Hill
- Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
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132
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Xiao M, Liu X, Ji J, Li M, Li J, Yang L, Sun W, Ren P, Yang G, Zhao J, Liang T, Ren H, Chen T, Zhong H, Song W, Wang Y, Deng Z, Zhao Y, Ou Z, Wang D, Cai J, Cheng X, Feng T, Wu H, Gong Y, Yang H, Wang J, Xu X, Zhu S, Chen F, Zhang Y, Chen W, Li Y, Li J. Multiple approaches for massively parallel sequencing of SARS-CoV-2 genomes directly from clinical samples. Genome Med 2020; 12:57. [PMID: 32605661 PMCID: PMC7325194 DOI: 10.1186/s13073-020-00751-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 06/10/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND COVID-19 (coronavirus disease 2019) has caused a major epidemic worldwide; however, much is yet to be known about the epidemiology and evolution of the virus partly due to the scarcity of full-length SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) genomes reported. One reason is that the challenges underneath sequencing SARS-CoV-2 directly from clinical samples have not been completely tackled, i.e., sequencing samples with low viral load often results in insufficient viral reads for analyses. METHODS We applied a novel multiplex PCR amplicon (amplicon)-based and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of SARS-CoV-2 from serials dilutions of a cultured isolate, and eight clinical samples covering a range of sample types and viral loads. We also examined and compared the sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. RESULTS We demonstrated that both amplicon and capture methods efficiently enriched SARS-CoV-2 content from clinical samples, while the enrichment efficiency of amplicon outran that of capture in more challenging samples. We found that capture was not as accurate as meta and amplicon in identifying between-sample variations, whereas amplicon method was not as accurate as the other two in investigating within-sample variations, suggesting amplicon sequencing was not suitable for studying virus-host interactions and viral transmission that heavily rely on intra-host dynamics. We illustrated that meta uncovered rich genetic information in the clinical samples besides SARS-CoV-2, providing references for clinical diagnostics and therapeutics. Taken all factors above and cost-effectiveness into consideration, we proposed guidance for how to choose sequencing strategy for SARS-CoV-2 under different situations. CONCLUSIONS This is, to the best of our knowledge, the first work systematically investigating inter- and intra-individual variations of SARS-CoV-2 using amplicon- and capture-based whole-genome sequencing, as well as the first comparative study among multiple approaches. Our work offers practical solutions for genome sequencing and analyses of SARS-CoV-2 and other emerging viruses.
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Affiliation(s)
- Minfeng Xiao
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | - Xiaoqing Liu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jingkai Ji
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Min Li
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China
| | - Jiandong Li
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China
| | - Lin Yang
- MGI, BGI-Shenzhen, Shenzhen, 518083, China
| | - Wanying Sun
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China
| | - Peidi Ren
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Jincun Zhao
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Institute of Infectious Disease, Guangzhou Eighth People's Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tianzhu Liang
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Tian Chen
- MGI, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Wenchen Song
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yanqun Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ziqing Deng
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | - Yanping Zhao
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | - Zhihua Ou
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | - Daxi Wang
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Xinyi Cheng
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | | | - Honglong Wu
- BGI PathoGenesis Pharmaceutical Technology, Shenzhen, China
| | - Yanping Gong
- BGI PathoGenesis Pharmaceutical Technology, Shenzhen, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, 518083, China
- James D. Watson Institute of Genome Science, Hangzhou, 310008, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, 518083, China
- James D. Watson Institute of Genome Science, Hangzhou, 310008, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen, 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen, Shenzhen, 518120, China
| | - Shida Zhu
- BGI-Shenzhen, Shenzhen, 518083, China
- Shenzhen Engineering Laboratory for Innovative Molecular Diagnostics, BGI-Shenzhen, Shenzhen, 518120, China
| | - Fang Chen
- BGI-Shenzhen, Shenzhen, 518083, China
- MGI, BGI-Shenzhen, Shenzhen, 518083, China
| | | | - Weijun Chen
- BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, 518083, China.
- BGI PathoGenesis Pharmaceutical Technology, Shenzhen, China.
| | - Yimin Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Junhua Li
- BGI-Shenzhen, Shenzhen, 518083, China.
- Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen, 518083, China.
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China.
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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: 1.3] [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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135
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Johnson S, Parker M. Ethical challenges in pathogen sequencing: a systematic scoping review. Wellcome Open Res 2020; 5:119. [PMID: 32864469 PMCID: PMC7445679 DOI: 10.12688/wellcomeopenres.15806.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/01/2020] [Indexed: 11/29/2022] Open
Abstract
Background: Going forward, the routine implementation of genomic surveillance activities and outbreak investigation is to be expected. We sought to systematically identify the emerging ethical challenges; and to systematically assess the gaps in ethical frameworks or thinking and identify where further work is needed to solve practical challenges. Methods: We systematically searched indexed academic literature from PubMed, Google Scholar, and Web of Science from 2000 to April 2019 for peer-reviewed articles that substantively engaged in discussion of ethical issues in the use of pathogen genome sequencing technologies for diagnostic, surveillance and outbreak investigation. Results: 28 articles were identified; nine United States, five United Kingdom, five The Netherlands, three Canada, two Switzerland, one Australia, two South Africa, and one Italy. Eight articles were specifically about the use of sequencing in HIV. Eleven were not specific to a particular disease. Results were organized into four themes: tensions between public and private interests; difficulties with translation from research to clinical and public health practice; the importance of community trust and support; equity and global partnerships; and the importance of context. Conclusion: While pathogen sequencing has the potential to be transformative for public health, there are a number of key ethical issues that must be addressed, particularly around the conditions of use for pathogen sequence data. Ethical standards should be informed by public values, and further empirical work investigating stakeholders’ views are required. Development in the field should also be under-pinned by a strong commitment to values of justice, in particular global health equity.
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Affiliation(s)
- Stephanie Johnson
- Wellcome Centre for Ethics and Humanities and Ethox Centre, University of Oxford, Oxford, OX3 7LF, UK
| | - Michael Parker
- Wellcome Centre for Ethics and Humanities and Ethox Centre, University of Oxford, Oxford, OX3 7LF, UK
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Fitak RR, Antonides JD, Baitchman EJ, Bonaccorso E, Braun J, Kubiski S, Chiu E, Fagre AC, Gagne RB, Lee JS, Malmberg JL, Stenglein MD, Dusek RJ, Forgacs D, Fountain-Jones NM, Gilbertson MLJ, Worsley-Tonks KEL, Funk WC, Trumbo DR, Ghersi BM, Grimaldi W, Heisel SE, Jardine CM, Kamath PL, Karmacharya D, Kozakiewicz CP, Kraberger S, Loisel DA, McDonald C, Miller S, O'Rourke D, Ott-Conn CN, Páez-Vacas M, Peel AJ, Turner WC, VanAcker MC, VandeWoude S, Pecon-Slattery J. The Expectations and Challenges of Wildlife Disease Research in the Era of Genomics: Forecasting with a Horizon Scan-like Exercise. J Hered 2020; 110:261-274. [PMID: 31067326 DOI: 10.1093/jhered/esz001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 01/08/2019] [Indexed: 12/14/2022] Open
Abstract
The outbreak and transmission of disease-causing pathogens are contributing to the unprecedented rate of biodiversity decline. Recent advances in genomics have coalesced into powerful tools to monitor, detect, and reconstruct the role of pathogens impacting wildlife populations. Wildlife researchers are thus uniquely positioned to merge ecological and evolutionary studies with genomic technologies to exploit unprecedented "Big Data" tools in disease research; however, many researchers lack the training and expertise required to use these computationally intensive methodologies. To address this disparity, the inaugural "Genomics of Disease in Wildlife" workshop assembled early to mid-career professionals with expertise across scientific disciplines (e.g., genomics, wildlife biology, veterinary sciences, and conservation management) for training in the application of genomic tools to wildlife disease research. A horizon scanning-like exercise, an activity to identify forthcoming trends and challenges, performed by the workshop participants identified and discussed 5 themes considered to be the most pressing to the application of genomics in wildlife disease research: 1) "Improving communication," 2) "Methodological and analytical advancements," 3) "Translation into practice," 4) "Integrating landscape ecology and genomics," and 5) "Emerging new questions." Wide-ranging solutions from the horizon scan were international in scope, itemized both deficiencies and strengths in wildlife genomic initiatives, promoted the use of genomic technologies to unite wildlife and human disease research, and advocated best practices for optimal use of genomic tools in wildlife disease projects. The results offer a glimpse of the potential revolution in human and wildlife disease research possible through multi-disciplinary collaborations at local, regional, and global scales.
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Affiliation(s)
| | - Jennifer D Antonides
- Department of Forestry & Natural Resources, Purdue University, West Lafayette, IN
| | - Eric J Baitchman
- The Zoo New England Division of Animal Health and Conservation, Boston, MA
| | - Elisa Bonaccorso
- The Instituto BIOSFERA and Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, vía Interoceánica y Diego de Robles, Quito, Ecuador
| | - Josephine Braun
- The Institute for Conservation Research, San Diego Zoo Global, Escondido, CA
| | - Steven Kubiski
- The Institute for Conservation Research, San Diego Zoo Global, Escondido, CA
| | - Elliott Chiu
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Anna C Fagre
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Roderick B Gagne
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Justin S Lee
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Jennifer L Malmberg
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Mark D Stenglein
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO
| | - Robert J Dusek
- The U. S. Geological Survey, National Wildlife Health Center, Madison, WI
| | - David Forgacs
- The Interdisciplinary Graduate Program of Genetics, Texas A&M University, College Station, TX
| | | | - Marie L J Gilbertson
- The Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN
| | | | - W Chris Funk
- The Department of Biology, Colorado State University, Fort Collins, CO
| | - Daryl R Trumbo
- The Department of Biology, Colorado State University, Fort Collins, CO
| | | | | | - Sara E Heisel
- The Odum School of Ecology, University of Georgia, Athens, GA
| | - Claire M Jardine
- The Department of Pathobiology, Canadian Wildlife Health Cooperative, University of Guelph, Guelph, Ontario, Canada
| | - Pauline L Kamath
- The School of Food and Agriculture, University of Maine, Orono, ME
| | | | | | - Simona Kraberger
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ
| | - Dagan A Loisel
- The Department of Biology, Saint Michael's College, Colchester, VT
| | - Cait McDonald
- The Department of Ecology & Evolutionary Biology, Cornell University, Ithaca, NY (McDonald)
| | - Steven Miller
- The Department of Biology, Drexel University, Philadelphia, PA
| | | | - Caitlin N Ott-Conn
- The Michigan Department of Natural Resources, Wildlife Disease Laboratory, Lansing, MI
| | - Mónica Páez-Vacas
- The Centro de Investigación de la Biodiversidad y Cambio Climático (BioCamb), Facultad de Ciencias de Medio Ambiente, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, Ecuador
| | - Alison J Peel
- The Environmental Futures Research Institute, Griffith University, Nathan, Queensland, Australia
| | - Wendy C Turner
- The Department of Biological Sciences, University at Albany, State University of New York, Albany, NY
| | - Meredith C VanAcker
- The Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY
| | - Sue VandeWoude
- The College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO
| | - Jill Pecon-Slattery
- The Center for Species Survival, Smithsonian Conservation Biology Institute-National Zoological Park, Front Royal, VA
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Vasey B, Shankar AH, Herrera BB, Becerra A, Xhaja K, Echenagucia M, Machado SR, Caicedo D, Miller J, Amedeo P, Naumova EN, Bosch I. Multivariate time-series analysis of biomarkers from a dengue cohort offers new approaches for diagnosis and prognosis. PLoS Negl Trop Dis 2020; 14:e0008199. [PMID: 32544159 PMCID: PMC7380649 DOI: 10.1371/journal.pntd.0008199] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 07/24/2020] [Accepted: 03/05/2020] [Indexed: 11/18/2022] Open
Abstract
Dengue is a major public health problem worldwide with distinct clinical manifestations: an acute presentation (dengue fever, DF) similar to other febrile illnesses (OFI) and a more severe, life-threatening form (severe dengue, SD). Due to nonspecific clinical presentation during the early phase of dengue infection, differentiating DF from OFI has remained a challenge, and current methods to determine severity of dengue remain poor early predictors. We present a prospective clinical cohort study conducted in Caracas, Venezuela from 2001-2005, designed to determine whether clinical and hematological parameters could distinguish DF from OFI, and identify early prognostic biomarkers of SD. From 204 enrolled suspected dengue patients, there were 111 confirmed dengue cases. Piecewise mixed effects regression and nonparametric statistics were used to analyze longitudinal records. Decreased serum albumin and fibrinogen along with increased D-dimer, thrombin-antithrombin complex, activated partial thromboplastin time and thrombin time were prognostic of SD on the day of defervescence. In the febrile phase, the day-to-day rates of change in serum albumin and fibrinogen concentration, along with platelet counts, were significantly decreased in dengue patients compared to OFI, while the day-to-day rates of change of lymphocytes (%) and thrombin time were increased. In dengue patients, the absolute lymphocytes to neutrophils ratio showed specific temporal increase, enabling classification of dengue patients entering the critical phase with an area under the ROC curve of 0.79. Secondary dengue patients had elongation of Thrombin time compared to primary cases while the D-dimer formation (fibrinolysis marker) remained always lower for secondary compared to primary cases. Based on partial analysis of 31 viral complete genomes, a high frequency of C-to-T transitions located at the third codon position was observed, suggesting deamination events with five major hot spots of amino acid polymorphic sites outside in non-structural proteins. No association of severe outcome was statistically significant for any of the five major polymorphic sites found. This study offers an improved understanding of dengue hemostasis and a novel way of approaching dengue diagnosis and disease prognosis using piecewise mixed effect regression modeling. It also suggests that a better discrimination of the day of disease can improve the diagnostic and prognostic classification power of clinical variables using ROC curve analysis. The piecewise mixed effect regression model corroborated key early clinical determinants of disease, and offers a time-series approach for future vaccine and pathogenesis clinical studies.
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Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Anuraj H. Shankar
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Bobby Brooke Herrera
- E25Bio Inc., Cambridge, Massachusetts, United States of America
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Aniuska Becerra
- Center for Infectious Diseases and Vaccine Research, University of Massachusetts, Worcester, Massachusetts, United States of America
| | - Kris Xhaja
- Center for Infectious Diseases and Vaccine Research, University of Massachusetts, Worcester, Massachusetts, United States of America
| | - Marion Echenagucia
- Centro Nacional de Hemofilia at Banco Municipal de Sangre, Universidad Central de Venezuela, Caracas, Venezuela
| | - Sara R. Machado
- Department of Health Policy, London School of Economics, London, United Kingdom
| | | | - John Miller
- J. Craig Venter Institute, La Jolla, California, United States of America
| | - Paolo Amedeo
- J. Craig Venter Institute, La Jolla, California, United States of America
| | - Elena N. Naumova
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts, United States of America
| | - Irene Bosch
- E25Bio Inc., Cambridge, Massachusetts, United States of America
- Department of Medicine, Mount Sinai School of Medicine, New York, New York, United States of America
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Norma Blumenfeld deBosch
- Center for Infectious Diseases and Vaccine Research, University of Massachusetts, Worcester, Massachusetts, United States of America
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Gill MS, Lemey P, Suchard MA, Rambaut A, Baele G. Online Bayesian Phylodynamic Inference in BEAST with Application to Epidemic Reconstruction. Mol Biol Evol 2020; 37:1832-1842. [PMID: 32101295 PMCID: PMC7253210 DOI: 10.1093/molbev/msaa047] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Reconstructing pathogen dynamics from genetic data as they become available during an outbreak or epidemic represents an important statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an "online" fashion. Widely used Bayesian phylogenetic inference packages are not set up for this purpose, generally requiring one to recompute trees and evolutionary model parameters de novo when new data arrive. To accommodate increasing data flow in a Bayesian phylogenetic framework, we introduce a methodology to efficiently update the posterior distribution with newly available genetic data. Our procedure is implemented in the BEAST 1.10 software package, and relies on a distance-based measure to insert new taxa into the current estimate of the phylogeny and imputes plausible values for new model parameters to accommodate growing dimensionality. This augmentation creates informed starting values and re-uses optimally tuned transition kernels for posterior exploration of growing data sets, reducing the time necessary to converge to target posterior distributions. We apply our framework to data from the recent West African Ebola virus epidemic and demonstrate a considerable reduction in time required to obtain posterior estimates at different time points of the outbreak. Beyond epidemic monitoring, this framework easily finds other applications within the phylogenetics community, where changes in the data-in terms of alignment changes, sequence addition or removal-present common scenarios that can benefit from online inference.
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Affiliation(s)
- Mandev S Gill
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Marc A Suchard
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA
- Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, MD
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
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139
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Jorgensen D, Pons-Salort M, Shaw AG, Grassly NC. The role of genetic sequencing and analysis in the polio eradication programme. Virus Evol 2020; 6:veaa040. [PMID: 32782825 PMCID: PMC7409915 DOI: 10.1093/ve/veaa040] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Genetic sequencing of polioviruses detected through clinical and environmental surveillance is used to confirm detection, identify their likely origin, track geographic patterns of spread, and determine the appropriate vaccination response. The critical importance of genetic sequencing and analysis to the Global Polio Eradication Initiative has grown with the increasing incidence of vaccine-derived poliovirus (VDPV) infections in Africa specifically (470 reported cases in 2019), and globally, alongside persistent transmission of serotype 1 wild-type poliovirus in Pakistan and Afghanistan (197 reported cases in 2019). Adapting what has been learned about the virus genetics and evolution to address these threats has been a major focus of recent work. Here, we review how phylogenetic and phylogeographic methods have been used to trace the spread of wild-type polioviruses and identify the likely origins of VDPVs. We highlight the analysis methods and sequencing technology currently used and the potential for new technologies to speed up poliovirus detection and the interpretation of genetic data. At a pivotal point in the eradication campaign with the threat of anti-vaccine sentiment and donor and public fatigue, innovation is critical to maintain drive and overcome the last remaining circulating virus.
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Affiliation(s)
- David Jorgensen
- Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Margarita Pons-Salort
- Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Alexander G Shaw
- Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
| | - Nicholas C Grassly
- Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK
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140
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Brunker K, Jaswant G, Thumbi S, Lushasi K, Lugelo A, Czupryna AM, Ade F, Wambura G, Chuchu V, Steenson R, Ngeleja C, Bautista C, Manalo DL, Gomez MRR, Chu MYJV, Miranda ME, Kamat M, Rysava K, Espineda J, Silo EAV, Aringo AM, Bernales RP, Adonay FF, Tildesley MJ, Marston DA, Jennings DL, Fooks AR, Zhu W, Meredith LW, Hill SC, Poplawski R, Gifford RJ, Singer JB, Maturi M, Mwatondo A, Biek R, Hampson K. Rapid in-country sequencing of whole virus genomes to inform rabies elimination programmes. Wellcome Open Res 2020; 5:3. [PMID: 32090172 PMCID: PMC7001756 DOI: 10.12688/wellcomeopenres.15518.2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2020] [Indexed: 12/19/2022] Open
Abstract
Genomic surveillance is an important aspect of contemporary disease management but has yet to be used routinely to monitor endemic disease transmission and control in low- and middle-income countries. Rabies is an almost invariably fatal viral disease that causes a large public health and economic burden in Asia and Africa, despite being entirely vaccine preventable. With policy efforts now directed towards achieving a global goal of zero dog-mediated human rabies deaths by 2030, establishing effective surveillance tools is critical. Genomic data can provide important and unique insights into rabies spread and persistence that can direct control efforts. However, capacity for genomic research in low- and middle-income countries is held back by limited laboratory infrastructure, cost, supply chains and other logistical challenges. Here we present and validate an end-to-end workflow to facilitate affordable whole genome sequencing for rabies surveillance utilising nanopore technology. We used this workflow in Kenya, Tanzania and the Philippines to generate rabies virus genomes in two to three days, reducing costs to approximately £60 per genome. This is over half the cost of metagenomic sequencing previously conducted for Tanzanian samples, which involved exporting samples to the UK and a three- to six-month lag time. Ongoing optimization of workflows are likely to reduce these costs further. We also present tools to support routine whole genome sequencing and interpretation for genomic surveillance. Moreover, combined with training workshops to empower scientists in-country, we show that local sequencing capacity can be readily established and sustainable, negating the common misperception that cutting-edge genomic research can only be conducted in high resource laboratories. More generally, we argue that the capacity to harness genomic data is a game-changer for endemic disease surveillance and should precipitate a new wave of researchers from low- and middle-income countries.
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Affiliation(s)
- Kirstyn Brunker
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- The Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Gurdeep Jaswant
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- University of Nairobi Institute of Tropical and Infectious Diseases (UNITID), Nairobi, Kenya
| | - S.M. Thumbi
- University of Nairobi Institute of Tropical and Infectious Diseases (UNITID), Nairobi, Kenya
- Center for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
- Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, USA
| | | | - Ahmed Lugelo
- Department of Veterinary Medicine and Public Health, Sokoine University of Agriculture, Morogoro, Tanzania
| | - Anna M. Czupryna
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fred Ade
- Center for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Gati Wambura
- Center for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Veronicah Chuchu
- Center for Global Health Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - Rachel Steenson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Chanasa Ngeleja
- Tanzania Veterinary Laboratory Agency, Ministry of Livestock and Fisheries Development, Dar es Salaam, Tanzania
| | - Criselda Bautista
- Research Institute for Tropical Medicine (RITM), Manilla, Philippines
| | - Daria L. Manalo
- Research Institute for Tropical Medicine (RITM), Manilla, Philippines
| | | | | | - Mary Elizabeth Miranda
- Research Institute for Tropical Medicine (RITM), Manilla, Philippines
- Field Epidemiology Training Program Alumni Foundation (FETPAFI), Manilla, Philippines
| | - Maya Kamat
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Kristyna Rysava
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematical Institute, University of Warwick, Coventry, UK
| | - Jason Espineda
- Department of Agriculture Regional Field Office 5, Regional Animal Disease, Diagnostic Laboratory, Cabangan, Camalig, Albay, Philippines
| | - Eva Angelica V. Silo
- Department of Agriculture Regional Field Office 5, Regional Animal Disease, Diagnostic Laboratory, Cabangan, Camalig, Albay, Philippines
| | - Ariane Mae Aringo
- Department of Agriculture Regional Field Office 5, Regional Animal Disease, Diagnostic Laboratory, Cabangan, Camalig, Albay, Philippines
| | - Rona P. Bernales
- Department of Agriculture Regional Field Office 5, Regional Animal Disease, Diagnostic Laboratory, Cabangan, Camalig, Albay, Philippines
| | - Florencio F. Adonay
- Albay Veterinary Office, Provincial Government of Albay, Albay Farmers' Bounty Village, Cabangan, Camalig, Albay, Philippines
| | - Michael J. Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematical Institute, University of Warwick, Coventry, UK
| | - Denise A. Marston
- Wildlife Zoonoses & Vector-Borne Diseases Research Group, Animal and Plant Health Agency (APHA), Weybridge, UK
| | - Daisy L. Jennings
- Wildlife Zoonoses & Vector-Borne Diseases Research Group, Animal and Plant Health Agency (APHA), Weybridge, UK
| | - Anthony R. Fooks
- Wildlife Zoonoses & Vector-Borne Diseases Research Group, Animal and Plant Health Agency (APHA), Weybridge, UK
- Institute of Infection and Global Health,, University of Liverpool, Liverpool, UK
| | - Wenlong Zhu
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | | | | | - Radoslaw Poplawski
- Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK
- Advanced Research Computing, University of Birmingham, Birmingham, B15 2TT, UK
| | - Robert J. Gifford
- MRC-University of Glasgow Centre for Virus Research (CVR), University of Glasgow, Glasgow, UK
| | - Joshua B. Singer
- MRC-University of Glasgow Centre for Virus Research (CVR), University of Glasgow, Glasgow, UK
| | - Mathew Maturi
- Zoonotic Disease Unit, Ministry of Health, Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya
| | - Athman Mwatondo
- Zoonotic Disease Unit, Ministry of Health, Ministry of Agriculture, Livestock and Fisheries, Nairobi, Kenya
| | - Roman Biek
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- The Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Katie Hampson
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
- The Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, Glasgow, G12 8QQ, UK
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141
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Lu J, Kang M, Zeng H, Zhong Y, Fang L, Zheng X, Liu L, Yi L, Lin H, Peng J, Li C, Zhang Y, Sun L, Luo S, Xiao J, Munnink BBO, Koopmans MPG, Wu J, Zhang Y, Zhang Y, Song T, Li H, Zheng H. Tracking echovirus eleven outbreaks in Guangdong, China: a metatranscriptomic, phylogenetic, and epidemiological study. Virus Evol 2020; 6:veaa029. [PMID: 32411392 PMCID: PMC7211399 DOI: 10.1093/ve/veaa029] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
In April 2019, a suspect cluster of enterovirus cases was reported in a neonatology department in Guangdong, China, resulting in five deaths. We aimed to investigate the pathogen profiles in fatal cases, the circulation and transmission pattern of the viruses by combining metatranscriptomic, phylogenetic, and epidemiological analyses. Metatranscriptomic sequencing was used to characterize the enteroviruses. Clinical and environmental surveillance in the local population was performed to understand the prevalence and genetic diversity of the viruses in the local population. The possible source(s), evolution, transmission, and recombination of the viruses were investigated by incorporating genomes from the current outbreak, from local retrospective surveillance, and from public databases. Metatranscriptomic analysis identified Echovirus 11 (E11) in three fatal cases. Seroprevalence of neutralization antibody to E11 was 35 to 44 per cent in 3–15 age groups of general population, and the viruses were associated with various clinical symptoms. From the viral phylogeny, nosocomial transmissions were identified and all E11 2019 outbreak strains were closely related with E11 strains circulating in local population 2017–19. Frequent recombination occurred among the 2019 Guangdong E11 outbreak strains and various genotypes in enterovirus B species. This study provides an example of combining advanced genetic technology and epidemiological surveillance in pathogen diagnosis, source(s), and transmission tracing during an infectious disease outbreak. The result highlights the hidden E11 circulation and the risk of viral transmission and infection in the young age population in China. Frequent recombination between Guangdong-like strains and other enterovirus genotypes also implies the prevalence of these emerging E11 strains.
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Affiliation(s)
- Jing Lu
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China.,Guangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China.,Southern Medical University, No. 1838, Shatai Road, Baiyun District, Guangzhou, China
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Hanri Zeng
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Yuwen Zhong
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Ling Fang
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Xiaoling Zheng
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China.,Guangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China.,Southern Medical University, No. 1838, Shatai Road, Baiyun District, Guangzhou, China.,Guangming District Center for Disease Control and Prevention, No. 61, Fengjing Road, Guangming District, Shenzhen, China.,Erasmus Medical Centre, Rotterdam, The Netherlands.,WHO WPRO Regional Polio Reference Laboratory and Ministry of Health Key Laboratory for Medical Virology, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, No. 155, Changbai Road, Changping District, Beijing, China
| | - Leng Liu
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Lina Yi
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China.,Guangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Huifang Lin
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China.,Guangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Jingju Peng
- Southern Medical University, No. 1838, Shatai Road, Baiyun District, Guangzhou, China
| | - Caixia Li
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Yingtao Zhang
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Limei Sun
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Shuhua Luo
- Guangming District Center for Disease Control and Prevention, No. 61, Fengjing Road, Guangming District, Shenzhen, China
| | - Jianpeng Xiao
- Guangdong Provincial Institution of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | | | | | - Jie Wu
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Yong Zhang
- WHO WPRO Regional Polio Reference Laboratory and Ministry of Health Key Laboratory for Medical Virology, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, No. 155, Changbai Road, Changping District, Beijing, China
| | - Yonghui Zhang
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Hui Li
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
| | - Huanying Zheng
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, China
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142
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The public health response to COVID-19: balancing precaution and unintended consequences. Ann Epidemiol 2020; 46:12-13. [PMID: 32532367 PMCID: PMC7207121 DOI: 10.1016/j.annepidem.2020.05.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 04/27/2020] [Accepted: 05/03/2020] [Indexed: 11/23/2022]
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143
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Degeling C, Carter SM, van Oijen AM, McAnulty J, Sintchenko V, Braunack-Mayer A, Yarwood T, Johnson J, Gilbert GL. Community perspectives on the benefits and risks of technologically enhanced communicable disease surveillance systems: a report on four community juries. BMC Med Ethics 2020; 21:31. [PMID: 32334597 PMCID: PMC7183724 DOI: 10.1186/s12910-020-00474-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 04/17/2020] [Indexed: 12/18/2022] Open
Abstract
Background Outbreaks of infectious disease cause serious and costly health and social problems. Two new technologies – pathogen whole genome sequencing (WGS) and Big Data analytics – promise to improve our capacity to detect and control outbreaks earlier, saving lives and resources. However, routinely using these technologies to capture more detailed and specific personal information could be perceived as intrusive and a threat to privacy. Method Four community juries were convened in two demographically different Sydney municipalities and two regional cities in New South Wales, Australia (western Sydney, Wollongong, Tamworth, eastern Sydney) to elicit the views of well-informed community members on the acceptability and legitimacy of:
making pathogen WGS and linked administrative data available for public health research using this information in concert with data linkage and machine learning to enhance communicable disease surveillance systems
Fifty participants of diverse backgrounds, mixed genders and ages were recruited by random-digit-dialling and topic-blinded social-media advertising. Each jury was presented with balanced factual evidence supporting different expert perspectives on the potential benefits and costs of technologically enhanced public health research and communicable disease surveillance and given the opportunity to question experts. Results Almost all jurors supported data linkage and WGS on routinely collected patient isolates for the purposes of public health research, provided standard de-identification practices were applied. However, allowing this information to be operationalised as a syndromic surveillance system was highly contentious with three juries voting in favour, and one against by narrow margins. For those in favour, support depended on several conditions related to system oversight and security being met. Those against were concerned about loss of privacy and did not trust Australian governments to run secure and effective systems. Conclusions Participants across all four events strongly supported the introduction of data linkage and pathogenomics to public health research under current research governance structures. Combining pathogen WGS with event-based data surveillance systems, however, is likely to be controversial because of a lack of public trust, even when the potential public health benefits are clear. Any suggestion of private sector involvement or commercialisation of WGS or surveillance data was unanimously rejected.
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Affiliation(s)
- Chris Degeling
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, Australia. .,School of Health and Society, University of Wollongong, Wollongong, Australia.
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, Australia.,School of Health and Society, University of Wollongong, Wollongong, Australia
| | - Antoine M van Oijen
- Molecular Horizons and the Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia
| | | | - Vitali Sintchenko
- The Centre for Infectious Diseases and Microbiology - Public Health, Westmead, Sydney, Australia.,Marie Bashir Institute for Infectious Disease and Biosecurity, The University of Sydney, Sydney, Australia
| | - Annette Braunack-Mayer
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, Australia.,School of Health and Society, University of Wollongong, Wollongong, Australia
| | - Trent Yarwood
- Cairns and Hinterland Hospital and Health Service, Cairns, Australia.,Cairns Clinical School, James Cook University, Cairns, Australia.,Rural Clinical School, University of Queensland, Brisbane, Australia
| | - Jane Johnson
- The Centre for Infectious Diseases and Microbiology - Public Health, Westmead, Sydney, Australia.,Sydney Health Ethics, School of Public Health, The University of Sydney, Sydney, Australia
| | - Gwendolyn L Gilbert
- The Centre for Infectious Diseases and Microbiology - Public Health, Westmead, Sydney, Australia.,Marie Bashir Institute for Infectious Disease and Biosecurity, The University of Sydney, Sydney, Australia.,Sydney Health Ethics, School of Public Health, The University of Sydney, Sydney, Australia
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144
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Alamil M, Hughes J, Berthier K, Desbiez C, Thébaud G, Soubeyrand S. Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases. Philos Trans R Soc Lond B Biol Sci 2020; 374:20180258. [PMID: 31056055 PMCID: PMC6553606 DOI: 10.1098/rstb.2018.0258] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.
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Affiliation(s)
- M Alamil
- 1 BioSP, INRA, 84914 Avignon , France
| | - J Hughes
- 2 MRC-University of Glasgow Centre for Virus Research , Glasgow G61 1QH , UK
| | - K Berthier
- 3 Pathologie Végétale, INRA , 84140 Montfavet , France
| | - C Desbiez
- 3 Pathologie Végétale, INRA , 84140 Montfavet , France
| | - G Thébaud
- 4 BGPI, INRA, Univ. Montpellier , SupAgro, Cirad, 34398 Montpellier , France
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145
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Eden JS, Rockett R, Carter I, Rahman H, de Ligt J, Hadfield J, Storey M, Ren X, Tulloch R, Basile K, Wells J, Byun R, Gilroy N, O'Sullivan MV, Sintchenko V, Chen SC, Maddocks S, Sorrell TC, Holmes EC, Dwyer DE, Kok J. An emergent clade of SARS-CoV-2 linked to returned travellers from Iran. Virus Evol 2020; 6:veaa027. [PMID: 32296544 PMCID: PMC7147362 DOI: 10.1093/ve/veaa027] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The SARS-CoV-2 epidemic has rapidly spread outside China with major outbreaks occurring in Italy, South Korea, and Iran. Phylogenetic analyses of whole-genome sequencing data identified a distinct SARS-CoV-2 clade linked to travellers returning from Iran to Australia and New Zealand. This study highlights potential viral diversity driving the epidemic in Iran, and underscores the power of rapid genome sequencing and public data sharing to improve the detection and management of emerging infectious diseases.
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Affiliation(s)
- John-Sebastian Eden
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Centre for Virus Research & Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, PO Box 412, Westmead, NSW 2145, Australia
| | - Rebecca Rockett
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia.,Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Ian Carter
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Hossinur Rahman
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Joep de Ligt
- Institute of Environmental Science and Research, Porirua 5240, New Zealand
| | - James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Matthew Storey
- Institute of Environmental Science and Research, Porirua 5240, New Zealand
| | - Xiaoyun Ren
- Institute of Environmental Science and Research, Porirua 5240, New Zealand
| | - Rachel Tulloch
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Centre for Virus Research & Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, PO Box 412, Westmead, NSW 2145, Australia
| | - Kerri Basile
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Jessica Wells
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Roy Byun
- NSW Ministry of Health, North Sydney, NSW 2059, Australia
| | - Nicky Gilroy
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Matthew V O'Sullivan
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia.,Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Vitali Sintchenko
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia.,Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Sharon C Chen
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia.,Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Susan Maddocks
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Tania C Sorrell
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Centre for Virus Research & Centre for Infectious Diseases and Microbiology, Westmead Institute for Medical Research, PO Box 412, Westmead, NSW 2145, Australia.,Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Edward C Holmes
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Dominic E Dwyer
- Marie Bashir Institute for Infectious Diseases and Biosecurity, School of Life and Environmental Sciences & School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia.,Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia.,Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Jen Kok
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia.,Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
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Leon KE, Schubert RD, Casas-Alba D, Hawes IA, Ramachandran PS, Ramesh A, Pak JE, Wu W, Cheung CK, Crawford ED, Khan LM, Launes C, Sample HA, Zorn KC, Cabrerizo M, Valero-Rello A, Langelier C, Muñoz-Almagro C, DeRisi JL, Wilson MR. Genomic and serologic characterization of enterovirus A71 brainstem encephalitis. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2020; 7:7/3/e703. [PMID: 32139440 PMCID: PMC7136061 DOI: 10.1212/nxi.0000000000000703] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/06/2020] [Indexed: 12/12/2022]
Abstract
Objective In 2016, Catalonia experienced a pediatric brainstem encephalitis outbreak caused by enterovirus A71 (EV-A71). Conventional testing identified EV in the periphery but rarely in CSF. Metagenomic next-generation sequencing (mNGS) and CSF pan-viral serology (VirScan) were deployed to enhance viral detection and characterization. Methods RNA was extracted from the CSF (n = 20), plasma (n = 9), stool (n = 15), and nasopharyngeal samples (n = 16) from 10 children with brainstem encephalitis and 10 children with meningitis or encephalitis. Pathogens were identified using mNGS. Available CSF from cases (n = 12) and pediatric other neurologic disease controls (n = 54) were analyzed with VirScan with a subset (n = 9 and n = 50) validated by ELISA. Results mNGS detected EV in all samples positive by quantitative reverse transcription polymerase chain reaction (qRT-PCR) (n = 25). In qRT-PCR-negative samples (n = 35), mNGS found virus in 23% (n = 8, 3 CSF samples). Overall, mNGS enhanced EV detection from 42% (25/60) to 57% (33/60) (p-value = 0.013). VirScan and ELISA increased detection to 92% (11/12) compared with 46% (4/12) for CSF mNGS and qRT-PCR (p-value = 0.023). Phylogenetic analysis confirmed the EV-A71 strain clustered with a neurovirulent German EV-A71. A single amino acid substitution (S241P) in the EVA71 VP1 protein was exclusive to the CNS in one subject. Conclusion mNGS with VirScan significantly increased the CNS detection of EVs relative to qRT-PCR, and the latter generated an antigenic profile of the acute EV-A71 immune response. Genomic analysis confirmed the close relation of the outbreak EV-A71 and neuroinvasive German EV-A71. A S241P substitution in VP1 was found exclusively in the CSF.
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Affiliation(s)
- Kristoffer E Leon
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Ryan D Schubert
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Didac Casas-Alba
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Isobel A Hawes
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Prashanth S Ramachandran
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Akshaya Ramesh
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - John E Pak
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Wesley Wu
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Carly K Cheung
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Emily D Crawford
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Lillian M Khan
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Cristian Launes
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Hannah A Sample
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Kelsey C Zorn
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Maria Cabrerizo
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Ana Valero-Rello
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Charles Langelier
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Carmen Muñoz-Almagro
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Joseph L DeRisi
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain
| | - Michael R Wilson
- From the Medical Scientist Training Program (K.E.L.), University of California, San Francisco; Biomedical Sciences Graduate Program (K.E.L., I.A.H.), University of California, San Francisco; Weill Institute for Neurosciences (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Department of Neurology (R.D.S., I.A.H., P.S.R., A.R., M.R.W.), University of California, San Francisco; Institut de Recerca Pediàtrica Hospital Sant Joan de Déu (D.C.-A., C.L., A.V.-R., C.M.-A.), Barcelona, Spain; Chan Zuckerberg Biohub (J.E.P., W.W., C.K.C., E.D.C., J.L.D.), San Francisco; Department of Biochemistry and Biophysics (L.M.K., H.A.S., K.C.Z., J.L.D.), University of California, San Francisco; CIBER Epidemiología y Salud Pública (CIBERESP) (C.L., M.C., C.M.-A.), Health Institute Carlos III; Department of Pediatrics (C.L.), Universitat de Barcelona, Barcelona; Enterovirus Unit (M.C.), Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain; Division of Infectious Diseases (C.L.), Department of Medicine, University of California, San Francisco; and Department of Medicine. Universitat Internacional de Catalunya (C.M.-A.), Barcelona, Spain.
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147
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Kahn R, Peak CM, Fernández-Gracia J, Hill A, Jambai A, Ganda L, Castro MC, Buckee CO. Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone. Proc Natl Acad Sci U S A 2020; 117:5067-5073. [PMID: 32054785 PMCID: PMC7060667 DOI: 10.1073/pnas.1913052117] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen's underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen's incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.
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Affiliation(s)
- Rebecca Kahn
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Corey M Peak
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Juan Fernández-Gracia
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
- Institute for Cross-Disciplinary Physics and Complex Systems, Universitat de les Illes Balears - Consell Superior d'Investigacions Científiques, E-07122 Palma de Mallorca, Spain
| | - Alexandra Hill
- Disease Control in Humanitarian Emergencies, World Health Organization, CH-1211 Geneva 27, Switzerland
| | - Amara Jambai
- Disease Control and Prevention, Sierra Leone Ministry of Health and Sanitation, Freetown, Sierra Leone FPGG+89
| | - Louisa Ganda
- Country Office, World Health Organization, Freetown, Sierra Leone FPGG+89
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115;
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148
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Vrancken B, Wawina-Bokalanga T, Vanmechelen B, Martí-Carreras J, Carroll MW, Nsio J, Kapetshi J, Makiala-Mandanda S, Muyembe-Tamfum JJ, Baele G, Vermeire K, Vergote V, Ahuka-Mundeke S, Maes P. Accounting for population structure reveals ambiguity in the Zaire Ebolavirus reservoir dynamics. PLoS Negl Trop Dis 2020; 14:e0008117. [PMID: 32130210 PMCID: PMC7075637 DOI: 10.1371/journal.pntd.0008117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 03/16/2020] [Accepted: 02/05/2020] [Indexed: 11/18/2022] Open
Abstract
Ebolaviruses pose a substantial threat to wildlife populations and to public health in Africa. Evolutionary analyses of virus genome sequences can contribute significantly to elucidate the origin of new outbreaks, which can help guide surveillance efforts. The reconstructed between-outbreak evolutionary history of Zaire ebolavirus so far has been highly consistent. By removing the confounding impact of population growth bursts during local outbreaks on the free mixing assumption that underlies coalescent-based demographic reconstructions, we find-contrary to what previous results indicated-that the circulation dynamics of Ebola virus in its animal reservoir are highly uncertain. Our findings also accentuate the need for a more fine-grained picture of the Ebola virus diversity in its reservoir to reliably infer the reservoir origin of outbreak lineages. In addition, the recent appearance of slower-evolving variants is in line with latency as a survival mechanism and with bats as the natural reservoir host.
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Affiliation(s)
- Bram Vrancken
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Tony Wawina-Bokalanga
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Bert Vanmechelen
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Joan Martí-Carreras
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Miles W. Carroll
- Research and Development Institute, National Infection Service, Public Health England, Porton Down, Wiltshire, United Kingdom
| | - Justus Nsio
- Ministère de la Santé, Kinshasa, Democratic Republic of the Congo
| | - Jimmy Kapetshi
- Institut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo
| | - Sheila Makiala-Mandanda
- Institut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo
| | | | - Guy Baele
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Kurt Vermeire
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven, Belgium
| | - Valentijn Vergote
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
| | - Steve Ahuka-Mundeke
- Institut National de Recherche Biomédicale (INRB), Kinshasa, Democratic Republic of the Congo
| | - Piet Maes
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Division of Clinical and Epidemiological Virology, Leuven, Belgium
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149
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Jacob ST, Crozier I, Fischer WA, Hewlett A, Kraft CS, Vega MADL, Soka MJ, Wahl V, Griffiths A, Bollinger L, Kuhn JH. Ebola virus disease. Nat Rev Dis Primers 2020; 6:13. [PMID: 32080199 PMCID: PMC7223853 DOI: 10.1038/s41572-020-0147-3] [Citation(s) in RCA: 296] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/10/2020] [Indexed: 12/16/2022]
Abstract
Ebola virus disease (EVD) is a severe and frequently lethal disease caused by Ebola virus (EBOV). EVD outbreaks typically start from a single case of probable zoonotic transmission, followed by human-to-human transmission via direct contact or contact with infected bodily fluids or contaminated fomites. EVD has a high case-fatality rate; it is characterized by fever, gastrointestinal signs and multiple organ dysfunction syndrome. Diagnosis requires a combination of case definition and laboratory tests, typically real-time reverse transcription PCR to detect viral RNA or rapid diagnostic tests based on immunoassays to detect EBOV antigens. Recent advances in medical countermeasure research resulted in the recent approval of an EBOV-targeted vaccine by European and US regulatory agencies. The results of a randomized clinical trial of investigational therapeutics for EVD demonstrated survival benefits from two monoclonal antibody products targeting the EBOV membrane glycoprotein. New observations emerging from the unprecedented 2013-2016 Western African EVD outbreak (the largest in history) and the ongoing EVD outbreak in the Democratic Republic of the Congo have substantially improved the understanding of EVD and viral persistence in survivors of EVD, resulting in new strategies toward prevention of infection and optimization of clinical management, acute illness outcomes and attendance to the clinical care needs of patients.
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Affiliation(s)
- Shevin T Jacob
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Global Health Security Department, Infectious Diseases Institute, Makerere University, Kampala, Uganda
| | - Ian Crozier
- Integrated Research Facility at Fort Detrick, Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research supported by the National Cancer Institute, Frederick, MD, USA
| | - William A Fischer
- Department of Medicine, Division of Pulmonary Disease and Critical Care Medicine, Chapel Hill, NC, USA
| | - Angela Hewlett
- Nebraska Biocontainment Unit, Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, NE, USA
| | - Colleen S Kraft
- Microbiology Section, Emory Medical Laboratory, Emory University School of Medicine, Atlanta, GA, USA
| | - Marc-Antoine de La Vega
- Department of Microbiology, Immunology & Infectious Diseases, Université Laval, Quebec City, QC, Canada
| | - Moses J Soka
- Partnership for Ebola Virus Disease Research in Liberia, Monrovia Medical Units ELWA-2 Hospital, Monrovia, Liberia
| | - Victoria Wahl
- National Biodefense Analysis and Countermeasures Center, Fort Detrick, Frederick, MD, USA
| | - Anthony Griffiths
- Department of Microbiology and National Emerging Infectious Diseases Laboratories, Boston University School of Medicine, Boston, MA, USA
| | - Laura Bollinger
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, MD, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, MD, USA.
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150
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Hong SL, Dellicour S, Vrancken B, Suchard MA, Pyne MT, Hillyard DR, Lemey P, Baele G. In Search of Covariates of HIV-1 Subtype B Spread in the United States-A Cautionary Tale of Large-Scale Bayesian Phylogeography. Viruses 2020; 12:v12020182. [PMID: 32033422 PMCID: PMC7077180 DOI: 10.3390/v12020182] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/24/2020] [Accepted: 01/28/2020] [Indexed: 12/21/2022] Open
Abstract
Infections with HIV-1 group M subtype B viruses account for the majority of the HIV epidemic in the Western world. Phylogeographic studies have placed the introduction of subtype B in the United States in New York around 1970, where it grew into a major source of spread. Currently, it is estimated that over one million people are living with HIV in the US and that most are infected with subtype B variants. Here, we aim to identify the drivers of HIV-1 subtype B dispersal in the United States by analyzing a collection of 23,588 pol sequences, collected for drug resistance testing from 45 states during 2004-2011. To this end, we introduce a workflow to reduce this large collection of data to more computationally-manageable sample sizes and apply the BEAST framework to test which covariates associate with the spread of HIV-1 across state borders. Our results show that we are able to consistently identify certain predictors of spread under reasonable run times across datasets of up to 10,000 sequences. However, the general lack of phylogenetic structure and the high uncertainty associated with HIV trees make it difficult to interpret the epidemiological relevance of the drivers of spread we are able to identify. While the workflow we present here could be applied to other virus datasets of a similar scale, the characteristic star-like shape of HIV-1 phylogenies poses a serious obstacle to reconstructing a detailed evolutionary and spatial history for HIV-1 subtype B in the US.
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Affiliation(s)
- Samuel L. Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
- Correspondence:
| | - Simon Dellicour
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Bram Vrancken
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
| | - Marc A. Suchard
- 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
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA
| | - Michael T. Pyne
- ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT 84108, USA;
| | - David R. Hillyard
- Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA;
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium; (S.D.); (B.V.); (P.L.); (G.B.)
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