Genomic epidemiology reveals multiple introductions of Zika virus into the United States

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Abstract

Zika virus (ZIKV) is causing an unprecedented epidemic linked to severe congenital syndromes ^{1,
2}. In July 2016, mosquito-borne ZIKV transmission was reported in the continental United States and since then, hundreds of locally-acquired infections have been reported in Florida ^{3,
4}. To gain insights into the timing, source, and likely route(s) of ZIKV introduction, we tracked the virus from its first detection in Florida by sequencing ZIKV genomes from infected patients and Aedes aegypti mosquitoes. We show that at least four introductions, but potentially as many as 40, contributed to the outbreak in Florida and that local transmission likely started in the spring of 2016 - several months before initial detection. By analyzing surveillance and genetic data, we discovered that ZIKV moved among transmission zones in Miami. Our analyses show that most introductions are linked to the Caribbean, a finding corroborated by the high incidence rates and traffic volumes from the region into the Miami area. Our study provides an understanding of how ZIKV initiates transmission in new regions.

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Most cited references 51

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Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Guy Baele, Philippe Lemey, Trevor Bedford … (2012)

Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.

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Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences.

Beth Shapiro, Andrew Rambaut, Alexei J. Drummond (2006)

Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.

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Differential Susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika Virus

Thais Chouin-Carneiro, Anubis Vega-Rúa, Marie Vazeille … (2016)

Background Since the major outbreak in 2007 in the Yap Island, Zika virus (ZIKV) causing dengue-like syndromes has affected multiple islands of the South Pacific region. In May 2015, the virus was detected in Brazil and then spread through South and Central America. In December 2015, ZIKV was detected in French Guiana and Martinique. The aim of the study was to evaluate the vector competence of the mosquito spp. Aedes aegypti and Aedes albopictus from the Caribbean (Martinique, Guadeloupe), North America (southern United States), South America (Brazil, French Guiana) for the currently circulating Asian genotype of ZIKV isolated from a patient in April 2014 in New Caledonia. Methodology/Principal Findings Mosquitoes were orally exposed to an Asian genotype of ZIKV (NC-2014-5132). Upon exposure, engorged mosquitoes were maintained at 28°±1°C, a 16h:8h light:dark cycle and 80% humidity. 25–30 mosquitoes were processed at 4, 7 and 14 days post-infection (dpi). Mosquito bodies (thorax and abdomen), heads and saliva were analyzed to measure infection, dissemination and transmission, respectively. High infection but lower disseminated infection and transmission rates were observed for both Ae. aegypti and Ae. albopictus. Ae. aegypti populations from Guadeloupe and French Guiana exhibited a higher dissemination of ZIKV than the other Ae. aegypti populations examined. Transmission of ZIKV was observed in both mosquito species at 14 dpi but at a low level. Conclusions/Significance This study suggests that although susceptible to infection, Ae. aegypti and Ae. albopictus were unexpectedly low competent vectors for ZIKV. This may suggest that other factors such as the large naïve population for ZIKV and the high densities of human-biting mosquitoes contribute to the rapid spread of ZIKV during the current outbreak.

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Author and article information

Journal

Journal ID (nlm-journal-id): 0410462

Journal ID (pubmed-jr-id): 6011

Journal ID (nlm-ta): Nature

Journal ID (iso-abbrev): Nature

Title: Nature

ISSN (Print): 0028-0836

ISSN (Electronic): 1476-4687

Publication date Nihms-submitted: 30 April 2017

Publication date (Electronic): 24 May 2017

Publication date (Print): 15 June 2017

Publication date PMC-release: 24 November 2017

Volume: 546

Issue: 7658

Pages: 401-405

Affiliations

[1 ]Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, CA 92037, USA

[2 ]Center for Genome Sciences, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702, USA

[3 ]Department of Zoology, University of Oxford, Oxford OX1 3PS, UK

[4 ]Boston Children’s Hospital, Boston, MA 02115, USA

[5 ]Harvard Medical School, Boston, MA 02115, USA

[6 ]Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA

[7 ]Department of Biological Sciences, College of Arts and Sciences, Florida Gulf Coast University, Fort Myers, FL 33965, USA

[8 ]Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Miami, FL 33125, USA

[9 ]Department of Pathology, University of Miami Miller School of Medicine, Miami, FL 33136, USA

[10 ]Bureau of Epidemiology, Division of Disease Control and Health Protection, Florida Department of Health, Tallahassee, FL 32399, USA

[11 ]Scripps Translational Science Institute, La Jolla, CA 92037, USA

[12 ]The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

[13 ]Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK

[14 ]Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80523, USA

[15 ]Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, ON M5B 1T8, Canada

[16 ]Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON M5B 1T8, Canada

[17 ]Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121, USA

[18 ]Division of Infectious Diseases, University of Miami Miller School of Medicine, Miami, FL 33155, USA

[19 ]Bureau of Public Health Laboratories, Division of Disease Control and Health Protection, Florida Department of Health, Tampa, FL 33612, USA

[20 ]Florida Department of Health in Miami-Dade County, Miami, FL 33125, USA

[21 ]National Center for Atmospheric Research, Boulder, CO 80307, USA

[22 ]Department of Microbiology and Immunology, Tulane University School of Medicine, New Orleans, LA 70112, USA

[23 ]Miami-Dade County Mosquito Control, Miami, FL 33178 USA

[24 ]Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA

[25 ]Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK

[26 ]Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA

[27 ]Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge MA 02138, USA

[28 ]Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston MA 02115, USA

[29 ]Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA

[30 ]Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA

Author notes

Correspondence and requests for materials should be addressed to K.G.A ( andersen@ 123456scripps.edu ) or G.P. ( gustavo.f.palacios.ctr@ 123456mail.mil )

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Manuscript ID: NIHMS872467

DOI: 10.1038/nature22400

PMC ID: 5536180

PubMed ID: 28538723

SO-VID: 7df52784-6b14-440f-bff6-56c19d9f3da7

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Genomic epidemiology reveals multiple introductions of Zika virus into the United States

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Abstract

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Zika Virus

Most cited references 51

Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences.

Differential Susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika Virus

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