1
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Nanduri S, Black A, Bedford T, Huddleston J. Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.07.579374. [PMID: 39253501 PMCID: PMC11383015 DOI: 10.1101/2024.02.07.579374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.
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2
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May MR, Rannala B. Early detection of highly transmissible viral variants using phylogenomics. SCIENCE ADVANCES 2024; 10:eadk7623. [PMID: 39141727 PMCID: PMC11323880 DOI: 10.1126/sciadv.adk7623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 07/09/2024] [Indexed: 08/16/2024]
Abstract
As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a serious threat to global health. Statistical models of genome sequence evolution may provide a critical tool for early detection of these strains. Using a novel stochastic model that links transmission rates to the entire viral genome sequence, we study the utility of phylogenetic methods that use a phylogenetic tree relating viral samples versus count-based methods that use case counts of variants over time exclusively to detect increased transmission rates and identify candidate causative mutations. We find that phylogenies in particular can detect novel transmission-enhancing variants very soon after their origin and may facilitate the development of early detection systems for outbreak surveillance.
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Affiliation(s)
- Michael R. May
- Department of Evolution and Ecology, University of California Davis, Davis, CA, USA
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3
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Welsh FC, Eguia RT, Lee JM, Haddox HK, Galloway J, Van Vinh Chau N, Loes AN, Huddleston J, Yu TC, Quynh Le M, Nhat NTD, Thi Le Thanh N, Greninger AL, Chu HY, Englund JA, Bedford T, Matsen FA, Boni MF, Bloom JD. Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin. Cell Host Microbe 2024; 32:1397-1411.e11. [PMID: 39032493 PMCID: PMC11329357 DOI: 10.1016/j.chom.2024.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/19/2024] [Accepted: 06/25/2024] [Indexed: 07/23/2024]
Abstract
Human influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population and how this heterogeneity affects virus evolution. Here, we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of two H3N2 strains, A/Hong Kong/45/2019 and A/Perth/16/2009, affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that were fixed in influenza variants after 2020 cause greater escape from sera from younger individuals compared with adults. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups and suggest approaches to understand how this heterogeneous selection shapes viral evolution.
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MESH Headings
- Humans
- Hemagglutinin Glycoproteins, Influenza Virus/genetics
- Hemagglutinin Glycoproteins, Influenza Virus/immunology
- Influenza A Virus, H3N2 Subtype/genetics
- Influenza A Virus, H3N2 Subtype/immunology
- Mutation
- Adult
- Antibodies, Viral/immunology
- Antibodies, Viral/blood
- Influenza, Human/virology
- Influenza, Human/immunology
- Age Factors
- Middle Aged
- Young Adult
- Antibodies, Neutralizing/immunology
- Antibodies, Neutralizing/blood
- Antigens, Viral/genetics
- Antigens, Viral/immunology
- Adolescent
- Evolution, Molecular
- Aged
- Child
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Affiliation(s)
- Frances C Welsh
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA 98109, USA; Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Rachel T Eguia
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Seattle, WA 98109, USA
| | - Juhye M Lee
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Seattle, WA 98109, USA
| | - Hugh K Haddox
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jared Galloway
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Nguyen Van Vinh Chau
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Andrea N Loes
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Seattle, WA 98109, USA
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Timothy C Yu
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA 98109, USA; Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Mai Quynh Le
- National Institutes for Hygiene and Epidemiology, Hanoi, Vietnam
| | - Nguyen T D Nhat
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam; Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Nguyen Thi Le Thanh
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Alexander L Greninger
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA; Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Helen Y Chu
- Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Janet A Englund
- Seattle Children's Research Institute, Seattle, WA 98109, USA
| | - Trevor Bedford
- Howard Hughes Medical Institute, Seattle, WA 98109, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Frederick A Matsen
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Seattle, WA 98109, USA
| | - Maciej F Boni
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam; Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Seattle, WA 98109, USA.
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4
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Sobel Leonard A, Mendoza L, McFarland AG, Marques AD, Everett JK, Moncla L, Bushman FD, Odom John AR, Hensley SE. Within-host influenza viral diversity in the pediatric population as a function of age, vaccine, and health status. Virus Evol 2024; 10:veae034. [PMID: 38859985 PMCID: PMC11163376 DOI: 10.1093/ve/veae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/23/2024] [Accepted: 04/22/2024] [Indexed: 06/12/2024] Open
Abstract
Seasonal influenza virus predominantly evolves through antigenic drift, marked by the accumulation of mutations at antigenic sites. Because of antigenic drift, influenza vaccines are frequently updated, though their efficacy may still be limited due to strain mismatches. Despite the high levels of viral diversity observed across populations, most human studies reveal limited intrahost diversity, leaving the origin of population-level viral diversity unclear. Previous studies show host characteristics, such as immunity, might affect within-host viral evolution. Here we investigate influenza A viral diversity in children aged between 6 months and 18 years. Influenza virus evolution in children is less well characterized than in adults, yet may be associated with higher levels of viral diversity given the lower level of pre-existing immunity and longer durations of infection in children. We obtained influenza isolates from banked influenza A-positive nasopharyngeal swabs collected at the Children's Hospital of Philadelphia during the 2017-18 influenza season. Using next-generation sequencing, we evaluated the population of influenza viruses present in each sample. We characterized within-host viral diversity using the number and frequency of intrahost single-nucleotide variants (iSNVs) detected in each sample. We related viral diversity to clinical metadata, including subjects' age, vaccination status, and comorbid conditions, as well as sample metadata such as virus strain and cycle threshold. Consistent with previous studies, most samples contained low levels of diversity with no clear association between the subjects' age, vaccine status, or health status. Further, there was no enrichment of iSNVs near known antigenic sites. Taken together, these findings are consistent with previous observations that the majority of intrahost influenza virus infection is characterized by low viral diversity without evidence of diversifying selection.
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Affiliation(s)
- Ashley Sobel Leonard
- Division of Infectious Diseases, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Lydia Mendoza
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Alexander G McFarland
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Andrew D Marques
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - John K Everett
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Louise Moncla
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, 3800 Spruce St., Philadelphia, PA 19104, USA
| | - Frederic D Bushman
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Audrey R Odom John
- Division of Infectious Diseases, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, 3800 Spruce St., Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
| | - Scott E Hensley
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104, USA
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5
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Celentano M, DeWitt WS, Prillo S, Song YS. Exact and efficient phylodynamic simulation from arbitrarily large populations. ARXIV 2024:arXiv:2402.17153v1. [PMID: 38463501 PMCID: PMC10925381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Many biological studies involve inferring the genealogical history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling (BDMS) model, there exists an equivalent pure birth process (i.e., no death) with mutation and complete sampling. The final trees generated under these processes have exactly the same distribution. Leveraging this property, we propose a highly efficient algorithm for simulating trees under a general BDMS model. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.
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Affiliation(s)
| | | | | | - Yun S Song
- Computer Science Division, University of California, Berkeley
- Department of Statistics, University of California, Berkeley
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6
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Welsh FC, Eguia RT, Lee JM, Haddox HK, Galloway J, Chau NVV, Loes AN, Huddleston J, Yu TC, Le MQ, Nhat NTD, Thanh NTL, Greninger AL, Chu HY, Englund JA, Bedford T, Matsen FA, Boni MF, Bloom JD. Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571235. [PMID: 38168237 PMCID: PMC10760046 DOI: 10.1101/2023.12.12.571235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Human influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population, and how this heterogeneity affects virus evolution. Here we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of the A/Hong Kong/45/2019 (H3N2) and A/Perth/16/2009 (H3N2) strains affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that fixed in influenza variants after 2020 cause the greatest escape from sera from younger individuals. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups, and suggest approaches to understand how this heterogeneous selection shapes viral evolution.
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Affiliation(s)
- Frances C Welsh
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA, 98109, USA
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Rachel T Eguia
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Juhye M Lee
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Hugh K Haddox
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jared Galloway
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Nguyen Van Vinh Chau
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Andrea N Loes
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Timothy C Yu
- Molecular and Cellular Biology Graduate Program, University of Washington, and Basic Sciences Division, Fred Hutch Cancer Center, Seattle, WA, 98109, USA
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Mai Quynh Le
- National Institutes for Hygiene and Epidemiology, Hanoi, Vietnam
| | - Nguyen T D Nhat
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Nguyen Thi Le Thanh
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Alexander L Greninger
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Helen Y Chu
- Division of Allergy and Infectious Diseases, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Janet A Englund
- Seattle Children's Research Institute, Seattle, WA, 98109, USA
| | - Trevor Bedford
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Frederick A Matsen
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
| | - Maciej F Boni
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98109, USA
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7
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Kistler KE, Bedford T. An atlas of continuous adaptive evolution in endemic human viruses. Cell Host Microbe 2023; 31:1898-1909.e3. [PMID: 37883977 DOI: 10.1016/j.chom.2023.09.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/25/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
Through antigenic evolution, viruses such as seasonal influenza evade recognition by neutralizing antibodies. This means that a person with antibodies well tuned to an initial infection will not be protected against the same virus years later and that vaccine-mediated protection will decay. To expand our understanding of which endemic human viruses evolve in this fashion, we assess adaptive evolution across the genome of 28 endemic viruses spanning a wide range of viral families and transmission modes. Surface proteins consistently show the highest rates of adaptation, and ten viruses in this panel are estimated to undergo antigenic evolution to selectively fix mutations that enable the escape of prior immunity. Thus, antibody evasion is not an uncommon evolutionary strategy among human viruses, and monitoring this evolution will inform future vaccine efforts. Additionally, by comparing overall amino acid substitution rates, we show that SARS-CoV-2 is accumulating protein-coding changes at substantially faster rates than endemic viruses.
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Affiliation(s)
- Kathryn E Kistler
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA, USA.
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA, USA
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8
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May MR, Rannala B. Phylogenies increase power to detect highly transmissible viral genome variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.28.23293332. [PMID: 37577556 PMCID: PMC10418580 DOI: 10.1101/2023.07.28.23293332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a significant threat to global health. Viral genome sequences, combined with statistical models of sequence evolution, may provide a critical tool for early detection of these strains. Using a novel statistical model that links transmission rates to the entire viral genome sequence, we study the power of phylogenetic methods-using a phylogenetic tree relating viral samples-and count-based methods-using case-counts of variants over time-to detect increased transmission rates, and to identify causative mutations. We find that phylogenies in particular can detect novel variants very soon after their origin, and may facilitate the development of early detection systems for outbreak surveillance.
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Affiliation(s)
- Michael R May
- Department of Evolution and Ecology, University of California Davis, Davis, CA USA
| | - Bruce Rannala
- Department of Evolution and Ecology, University of California Davis, Davis, CA USA
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9
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Ren H, Ling Y, Cao R, Wang Z, Li Y, Huang T. Early warning of emerging infectious diseases based on multimodal data. BIOSAFETY AND HEALTH 2023; 5:S2590-0536(23)00074-5. [PMID: 37362865 PMCID: PMC10245235 DOI: 10.1016/j.bsheal.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/18/2023] [Accepted: 05/31/2023] [Indexed: 06/28/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.
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Affiliation(s)
- Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yunchao Ling
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruifang Cao
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhen Wang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024 China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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10
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Pillai TK, Johnson KE, Song T, Gregianini TS, Tatiana G. B, Wang G, Medina RA, Van Bakel H, García-Sastre A, Nelson MI, Ghedin E, Veiga ABG. Tracking the emergence of antigenic variants in influenza A virus epidemics in Brazil. Virus Evol 2023; 9:vead027. [PMID: 37207002 PMCID: PMC10191192 DOI: 10.1093/ve/vead027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/04/2023] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
Influenza A virus (IAV) circulation patterns differ in North America and South America, with influenza seasons often characterized by different subtypes and strains. However, South America is relatively undersampled considering the size of its population. To address this gap, we sequenced the complete genomes of 220 IAVs collected between 2009 and 2016 from hospitalized patients in southern Brazil. New genetic drift variants were introduced into southern Brazil each season from a global gene pool, including four H3N2 clades (3c, 3c2, 3c3, and 3c2a) and five H1N1pdm clades (clades 6, 7, 6b, 6c, and 6b1). In 2016, H1N1pdm viruses belonging to a new 6b1 clade caused a severe influenza epidemic in southern Brazil that arrived early and spread rapidly, peaking mid-autumn. Inhibition assays showed that the A/California/07/2009(H1N1) vaccine strain did not protect well against 6b1 viruses. Phylogenetically, most 6b1 sequences that circulated in southern Brazil belong to a single transmission cluster that rapidly diffused across susceptible populations, leading to the highest levels of influenza hospitalization and mortality seen since the 2009 pandemic. Continuous genomic surveillance is needed to monitor rapidly evolving IAVs for vaccine strain selection and understand their epidemiological impact in understudied regions.
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Affiliation(s)
- Tara K Pillai
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
| | - Katherine E Johnson
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
- Department of Biology, Center for Genomics & Systems Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Timothy Song
- Department of Biology, Center for Genomics & Systems Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Tatiana S Gregianini
- Laboratório Central de Saúde Pública, Centro Estadual de Vigilância em Saúde da Secretaria de Saúde do Estado do Rio Grande do Sul—LACEN/CEVS/SES‐RS, Av. Ipiranga, 5400, Porto Alegre, RS 90450-190, Brazil
| | - Baccin Tatiana G.
- Graduate Program in Pathology, Universidade Federal de Ciências da Saúde de Porto Alegre, Rua Sarmento Leite, 245, Rio Grande do Sul, RS 90050-170, Brazil
- Department of Pediatric Infectious Diseases and Immunology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Marcoleta 391, Santiago, RM 8330024, Chile
| | - Guojun Wang
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Rafael A Medina
- Department of Pediatric Infectious Diseases and Immunology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Marcoleta 391, Santiago, RM 8330024, Chile
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Department of Pathology and Experimental Medicine, School of Medicine, Emory University, 1462 Clifton Road, Office 429, Atlanta, GA 30322, USA
| | - Harm Van Bakel
- Laboratory of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Martha I Nelson
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Elodie Ghedin
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, NIH, 50 South Drive, Bethesda, MD 20894, USA
- Department of Biology, Center for Genomics & Systems Biology, New York University, 12 Waverly Place, New York, NY 10003, USA
| | - Ana B G Veiga
- Graduate Program in Pathology, Universidade Federal de Ciências da Saúde de Porto Alegre, Rua Sarmento Leite, 245, Rio Grande do Sul, RS 90050-170, Brazil
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA
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11
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Lee J, Hadfield J, Black A, Sibley TR, Neher RA, Bedford T, Huddleston J. Joint visualization of seasonal influenza serology and phylogeny to inform vaccine composition. FRONTIERS IN BIOINFORMATICS 2023; 3:1069487. [PMID: 37035035 PMCID: PMC10073671 DOI: 10.3389/fbinf.2023.1069487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/08/2023] [Indexed: 04/11/2023] Open
Abstract
Seasonal influenza vaccines must be updated regularly to account for mutations that allow influenza viruses to escape our existing immunity. A successful vaccine should represent the genetic diversity of recently circulating viruses and induce antibodies that effectively prevent infection by those recent viruses. Thus, linking the genetic composition of circulating viruses and the serological experimental results measuring antibody efficacy is crucial to the vaccine design decision. Historically, genetic and serological data have been presented separately in the form of static visualizations of phylogenetic trees and tabular serological results to identify vaccine candidates. To simplify this decision-making process, we have created an interactive tool for visualizing serological data that has been integrated into Nextstrain's real-time phylogenetic visualization framework, Auspice. We show how the combined interactive visualizations may be used by decision makers to explore the relationships between complex data sets for both prospective vaccine virus selection and retrospectively exploring the performance of vaccine viruses.
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Affiliation(s)
- Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Allison Black
- Chan Zuckerberg Initiative, San Francisco, CA, United States
| | - Thomas R. Sibley
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Richard A. Neher
- Biozentrum, Universität Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Howard Hughes Medical Institute, Seattle, WA, United States
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
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12
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Braun KM, Haddock III LA, Crooks CM, Barry GL, Lalli J, Neumann G, Watanabe T, Imai M, Yamayoshi S, Ito M, Moncla LH, Koelle K, Kawaoka Y, Friedrich TC. Avian H7N9 influenza viruses are evolutionarily constrained by stochastic processes during replication and transmission in mammals. Virus Evol 2023; 9:vead004. [PMID: 36814938 PMCID: PMC9939568 DOI: 10.1093/ve/vead004] [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: 05/13/2022] [Revised: 01/05/2023] [Accepted: 01/17/2023] [Indexed: 01/20/2023] Open
Abstract
H7N9 avian influenza viruses (AIVs) have caused over 1,500 documented human infections since emerging in 2013. Although wild-type H7N9 AIVs can be transmitted by respiratory droplets in ferrets, they have not yet caused widespread outbreaks in humans. Previous studies have revealed molecular determinants of H7N9 AIV host switching, but little is known about potential evolutionary constraints on this process. Here, we compare patterns of sequence evolution for H7N9 AIV and mammalian H1N1 viruses during replication and transmission in ferrets. We show that three main factors-purifying selection, stochasticity, and very narrow transmission bottlenecks-combine to severely constrain the ability of H7N9 AIV to effectively adapt to mammalian hosts in isolated, acute spillover events. We find rare evidence of natural selection favoring new, potentially mammal-adapting mutations within ferrets but no evidence of natural selection acting during transmission. We conclude that human-adapted H7N9 viruses are unlikely to emerge during typical spillover infections. Our findings are instead consistent with a model in which the emergence of a human-transmissible virus would be a rare and unpredictable, though highly consequential, 'jackpot' event. Strategies to control the total number of spillover infections will limit opportunities for the virus to win this evolutionary lottery.
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Affiliation(s)
| | | | - Chelsea M Crooks
- AIDS Vaccine Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 585 Science Dr. Madison, WI 53711, USA
| | - Gabrielle L Barry
- AIDS Vaccine Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 585 Science Dr. Madison, WI 53711, USA
| | - Joseph Lalli
- Department of Genetics, University of Wisconsin-Madison, 425 Henry Mall Madison, WI 53706, US
| | - Gabriele Neumann
- Influenza Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 575 Science Dr. Madison, WI 53711, USA
| | - Tokiko Watanabe
- Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan,Department of Molecular Virology, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka Suita City, Osaka 565-0871, Japan,Center for Infectious Disease Education and Research (CiDER), Osaka University, 2-8 Yamadaoka Suita City, Osaka 565-0871, Japan
| | - Masaki Imai
- Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan,The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, 1 Chome-21-1 Toyama Shinjuku City, Tokyo 162-8655, Japan
| | | | - Mutsumi Ito
- Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan
| | | | | | - Yoshihiro Kawaoka
- Influenza Research Institute, Department of Pathobiological Sciences, University of Wisconsin-Madison, 575 Science Dr. Madison, WI 53711, USA,Division of Virology, Institute of Medical Science, University of Tokyo, 4 Chome-6-1 Shirokanedai Minato City, Tokyo 108-0071, Japan,The Research Center for Global Viral Diseases, National Center for Global Health and Medicine Research Institute, 1 Chome-21-1 Toyama Shinjuku City, Tokyo 162-8655, Japan
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13
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Phillips AM, Maurer DP, Brooks C, Dupic T, Schmidt AG, Desai MM. Hierarchical sequence-affinity landscapes shape the evolution of breadth in an anti-influenza receptor binding site antibody. eLife 2023; 12:83628. [PMID: 36625542 PMCID: PMC9995116 DOI: 10.7554/elife.83628] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the VH-VL interface is essential for binding a divergent H1. Collectively, this is the first work to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.
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Affiliation(s)
- Angela M Phillips
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
| | - Daniel P Maurer
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Caelan Brooks
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Thomas Dupic
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - Aaron G Schmidt
- Ragon Institute of MGH, MIT, and HarvardCambridgeUnited States
- Department of Microbiology, Harvard Medical SchoolBostonUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
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14
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Henry B, Méléard S, Tran VC. Time reversal of spinal processes for linear and non-linear branching processes near stationarity. ELECTRON J PROBAB 2023. [DOI: 10.1214/23-ejp911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Affiliation(s)
- Benoît Henry
- IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, F-59000 Lille, France
| | - Sylvie Méléard
- CMAP, CNRS, Ecole polytechnique, Institut polytechnique de Paris, 91120 Palaiseau, France and Institut Universitaire de France
| | - Viet Chi Tran
- LAMA, Univ Gustave Eiffel, Univ Paris Est Creteil, CNRS, F-77454 Marne-la-Vallée, France
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15
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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16
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Cárdenas P, Corredor V, Santos-Vega M. Genomic epidemiological models describe pathogen evolution across fitness valleys. SCIENCE ADVANCES 2022; 8:eabo0173. [PMID: 35857510 PMCID: PMC9278859 DOI: 10.1126/sciadv.abo0173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Genomics is fundamentally changing epidemiological research. However, systematically exploring hypotheses in pathogen evolution requires new modeling tools. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmissibility or resistance to treatment. In this work, we present Opqua, a flexible simulation framework that explicitly links epidemiology to sequence evolution and selection. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high-transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling of selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.
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Affiliation(s)
- Pablo Cárdenas
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vladimir Corredor
- Departamento de Salud Pública, Facultad de Medicina, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Mauricio Santos-Vega
- Grupo Biología Matemática y Computacional, Departamento Ingeniería Biomédica, Universidad de los Andes, Bogotá, D.C., Colombia
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17
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STAMINA: Bioinformatics Platform for Monitoring and Mitigating Pandemic Outbreaks. TECHNOLOGIES 2022. [DOI: 10.3390/technologies10030063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
This paper presents the components and integrated outcome of a system that aims to achieve early detection, monitoring and mitigation of pandemic outbreaks. The architecture of the platform aims at providing a number of pandemic-response-related services, on a modular basis, that allows for the easy customization of the platform to address user’s needs per case. This customization is achieved through its ability to deploy only the necessary, loosely coupled services and tools for each case, and by providing a common authentication, data storage and data exchange infrastructure. This way, the platform can provide the necessary services without the burden of additional services that are not of use in the current deployment (e.g., predictive models for pathogens that are not endemic to the deployment area). All the decisions taken for the communication and integration of the tools that compose the platform adhere to this basic principle. The tools presented here as well as their integration is part of the project STAMINA.
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18
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Liao H, Cai D, Sun Y. VirStrain: a strain identification tool for RNA viruses. Genome Biol 2022; 23:38. [PMID: 35101081 PMCID: PMC8801933 DOI: 10.1186/s13059-022-02609-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 01/12/2022] [Indexed: 12/18/2022] Open
Abstract
Viruses change constantly during replication, leading to high intra-species diversity. Although many changes are neutral or deleterious, some can confer on the virus different biological properties such as better adaptability. In addition, viral genotypes often have associated metadata, such as host residence, which can help with inferring viral transmission during pandemics. Thus, subspecies analysis can provide important insights into virus characterization. Here, we present VirStrain, a tool taking short reads as input with viral strain composition as output. We rigorously test VirStrain on multiple simulated and real virus sequencing datasets. VirStrain outperforms the state-of-the-art tools in both sensitivity and accuracy.
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Affiliation(s)
- Herui Liao
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, China
| | - Dehan Cai
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, China
| | - Yanni Sun
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, China.
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19
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OUP accepted manuscript. Syst Biol 2022; 71:1378-1390. [DOI: 10.1093/sysbio/syac008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 02/05/2022] [Accepted: 02/08/2022] [Indexed: 11/12/2022] Open
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20
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Molecular epidemiologic characteristics of hemagglutinin from five waves of avian influenza A (H7N9) virus infection, from 2013 to 2017, in Zhejiang Province, China. Arch Virol 2021; 166:3323-3332. [PMID: 34595553 PMCID: PMC8616886 DOI: 10.1007/s00705-021-05233-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 07/19/2021] [Indexed: 11/22/2022]
Abstract
There have been five waves of influenza A (H7N9) epidemics in Zhejiang Province between 2013 and 2017. Although the epidemiological characteristics of the five waves have been reported, the molecular genetics aspects, including the phylogeny, evolution, and mutation of hemagglutinin (HA), have not been systematically investigated. A total of 154 H7N9 samples from Zhejiang Province were collected between 2013 and 2017 and sequenced using an Ion Torrent Personal Genome Machine. The starting dates of the waves were 16 March 2013, 1 July 2013, 1 July 2014, 1 July 2015, and 1 July 2016. Single-nucleotide polymorphisms (SNPs) and amino acid mutations were counted after the HA sequences were aligned. The evolution of H7N9 matched the temporal order of the five waves, among which wave 3 played an important role. The 55 SNPs and 14 amino acid mutations with high frequency identified among the five waves revealed the dynamic occurrence of mutation in the process of viral dissemination. Wave 3 contributed greatly to the subsequent epidemic of waves 4 and 5 of H7N9. Compared with wave 1, wave 5 was characterized by more mutations, including A143V and R148K, two mutations that have been reported to weaken the immune response. In addition, some amino acid mutations were observed in wave 5 that led to more lineages. It is necessary to strengthen the surveillance of subsequent H7N9 influenza outbreaks.
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21
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Regional differences in vaccine uptake and serological responses to vaccine and circulating strains of H1N1 viruses among patients with confirmed influenza. JOURNAL OF CLINICAL VIROLOGY PLUS 2021. [DOI: 10.1016/j.jcvp.2021.100034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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22
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Metaheuristics for multiple sequence alignment: A systematic review. Comput Biol Chem 2021; 94:107563. [PMID: 34425495 DOI: 10.1016/j.compbiolchem.2021.107563] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/04/2021] [Accepted: 08/09/2021] [Indexed: 11/21/2022]
Abstract
The Multiple Sequence Alignment (MSA) is a key task in bioinformatics, because it is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform MSA and the use of metaheuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. This paper presents a Systematic Literature Review (SLR) on metaheuristics for MSA, compiling relevant works published between 2014 and 2019. The results of our SLR show the constant interest in this subject, due to the several recent publications that use different metaheuristics to obtain more accurate alignments. Moreover, the final results of our SLR show a multi-objective and hybrid approaches trends, which generally leads these methods to achieve even better results. Thus, we show in this work how the use of metaheuristics to perform MSA still remains an important and promising open research field.
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23
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Chamie G, Marquez C, Crawford E, Peng J, Petersen M, Schwab D, Schwab J, Martinez J, Jones D, Black D, Gandhi M, Kerkhoff AD, Jain V, Sergi F, Jacobo J, Rojas S, Tulier-Laiwa V, Gallardo-Brown T, Appa A, Chiu C, Rodgers M, Hackett J, Kistler A, Hao S, Kamm J, Dynerman D, Batson J, Greenhouse B, DeRisi J, Havlir DV. Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 Disproportionately Affects the Latinx Population During Shelter-in-Place in San Francisco. Clin Infect Dis 2021; 73:S127-S135. [PMID: 32821935 PMCID: PMC7499499 DOI: 10.1093/cid/ciaa1234] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 08/17/2020] [Indexed: 12/21/2022] Open
Abstract
Background There is urgent need to understand the dynamics and risk factors driving ongoing SARS-CoV-2 transmission during shelter-in-place mandates. Methods We offered SARS-CoV-2 reverse transcription-PCR and antibody (Abbott ARCHITECT IgG) testing, regardless of symptoms, to all residents (≥4 years) and workers in a San Francisco census tract (population: 5,174) at outdoor, community-mobilized events over four days. We estimated SARS-CoV-2 point prevalence (PCR-positive) and cumulative incidence (antibody or PCR-positive) in the census tract and evaluated risk factors for recent (PCR-positive/antibody-negative) versus prior infection (antibody-positive/PCR-negative). SARS-CoV-2 genome recovery and phylogenetics were used to measure viral strain diversity, establish viral lineages present, and estimate number of introductions. Results We tested 3,953 persons: 40% Latinx; 41% White; 9% Asian/Pacific Islander; and 2% Black. Overall, 2.1% (83/3,871) tested PCR-positive: 95% were Latinx and 52% asymptomatic when tested. 1.7% of census tract residents and 6.0% of workers (non-census tract residents) were PCR-positive. Among 2,598 tract residents, estimated point prevalence of PCR-positives was 2.3% (95%CI: 1.2-3.8%): 3.9% (95%CI: 2.0-6.4%) among Latinx vs. 0.2% (95%CI: 0.0-0.4%) among non-Latinx persons. Estimated cumulative incidence among residents was 6.1% (95%CI: 4.0-8.6%). Prior infections were 67% Latinx, 16% White, and 17% other ethnicities. Among recent infections, 96% were Latinx. Risk factors for recent infection were Latinx ethnicity, inability to shelter-in-place and maintain income, frontline service work, unemployment, and household income &$50,000/year. Five SARS-CoV-2 phylogenetic lineages were detected. Conclusion SARS-CoV-2 infections from diverse lineages continued circulating among low-income, Latinx persons unable to work from home and maintain income during San Francisco’s shelter-in-place ordinance.
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Affiliation(s)
- Gabriel Chamie
- University of California, San Francisco, San Francisco, California, USA
| | - Carina Marquez
- University of California, San Francisco, San Francisco, California, USA
| | - Emily Crawford
- University of California, San Francisco, San Francisco, California, USA.,Chan Zuckerberg Biohub, San Francisco, California, USA
| | - James Peng
- University of California, San Francisco, San Francisco, California, USA
| | - Maya Petersen
- University of California, Berkeley, Berkeley, California, USA
| | - Daniel Schwab
- College of the Holy Cross, Worcester, Massachusetts, USA
| | - Joshua Schwab
- University of California, Berkeley, Berkeley, California, USA
| | - Jackie Martinez
- University of California, San Francisco, San Francisco, California, USA
| | - Diane Jones
- Unidos en Salud/United in Health, San Francisco, California, USA
| | - Douglas Black
- University of California, San Francisco, San Francisco, California, USA
| | - Monica Gandhi
- University of California, San Francisco, San Francisco, California, USA
| | - Andrew D Kerkhoff
- University of California, San Francisco, San Francisco, California, USA
| | - Vivek Jain
- University of California, San Francisco, San Francisco, California, USA
| | - Francesco Sergi
- University of California, San Francisco, San Francisco, California, USA
| | - Jon Jacobo
- Latino Task Force for COVID-19, San Francisco, California, USA
| | - Susana Rojas
- Latino Task Force for COVID-19, San Francisco, California, USA
| | | | | | - Ayesha Appa
- University of California, San Francisco, San Francisco, California, USA
| | - Charles Chiu
- University of California, San Francisco, San Francisco, California, USA
| | | | | | | | - Amy Kistler
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Samantha Hao
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Jack Kamm
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | | | - Joshua Batson
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Bryan Greenhouse
- University of California, San Francisco, San Francisco, California, USA
| | - Joe DeRisi
- University of California, San Francisco, San Francisco, California, USA.,Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Diane V Havlir
- University of California, San Francisco, San Francisco, California, USA
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24
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Piantham C, Ito K. Modeling the selective advantage of new amino acids on the hemagglutinin of H1N1 influenza viruses using their patient age distributions. Virus Evol 2021; 7:veab049. [PMID: 34285812 PMCID: PMC8286795 DOI: 10.1093/ve/veab049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
In 2009, a new strain of H1N1 influenza A virus caused a pandemic, and its descendant strains are causing seasonal epidemics worldwide. Given the high mutation rate of influenza viruses, variant strains having different amino acids on hemagglutinin (HA) continuously emerge. To prepare vaccine strains for the next influenza seasons, it is an urgent task to predict which variants will be selected in the viral population. An analysis of 24,681 pairs of an amino acid sequence of HA of H1N1pdm2009 viruses and its patient age showed that the empirical fixation probability of new amino acids on HA significantly differed depending on their frequencies in the population, patient age distributions, and epitope flags. The selective advantage of a variant strain having a new amino acid was modeled by linear combinations of patients age distributions and epitope flags, and then the fixation probability of the new amino acid was modeled using Kimura’s formula for advantageous selection. The parameters of models were estimated from the sequence data and models were tested with four-fold cross validations. The frequency of new amino acids alone can achieve high sensitivity, specificity, and precision in predicting the fixation of a new amino acid of which frequency is more than 0.11. The estimated parameter suggested that viruses with a new amino acid having a frequency in the population higher than 0.11 have a significantly higher selective advantage compared to viruses with the old amino acid at the same position. The model considering the Z-value of patient age rank-sums of new amino acids predicted amino acid substitutions on HA with a sensitivity of 0.78, specificity of 0.86, and precision of 0.83, showing significant improvement compared to the constant selective advantage model, which used only the frequency of the amino acid. These results suggested that H1N1 viruses tend to be selected in the adult population, and frequency of viruses having new amino acids and their patient ages are useful to predict amino acid substitutions on HA.
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Affiliation(s)
- Chayada Piantham
- Division of Bioinformatics, Graduate School of Infectious Diseases, Hokkaido University, Sapporo 0600818, Japan
| | - Kimihito Ito
- Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo 0010020, Japan
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25
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Liu H, Gong YN, Shaw-Saliba K, Mehoke T, Evans J, Liu ZY, Lewis M, Sauer L, Thielen P, Rothman R, Chen KF, Pekosz A. Differential disease severity and whole-genome sequence analysis for human influenza A/H1N1pdm virus in 2015-2016 influenza season. Virus Evol 2021; 7:veab044. [PMID: 34040796 PMCID: PMC8135377 DOI: 10.1093/ve/veab044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
During the 2015–16 winter, the US experienced a relatively mild influenza season compared to Taiwan, which had a higher number of total and severe cases. While H1N1pdm viruses dominated global surveillance efforts that season, the global distribution of genetic variants and their contributions to disease severity have not been investigated. Samples collected from influenza A-positive patients by the Johns Hopkins Center of Excellence for Influenza Research and Surveillance active surveillance in the emergency rooms in Baltimore, Maryland, USA, and northern Taiwan between November 2015 and April 2016, were processed for influenza A virus whole-genome sequencing. In Baltimore, the majority of the viruses were the H1N1pdm clade 6B.1 and no H1N1pdm clade 6B.2 viruses were detected. In northern Taiwan, more than half of the H1N1pdm viruses were clade 6B.1 and 38% were clade 6B.2, consistent with the global observation that most 6B.2 viruses circulated in Asia and not North America. Whole virus genome sequence analysis identified two genetic subgroups present in each of the 6B.1 and 6B.2 clades and one 6B.1 interclade reassortant virus. Clinical data showed 6B.2 patients had more disease symptoms including higher crude and inverse probability weighted odds of pneumonia than 6B.1 patients, suggesting 6B.2 circulation may be one of the reasons for the severe flu season in Taiwan. Local surveillance efforts linking H1N1pdm virus sequences to patient clinical and demographic data improve our understanding of influenza circulation and disease potential.
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Affiliation(s)
- Hsuan Liu
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Yu-Nong Gong
- Research Center for Emerging Viral Infections, College of Medicine, Chang Gung University, Taoyuan, Taiwan.,Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Kathryn Shaw-Saliba
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.,Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Thomas Mehoke
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, Maryland, 20723, USA
| | - Jared Evans
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, Maryland, 20723, USA
| | - Zhen-Ying Liu
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Mitra Lewis
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Lauren Sauer
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Peter Thielen
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, Maryland, 20723, USA
| | - Richard Rothman
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Kuan-Fu Chen
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan.,Clinical Informatics and Medical Statistics Research Center, Chang Gung University, Taoyuan, Taiwan.,Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Andrew Pekosz
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.,Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Environmental Health and Engineering, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
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26
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A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of the COVID-19 Pandemic. INFORMATION 2021. [DOI: 10.3390/info12020069] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.
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27
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Lepers C, Billiard S, Porte M, Méléard S, Tran VC. Inference with selection, varying population size, and evolving population structure: application of ABC to a forward-backward coalescent process with interactions. Heredity (Edinb) 2021; 126:335-350. [PMID: 33128035 PMCID: PMC8027416 DOI: 10.1038/s41437-020-00381-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 10/15/2020] [Indexed: 11/08/2022] Open
Abstract
Genetic data are often used to infer demographic history and changes or detect genes under selection. Inferential methods are commonly based on models making various strong assumptions: demography and population structures are supposed a priori known, the evolution of the genetic composition of a population does not affect demography nor population structure, and there is no selection nor interaction between and within genetic strains. In this paper, we present a stochastic birth-death model with competitive interactions and asexual reproduction. We develop an inferential procedure for ecological, demographic, and genetic parameters. We first show how genetic diversity and genealogies are related to birth and death rates, and to how individuals compete within and between strains. This leads us to propose an original model of phylogenies, with trait structure and interactions, that allows multiple merging. Second, we develop an Approximate Bayesian Computation framework to use our model for analyzing genetic data. We apply our procedure to simulated data from a toy model, and to real data by analyzing the genetic diversity of microsatellites on Y-chromosomes sampled from Central Asia human populations in order to test whether different social organizations show significantly different fertilities.
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Affiliation(s)
| | - Sylvain Billiard
- Univ. Lille, CNRS, UMR 819 8 -Evo-Eco-Paleo, F-59000, Lille, France.
| | - Matthieu Porte
- IGN, Institut National de l'Information Géographique et Forestière, F-94165, Saint-Mandé, France.
| | - Sylvie Méléard
- CMAP, CNRS, Ecole Polytechnique, Institut polytechnique de Paris, route de Saclay, 91128, Palaiseau Cedex, France.
| | - Viet Chi Tran
- LAMA, Univ Gustave Eiffel, Univ Paris Est Creteil, CNRS, F-77454, Marne-la-Vallée, France.
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28
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Huddleston J, Hadfield J, Sibley TR, Lee J, Fay K, Ilcisin M, Harkins E, Bedford T, Neher RA, Hodcroft EB. Augur: a bioinformatics toolkit for phylogenetic analyses of human pathogens. JOURNAL OF OPEN SOURCE SOFTWARE 2021; 6:2906. [PMID: 34189396 PMCID: PMC8237802 DOI: 10.21105/joss.02906] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The analysis of human pathogens requires a diverse collection of bioinformatics tools. These tools include standard genomic and phylogenetic software and custom software developed to handle the relatively numerous and short genomes of viruses and bacteria. Researchers increasingly depend on the outputs of these tools to infer transmission dynamics of human diseases and make actionable recommendations to public health officials (Black et al., 2020; Gardy et al., 2015). In order to enable real-time analyses of pathogen evolution, bioinformatics tools must scale rapidly with the number of samples and be flexible enough to adapt to a variety of questions and organisms. To meet these needs, we developed Augur, a bioinformatics toolkit designed for phylogenetic analyses of human pathogens.
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Affiliation(s)
- John Huddleston
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Thomas R Sibley
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kairsten Fay
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Misja Ilcisin
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Elias Harkins
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Trevor Bedford
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Richard A Neher
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Emma B Hodcroft
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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29
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Naeem A, Elbakkouri K, Alfaiz A, Hamed ME, Alsaran H, AlOtaiby S, Enani M, Alosaimi B. Antigenic drift of hemagglutinin and neuraminidase in seasonal H1N1 influenza viruses from Saudi Arabia in 2014 to 2015. J Med Virol 2020; 92:3016-3027. [PMID: 32159230 PMCID: PMC7228267 DOI: 10.1002/jmv.25759] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 03/04/2020] [Indexed: 01/19/2023]
Abstract
Antigenic drift of the hemagglutinin (HA) and neuraminidase (NA) proteins of the influenza virus cause a decrease in vaccine efficacy. Since the information about the evolution of these viruses in Saudi is deficient so we investigated the genetic diversity of circulating H1N1 viruses. Nasopharyngeal aspirates/swabs collected from 149 patients hospitalized with flu-like symptoms during 2014 and 2015 were analyzed. Viral RNA extraction was followed by a reverse transcription-polymerase chain reaction and genetic sequencing. We analyzed complete gene sequences of HA and NA from 80 positive isolates. Phylogenetic analysis of HA and NA genes of 80 isolates showed similar topologies and co-circulation of clades 6b. Genetic diversity was observed among circulating viruses belonging to clade 6B.1A. The amino acid residues in the HA epitope domain were under purifying selection. Amino acid changes at key antigenic sites, such as position S101N, S179N (antigenic site-Sa), I233T (antigenic site-Sb) in the head domain might have resulted in antigenic drift and emergence of variant viruses. For NA protein, 36% isolates showed the presence of amino acid changes such as V13I (n = 29), I314M (n = 29) and 12% had I34V (n = 10). However, H257Y mutation responsible for resistance to neuraminidase inhibitors was missing. The presence of amino acid changes at key antigenic sites and their topologies with structural mapping of residues under purifying selection highlights the importance of antigenic drift and warrants further characterization of recently circulating viruses in view of vaccine effectiveness. The co-circulation of several clades and the predominance of clade 6B.1 suggest multiple introductions in Saudi.
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MESH Headings
- Humans
- Neuraminidase/genetics
- Saudi Arabia/epidemiology
- Influenza, Human/virology
- Influenza, Human/epidemiology
- Phylogeny
- Influenza A Virus, H1N1 Subtype/genetics
- Influenza A Virus, H1N1 Subtype/immunology
- Influenza A Virus, H1N1 Subtype/enzymology
- Influenza A Virus, H1N1 Subtype/isolation & purification
- Hemagglutinin Glycoproteins, Influenza Virus/genetics
- Adult
- Male
- Female
- Young Adult
- Genetic Variation
- Middle Aged
- Adolescent
- Genetic Drift
- Child
- Child, Preschool
- Amino Acid Substitution
- Viral Proteins/genetics
- Nasopharynx/virology
- Antigens, Viral/genetics
- Antigens, Viral/immunology
- RNA, Viral/genetics
- Antigenic Variation
- Aged
- Sequence Analysis, DNA
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Affiliation(s)
- Asif Naeem
- Research CenterKing Fahad Medical CityRiyadhSaudi Arabia
| | | | - Ali Alfaiz
- Research CenterKing Fahad Medical CityRiyadhSaudi Arabia
| | | | - Hadel Alsaran
- Research CenterKing Fahad Medical CityRiyadhSaudi Arabia
| | | | - Mushira Enani
- Medical Specialties Department, Section of Infectious DiseasesKing Fahad Medical CityRiyadhSaudi Arabia
| | - Bandar Alosaimi
- Research CenterKing Fahad Medical CityRiyadhSaudi Arabia
- College of MedicineKing Fahad Medical CityRiyadhSaudi Arabia
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30
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Turakhia Y, De Maio N, Thornlow B, Gozashti L, Lanfear R, Walker CR, Hinrichs AS, Fernandes JD, Borges R, Slodkowicz G, Weilguny L, Haussler D, Goldman N, Corbett-Detig R. Stability of SARS-CoV-2 phylogenies. PLoS Genet 2020; 16:e1009175. [PMID: 33206635 PMCID: PMC7721162 DOI: 10.1371/journal.pgen.1009175] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 12/07/2020] [Accepted: 10/06/2020] [Indexed: 12/23/2022] Open
Abstract
The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral samples and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab-or protocol-specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein-coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation or recombination among viral lineages. We suggest how samples can be screened and problematic variants removed, and we plan to regularly inform the scientific community with our updated results as more SARS-CoV-2 genome sequences are shared (https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473 and https://virological.org/t/masking-strategies-for-sars-cov-2-alignments/480). We also develop tools for comparing and visualizing differences among very large phylogenies and we show that consistent clade- and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.
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Affiliation(s)
- Yatish Turakhia
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, United States of America
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Bryan Thornlow
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, United States of America
| | - Landen Gozashti
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States of America
| | - Robert Lanfear
- Department of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - Conor R. Walker
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Angie S. Hinrichs
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, United States of America
| | - Jason D. Fernandes
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Howard Hughes Medical Institute, University of California, Santa Cruz, CA, United States of America
| | - Rui Borges
- Institut für Populationsgenetik, Vetmeduni Vienna, Wien, Austria
| | - Greg Slodkowicz
- MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
| | - Lukas Weilguny
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - David Haussler
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Howard Hughes Medical Institute, University of California, Santa Cruz, CA, United States of America
| | - Nick Goldman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, United States of America
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, United States of America
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31
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Perofsky AC, Nelson MI. The challenges of vaccine strain selection. eLife 2020; 9:62955. [PMID: 33048046 PMCID: PMC7553772 DOI: 10.7554/elife.62955] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 10/09/2020] [Indexed: 12/23/2022] Open
Abstract
New measures of influenza virus fitness could improve vaccine strain selection through more accurate forecasts of the evolution of the virus.
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Affiliation(s)
- Amanda C Perofsky
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, United States
| | - Martha I Nelson
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, United States
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32
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Forghani M, Khachay M. Convolutional Neural Network Based Approach to in Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus. Viruses 2020; 12:E1019. [PMID: 32932748 PMCID: PMC7551508 DOI: 10.3390/v12091019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/06/2020] [Accepted: 09/08/2020] [Indexed: 12/18/2022] Open
Abstract
Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between two strains is calculated on the basis of HI assays. Usually, such distances are visualized by using some kind of antigenic cartography method. The known drawback of the HI assay is that it is rather time-consuming and expensive. In this paper, we propose a novel approach for antigenic distance approximation based on deep learning in the feature spaces induced by hemagglutinin protein sequences and Convolutional Neural Networks (CNNs). To apply a CNN to compare the protein sequences, we utilize the encoding based on the physical and chemical characteristics of amino acids. By varying (hyper)parameters of the CNN architecture design, we find the most robust network. Further, we provide insight into the relationship between approximated antigenic distance and antigenicity by evaluating the network on the HI assay database for the H1N1 subtype. The results indicate that the best-trained network gives a high-precision approximation for the ground-truth antigenic distances, and can be used as a good exploratory tool in practical tasks.
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33
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Gouma S, Kim K, Weirick ME, Gumina ME, Branche A, Topham DJ, Martin ET, Monto AS, Cobey S, Hensley SE. Middle-aged individuals may be in a perpetual state of H3N2 influenza virus susceptibility. Nat Commun 2020; 11:4566. [PMID: 32917903 PMCID: PMC7486384 DOI: 10.1038/s41467-020-18465-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 08/17/2020] [Indexed: 12/20/2022] Open
Abstract
Influenza virus exposures in childhood can establish long-lived memory B cell responses that can be recalled later in life. Here, we complete a large serological survey to elucidate the specificity of antibodies against contemporary H3N2 viruses in differently aged individuals who were likely primed with different H3N2 strains in childhood. We find that most humans who were first infected in childhood with H3N2 viral strains from the 1960s and 1970s possess non-neutralizing antibodies against contemporary 3c2.A H3N2 viruses. We find that 3c2.A H3N2 virus infections boost non-neutralizing H3N2 antibodies in middle-aged individuals, potentially leaving many of them in a perpetual state of 3c2.A H3N2 viral susceptibility.
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MESH Headings
- Adolescent
- Adult
- Aged
- Aged, 80 and over
- Antibodies, Neutralizing/immunology
- Antibodies, Viral/blood
- Antibodies, Viral/immunology
- Antigens, Viral/immunology
- Child
- Child, Preschool
- Disease Susceptibility
- Female
- Hemagglutinin Glycoproteins, Influenza Virus/immunology
- Humans
- Infant
- Influenza A Virus, H3N2 Subtype/immunology
- Influenza, Human/immunology
- Male
- Middle Aged
- Models, Biological
- Philadelphia
- Recombinant Proteins
- Seasons
- Young Adult
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Affiliation(s)
- Sigrid Gouma
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kangchon Kim
- Department of Ecology & Evolution, University of Chicago, Chicago, IL, 60637, USA
| | - Madison E Weirick
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Megan E Gumina
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Angela Branche
- Division of Infectious Diseases, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - David J Topham
- Department of Medicine and Department of Microbiology and Immunology, David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Emily T Martin
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Arnold S Monto
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sarah Cobey
- Department of Ecology & Evolution, University of Chicago, Chicago, IL, 60637, USA
| | - Scott E Hensley
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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34
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Zhang C, Dinh VU, Matsen FA. Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO. J Am Stat Assoc 2020; 116:858-873. [PMID: 34305211 DOI: 10.1080/01621459.2020.1778481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including sampled ancestors in which we sequence a genotype along with its direct descendants, and polytomies in which multiple descendants arise simultaneously. These features are apparent after identifying zero-length branches in the tree. However, current maximum-likelihood based approaches are not capable of revealing such zero-length branches. In this paper, we find these zero-length branches by introducing adaptive-LASSO-type regularization estimators for the branch lengths of phylogenetic trees, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics.
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Affiliation(s)
- Cheng Zhang
- School of Mathematical Sciences and Center for Statistical Science, Peking University
| | - V U Dinh
- Department of Mathematical Sciences, University of Delaware
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35
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Saad-Roy CM, McDermott AB, Grenfell BT. Dynamic Perspectives on the Search for a Universal Influenza Vaccine. J Infect Dis 2020; 219:S46-S56. [PMID: 30715467 DOI: 10.1093/infdis/jiz044] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
A universal influenza vaccine (UIV) could considerably alleviate the public health burden of both seasonal and pandemic influenza. Although significant progress has been achieved in clarifying basic immunology and virology relating to UIV, several important questions relating to the dynamics of infection, immunity, and pathogen evolution remain unsolved. In this study, we review these gaps, which span integrative levels, from cellular to global and timescales from molecular events to decades. We argue that they can be best addressed by a tight integration of empirical (laboratory, epidemiological) research and theory and suggest fruitful areas for this synthesis. In particular, quantifying natural and vaccinal limitations on viral transmission are central to this effort.
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Affiliation(s)
| | - Adrian B McDermott
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, New Jersey.,Woodrow Wilson School of Public and International Affairs, Princeton University, New Jersey.,Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland
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36
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Hayati M, Biller P, Colijn C. Predicting the short-term success of human influenza virus variants with machine learning. Proc Biol Sci 2020; 287:20200319. [PMID: 32259469 PMCID: PMC7209065 DOI: 10.1098/rspb.2020.0319] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 03/16/2020] [Indexed: 12/13/2022] Open
Abstract
Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines' benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier 'area under the curve' 0.75-0.9.
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Affiliation(s)
- Maryam Hayati
- Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, CanadaV5A 1S6
| | - Priscila Biller
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, CanadaV5A 1S6
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, CanadaV5A 1S6
- Department of Mathematics, Imperial College London, London SW7 2BU, UK
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37
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Szarvas J, Ahrenfeldt J, Cisneros JLB, Thomsen MCF, Aarestrup FM, Lund O. Large scale automated phylogenomic analysis of bacterial isolates and the Evergreen Online platform. Commun Biol 2020; 3:137. [PMID: 32198478 PMCID: PMC7083913 DOI: 10.1038/s42003-020-0869-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 03/03/2020] [Indexed: 11/09/2022] Open
Abstract
Public health authorities whole-genome sequence thousands of isolates each month for microbial diagnostics and surveillance of pathogenic bacteria. The computational methods have not kept up with the deluge of data and the need for real-time results. We have therefore created a bioinformatics pipeline for rapid subtyping and continuous phylogenomic analysis of bacterial samples, suited for large-scale surveillance. The data is divided into sets by mapping to reference genomes, then consensus sequences are generated. Nucleotide based genetic distance is calculated between the sequences in each set, and isolates are clustered together at 10 single-nucleotide polymorphisms. Phylogenetic trees are inferred from the non-redundant sequences and the clustered isolates are added back. The method is accurate at grouping outbreak strains together, while discriminating them from non-outbreak strains. The pipeline is applied in Evergreen Online, which processes publicly available sequencing data from foodborne bacterial pathogens on a daily basis, updating phylogenetic trees as needed.
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Affiliation(s)
- Judit Szarvas
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - Johanne Ahrenfeldt
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jose Luis Bellod Cisneros
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Frank M Aarestrup
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ole Lund
- Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
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38
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Early prediction of antigenic transitions for influenza A/H3N2. PLoS Comput Biol 2020; 16:e1007683. [PMID: 32069282 PMCID: PMC7048310 DOI: 10.1371/journal.pcbi.1007683] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 02/28/2020] [Accepted: 01/26/2020] [Indexed: 11/20/2022] Open
Abstract
Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection. The efficacy of annual seasonal influenza vaccines depends on selecting the strain that best matches circulating viruses. This selection takes place 9–12 months prior to the influenza season. To advise this decision, we used an influenza A/H3N2 phylodynamic simulation to explore how reliably and how far in advance can we identify strains that will dominate future influenza seasons? What data should we collect to accelerate and improve the accuracy of such forecasts? And importantly, what is the gap between the theoretical limit of prediction and prediction based on current influenza surveillance? Our results suggest that even with detailed virological information, the tight race between the antigenic turnover dynamics and the vaccine development timeline limits early detection of emerging viruses. Predictions based on current influenza surveillance do not achieve the theoretical limit and thus our results provide impetus for denser sampling and the development of rapid methods for estimating viral fitness.
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39
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Potter BI, Kondor R, Hadfield J, Huddleston J, Barnes J, Rowe T, Guo L, Xu X, Neher RA, Bedford T, Wentworth DE. Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season. Virus Evol 2019; 5:vez046. [PMID: 33282337 DOI: 10.1093/ve/vez046] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The 2017-2018 North American influenza season caused more hospitalizations and deaths than any year since the 2009 H1N1 pandemic. The majority of recorded influenza infections were caused by A(H3N2) viruses, with most of the virus's North American diversity falling into the A2 clade. Within A2, we observe a subclade which we call A2/re that rose to comprise almost 70 per cent of A(H3N2) viruses circulating in North America by early 2018. Unlike most fast-growing clades, however, A2/re contains no amino acid substitutions in the hemagglutinin (HA) segment. Moreover, hemagglutination inhibition assays did not suggest substantial antigenic differences between A2/re viruses and viruses sampled during the 2016-2017 season. Rather, we observe that the A2/re clade was the result of a reassortment event that occurred in late 2016 or early 2017 and involved the combination of the HA and PB1 segments of an A2 virus with neuraminidase (NA) and other segments a virus from the clade A1b. The success of this clade shows the need for antigenic analysis that targets NA in addition to HA. Our results illustrate the potential for non-HA drivers of viral success and necessitate the need for more thorough tracking of full viral genomes to better understand the dynamics of influenza epidemics.
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Affiliation(s)
- Barney I Potter
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
| | - Rebecca Kondor
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), 1600 Clifton Road, Atlanta, GA 30333, USA
| | - James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.,Molecular and Cellular Biology Program, University of Washington, 4109 E Stevens Way NE, Seattle, WA 98105, USA
| | - John Barnes
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Thomas Rowe
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Lizheng Guo
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Xiyan Xu
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Richard A Neher
- Biozentrum, University of Basel, Klingelbergstrasse 70, 4056 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Klingelbergstrasse 70, 4056 Basel, Switzerland
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
| | - David E Wentworth
- Virology Surveillance and Diagnosis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), 1600 Clifton Road, Atlanta, GA 30333, USA
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40
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Cowling BJ, Sullivan SG. The Value of Neuraminidase Inhibition Antibody Titers in Influenza Seroepidemiology. J Infect Dis 2019; 219:341-343. [PMID: 30011035 DOI: 10.1093/infdis/jiy446] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 07/12/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Benjamin J Cowling
- World Health Organization (WHO) Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, China
| | - Sheena G Sullivan
- WHO Collaborating Center for Reference and Research on Influenza, Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.,Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles.,Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Victoria, Australia
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41
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Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, Sagulenko P, Bedford T, Neher RA. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 2019; 34:4121-4123. [PMID: 29790939 PMCID: PMC6247931 DOI: 10.1093/bioinformatics/bty407] [Citation(s) in RCA: 1881] [Impact Index Per Article: 376.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 05/16/2018] [Indexed: 01/19/2023] Open
Abstract
Summary Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike. Availability and implementation All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.
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Affiliation(s)
- James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Colin Megill
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sidney M Bell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - John Huddleston
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Barney Potter
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Charlton Callender
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Pavel Sagulenko
- Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Richard A Neher
- Max Planck Institute for Developmental Biology, Tübingen, Germany.,Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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42
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Antigenic Site Variation in the Hemagglutinin of Pandemic Influenza A(H1N1)pdm09 Viruses between 2009-2017 in Ukraine. Pathogens 2019; 8:pathogens8040194. [PMID: 31635227 PMCID: PMC6963832 DOI: 10.3390/pathogens8040194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 09/27/2019] [Accepted: 10/01/2019] [Indexed: 01/16/2023] Open
Abstract
The hemagglutinin (HA) is a major influenza virus antigen, which, once recognized by antibodies and substitutions in HA genes, helps virus in escaping the human immune response. It is therefore critical to perform genetic and phylogenetic analysis of HA in circulating influenza viruses. We performed phylogenetic and genetic analysis of isolates from Ukraine, the vaccine strain and reference strains were used to phylogenetically identify trends in mutation locations and substitutions. Ukrainian isolates were collected between 2009–2017 and clustered in the influenza genetic groups 2, 6, 7, and 8. Genetic changes were observed in each of the antigenic sites: Sa – S162T, K163Q, K163I; Sb – S185T, A186T, S190G, S190R; Ca1 – S203T, R205K, E235V, E235D, S236P; Ca2 – P137H, H138R, A141T, D222G, D222N; Cb – A73S, S74R, S74N. In spite of detected mutations in antigenic sites, Ukrainian isolates retained similarity to the vaccine strain A/California/07/09 circulated during 2009–2017. However, WHO recommended a new vaccine strain A/Michigan/45/2015 for the Southern Hemisphere after the emergence of the new genetic groups 6B.1 and 6B.2. Our study demonstrated genetic variability of HA protein of A(H1N1)pdm09 viruses isolated in 2009–2017 in Ukraine. Influenza surveillance is very important for understanding epidemiological situations.
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43
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Hadfield J, Brito AF, Swetnam DM, Vogels CBF, Tokarz RE, Andersen KG, Smith RC, Bedford T, Grubaugh ND. Twenty years of West Nile virus spread and evolution in the Americas visualized by Nextstrain. PLoS Pathog 2019; 15:e1008042. [PMID: 31671157 PMCID: PMC6822705 DOI: 10.1371/journal.ppat.1008042] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It has been 20 years since West Nile virus first emerged in the Americas, and since then, little progress has been made to control outbreaks caused by this virus. After its first detection in New York in 1999, West Nile virus quickly spread across the continent, causing an epidemic of human disease and massive bird die-offs. Now the virus has become endemic to the United States, where an estimated 7 million human infections have occurred, making it the leading mosquito-borne virus infection and the most common cause of viral encephalitis in the country. To bring new attention to one of the most important mosquito-borne viruses in the Americas, we provide an interactive review using Nextstrain: a visualization tool for real-time tracking of pathogen evolution (nextstrain.org/WNV/NA). Nextstrain utilizes a growing database of more than 2,000 West Nile virus genomes and harnesses the power of phylogenetics for students, educators, public health workers, and researchers to visualize key aspects of virus spread and evolution. Using Nextstrain, we use virus genomics to investigate the emergence of West Nile virus in the U S, followed by its rapid spread, evolution in a new environment, establishment of endemic transmission, and subsequent international spread. For each figure, we include a link to Nextstrain to allow the readers to directly interact with and explore the underlying data in new ways. We also provide a brief online narrative that parallels this review to further explain the data and highlight key epidemiological and evolutionary features (nextstrain.org/narratives/twenty-years-of-WNV). Mirroring the dynamic nature of outbreaks, the Nextstrain links provided within this paper are constantly updated as new West Nile virus genomes are shared publicly, helping to stay current with the research. Overall, our review showcases how genomics can track West Nile virus spread and evolution, as well as potentially uncover novel targeted control measures to help alleviate its public health burden.
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Affiliation(s)
- James Hadfield
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Anderson F. Brito
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Daniele M. Swetnam
- Department of Pathology, Microbiology and Immunology, University of California, Davis, Davis, California, United States of America
| | - Chantal B. F. Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Ryan E. Tokarz
- Department of Entomology, Iowa State University, Ames, Iowa, United States of America
| | - Kristian G. Andersen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, California, United States of America
- Scripps Research Translational Institute, La Jolla, California, United States of America
| | - Ryan C. Smith
- Department of Entomology, Iowa State University, Ames, Iowa, United States of America
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Nathan D. Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
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44
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Yan L, Neher RA, Shraiman BI. Phylodynamic theory of persistence, extinction and speciation of rapidly adapting pathogens. eLife 2019; 8:e44205. [PMID: 31532393 PMCID: PMC6809594 DOI: 10.7554/elife.44205] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 09/14/2019] [Indexed: 11/13/2022] Open
Abstract
Rapidly evolving pathogens like influenza viruses can persist by changing their antigenic properties fast enough to evade the adaptive immunity, yet they rarely split into diverging lineages. By mapping the multi-strain Susceptible-Infected-Recovered model onto the traveling wave model of adapting populations, we demonstrate that persistence of a rapidly evolving, Red-Queen-like state of the pathogen population requires long-ranged cross-immunity and sufficiently large population sizes. This state is unstable and the population goes extinct or 'speciates' into two pathogen strains with antigenic divergence beyond the range of cross-inhibition. However, in a certain range of evolutionary parameters, a single cross-inhibiting population can exist for times long compared to the time to the most recent common ancestor ([Formula: see text]) and gives rise to phylogenetic patterns typical of influenza virus. We demonstrate that the rate of speciation is related to fluctuations of [Formula: see text] and construct a 'phase diagram' identifying different phylodynamic regimes as a function of evolutionary parameters.
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Affiliation(s)
- Le Yan
- Kavli Institute for Theoretical PhysicsUniversity of California, Santa BarbaraSanta BarbaraUnited States
| | - Richard A Neher
- BiozentrumUniversity of Basel, Swiss Institute for BioinformaticsBaselSwitzerland
| | - Boris I Shraiman
- Kavli Institute for Theoretical PhysicsUniversity of California, Santa BarbaraSanta BarbaraUnited States
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45
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Lee JM, Eguia R, Zost SJ, Choudhary S, Wilson PC, Bedford T, Stevens-Ayers T, Boeckh M, Hurt AC, Lakdawala SS, Hensley SE, Bloom JD. Mapping person-to-person variation in viral mutations that escape polyclonal serum targeting influenza hemagglutinin. eLife 2019; 8:e49324. [PMID: 31452511 PMCID: PMC6711711 DOI: 10.7554/elife.49324] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/27/2019] [Indexed: 12/11/2022] Open
Abstract
A longstanding question is how influenza virus evolves to escape human immunity, which is polyclonal and can target many distinct epitopes. Here, we map how all amino-acid mutations to influenza's major surface protein affect viral neutralization by polyclonal human sera. The serum of some individuals is so focused that it selects single mutations that reduce viral neutralization by over an order of magnitude. However, different viral mutations escape the sera of different individuals. This individual-to-individual variation in viral escape mutations is not present among ferrets that have been infected just once with a defined viral strain. Our results show how different single mutations help influenza virus escape the immunity of different members of the human population, a phenomenon that could shape viral evolution and disease susceptibility.
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Affiliation(s)
- Juhye M Lee
- Basic Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome SciencesUniversity of WashingtonSeattleUnited States
| | - Rachel Eguia
- Basic Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Seth J Zost
- Department of MicrobiologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Saket Choudhary
- Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesUnited States
| | - Patrick C Wilson
- Department of MedicineSection of Rheumatology, University of ChicagoChicagoUnited States
| | - Trevor Bedford
- Vaccine and Infectious Disease DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Terry Stevens-Ayers
- Vaccine and Infectious Disease DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Michael Boeckh
- Vaccine and Infectious Disease DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
| | - Aeron C Hurt
- WHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Seema S Lakdawala
- Department of Microbiology and Molecular GeneticsSchool of Medicine, University of PittsburghPittsburghUnited States
| | - Scott E Hensley
- Department of MicrobiologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Jesse D Bloom
- Basic Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUnited States
- Department of Genome SciencesUniversity of WashingtonSeattleUnited States
- Howard Hughes Medical InstituteSeattleUnited States
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46
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Theys K, Lemey P, Vandamme AM, Baele G. Advances in Visualization Tools for Phylogenomic and Phylodynamic Studies of Viral Diseases. Front Public Health 2019; 7:208. [PMID: 31428595 PMCID: PMC6688121 DOI: 10.3389/fpubh.2019.00208] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Accepted: 07/12/2019] [Indexed: 01/28/2023] Open
Abstract
Genomic and epidemiological monitoring have become an integral part of our response to emerging and ongoing epidemics of viral infectious diseases. Advances in high-throughput sequencing, including portable genomic sequencing at reduced costs and turnaround time, are paralleled by continuing developments in methodology to infer evolutionary histories (dynamics/patterns) and to identify factors driving viral spread in space and time. The traditionally static nature of visualizing phylogenetic trees that represent these evolutionary relationships/processes has also evolved, albeit perhaps at a slower rate. Advanced visualization tools with increased resolution assist in drawing conclusions from phylogenetic estimates and may even have potential to better inform public health and treatment decisions, but the design (and choice of what analyses are shown) is hindered by the complexity of information embedded within current phylogenetic models and the integration of available meta-data. In this review, we discuss visualization challenges for the interpretation and exploration of reconstructed histories of viral epidemics that arose from increasing volumes of sequence data and the wealth of additional data layers that can be integrated. We focus on solutions that address joint temporal and spatial visualization but also consider what the future may bring in terms of visualization and how this may become of value for the coming era of real-time digital pathogen surveillance, where actionable results and adequate intervention strategies need to be obtained within days.
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Affiliation(s)
- Kristof Theys
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Anne-Mieke Vandamme
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
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47
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Ding W, Baumdicker F, Neher RA. panX: pan-genome analysis and exploration. Nucleic Acids Res 2019; 46:e5. [PMID: 29077859 PMCID: PMC5758898 DOI: 10.1093/nar/gkx977] [Citation(s) in RCA: 143] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 10/10/2017] [Indexed: 11/24/2022] Open
Abstract
Horizontal transfer, gene loss, and duplication result in dynamic bacterial genomes shaped by a complex mixture of different modes of evolution. Closely related strains can differ in the presence or absence of many genes, and the total number of distinct genes found in a set of related isolates—the pan-genome—is often many times larger than the genome of individual isolates. We have developed a pipeline that efficiently identifies orthologous gene clusters in the pan-genome. This pipeline is coupled to a powerful yet easy-to-use web-based visualization for interactive exploration of the pan-genome. The visualization consists of connected components that allow rapid filtering and searching of genes and inspection of their evolutionary history. For each gene cluster, panX displays an alignment, a phylogenetic tree, maps mutations within that cluster to the branches of the tree and infers gain and loss of genes on the core-genome phylogeny. PanX is available at pangenome.de. Custom pan-genomes can be visualized either using a web server or by serving panX locally as a browser-based application.
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Affiliation(s)
- Wei Ding
- Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Franz Baumdicker
- Mathematisches Institut, Albert-Ludwigs University of Freiburg, 79104 Freiburg, Germany
| | - Richard A Neher
- Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany.,Biozentrum and SIB Swiss Institute of Bioinformatics, University of Basel, 4056 Basel, Switzerland
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48
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Janies D. Phylogenetic Concepts and Tools Applied to Epidemiologic Investigations of Infectious Diseases. Microbiol Spectr 2019; 7:10.1128/microbiolspec.ame-0006-2018. [PMID: 31325287 PMCID: PMC10956736 DOI: 10.1128/microbiolspec.ame-0006-2018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Indexed: 01/13/2023] Open
Abstract
In this review, which is a part of the Microbiology Spectrum Curated Collection: Advances in Molecular Epidemiology of Infectious Diseases, I present an overview of the principles used to classify organisms in the field of phylogenetics, highlight the methods used to infer the interrelationships of organisms, and summarize how these concepts are applied to molecular epidemiologic analyses. I present steps in analyses that come downstream of the assembly of a set of genomes or genes and the production of a multiple-sequence alignment or other matrices of putative orthologs for comparison. I focus on the history of the problem of phylogenetic reconstruction and debates within the field about the most appropriate methods. I illustrate methods that bridge the gap between molecular epidemiology and traditional epidemiology, including phylogenetic character evolution and geographic visualization. Finally, I provide practical advice on how to conduct an example analysis in the appendix. *This article is part of a curated collection.
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Affiliation(s)
- Daniel Janies
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223
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Redmond SN, MacInnis BM, Bopp S, Bei AK, Ndiaye D, Hartl DL, Wirth DF, Volkman SK, Neafsey DE. De Novo Mutations Resolve Disease Transmission Pathways in Clonal Malaria. Mol Biol Evol 2019; 35:1678-1689. [PMID: 29722884 PMCID: PMC5995194 DOI: 10.1093/molbev/msy059] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Detecting de novo mutations in viral and bacterial pathogens enables researchers to reconstruct detailed networks of disease transmission and is a key technique in genomic epidemiology. However, these techniques have not yet been applied to the malaria parasite, Plasmodium falciparum, in which a larger genome, slower generation times, and a complex life cycle make them difficult to implement. Here, we demonstrate the viability of de novo mutation studies in P. falciparum for the first time. Using a combination of sequencing, library preparation, and genotyping methods that have been optimized for accuracy in low-complexity genomic regions, we have detected de novo mutations that distinguish nominally identical parasites from clonal lineages. Despite its slower evolutionary rate compared with bacterial or viral species, de novo mutation can be detected in P. falciparum across timescales of just 1–2 years and evolutionary rates in low-complexity regions of the genome can be up to twice that detected in the rest of the genome. The increased mutation rate allows the identification of separate clade expansions that cannot be found using previous genomic epidemiology approaches and could be a crucial tool for mapping residual transmission patterns in disease elimination campaigns and reintroduction scenarios.
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Affiliation(s)
- Seth N Redmond
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Bronwyn M MacInnis
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Selina Bopp
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Amy K Bei
- Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Parasitology, Faculty of Medicine and Pharmacy, Cheikh Anta Diop University, Dakar, Senegal
| | - Daouda Ndiaye
- Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Parasitology, Faculty of Medicine and Pharmacy, Cheikh Anta Diop University, Dakar, Senegal
| | - Daniel L Hartl
- Broad Institute of MIT and Harvard, Cambridge, MA.,Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
| | - Dyann F Wirth
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Sarah K Volkman
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Nursing, School of Nursing and Health Sciences, Simmons College, Boston, MA, 02115
| | - Daniel E Neafsey
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
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50
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Levine MZ, Martin ET, Petrie JG, Lauring AS, Holiday C, Jefferson S, Fitzsimmons WJ, Johnson E, Ferdinands JM, Monto AS. Antibodies Against Egg- and Cell-Grown Influenza A(H3N2) Viruses in Adults Hospitalized During the 2017-2018 Influenza Season. J Infect Dis 2019; 219:1904-1912. [PMID: 30721982 PMCID: PMC6534188 DOI: 10.1093/infdis/jiz049] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 01/29/2019] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Influenza vaccine effectiveness was low in 2017-2018, yet circulating influenza A(H3N2) viruses were antigenically similar to cell-grown vaccine strains. Notably, most influenza vaccines are egg propagated. METHODS Serum specimens were collected shortly after illness onset from 15 influenza A(H3N2) virus-infected cases and 15 uninfected hospitalized adults. Geometric mean titers against egg- and cell-grown influenza A/Hong Kong/4801/2014(H3N2) virus vaccine strains and representative circulating viruses (including A/Washington/16/2017) were determined by a microneutralization (MN) assay. Independent effects of strain-specific titers on susceptibility were estimated by logistic regression. RESULTS MN titers against egg-grown influenza A/Hong Kong virus were significantly higher among vaccinated individuals (173 vs 41; P = 0.01). In unadjusted models, a 2-fold increase in titers against egg-grown influenza A/Hong Kong virus was not significantly protective (29% reduction; P = .09), but a similar increase in the cell-grown influenza A/Washington virus antibody titer (3C.2a2) was protective (60% reduction; P = .02). Higher egg-grown influenza A/Hong Kong virus titers were not significantly associated with infection, when adjusted for antibody titers against influenza A/Washington virus (15% reduction; P = .61). A 54% reduction in the odds of infection was observed with a 2-fold increase in titer against influenza A/Washington virus (P = not significant), adjusted for the titer against egg-grown influenza A/Hong Kong virus titer. CONCLUSION Individuals vaccinated in 2017-2018 had high antibody titers against the egg-adapted vaccine strain and lower titers against circulating viruses. Titers against circulating but not egg-adapted strains were correlated with protection.
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Affiliation(s)
- Min Z Levine
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Emily T Martin
- Department of Epidemiology, University of Michigan School of Public Health
| | - Joshua G Petrie
- Department of Epidemiology, University of Michigan School of Public Health
| | - Adam S Lauring
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Crystal Holiday
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Stacie Jefferson
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - William J Fitzsimmons
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Emileigh Johnson
- Department of Epidemiology, University of Michigan School of Public Health
| | - Jill M Ferdinands
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Arnold S Monto
- Department of Epidemiology, University of Michigan School of Public Health
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