1
|
Zhai K, Dong J, Zeng J, Cheng P, Wu X, Han W, Chen Y, Qiu Z, Zhou Y, Pu J, Jiang T, Du X. Global antigenic landscape and vaccine recommendation strategy for low pathogenic avian influenza A (H9N2) viruses. J Infect 2024; 89:106199. [PMID: 38901571 DOI: 10.1016/j.jinf.2024.106199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/09/2024] [Accepted: 06/11/2024] [Indexed: 06/22/2024]
Abstract
The sustained circulation of H9N2 avian influenza viruses (AIVs) poses a significant threat for contributing to a new pandemic. Given the temporal and spatial uncertainty in the antigenicity of H9N2 AIVs, the immune protection efficiency of vaccines remains challenging. By developing an antigenicity prediction method for H9N2 AIVs, named PREDAC-H9, the global antigenic landscape of H9N2 AIVs was mapped. PREDAC-H9 utilizes the XGBoost model with 14 well-designed features. The XGBoost model was built and evaluated to predict the antigenic relationship between any two viruses with high values of 81.1 %, 81.4 %, 81.3 %, 81.1 %, and 89.4 % in accuracy, precision, recall, F1 value, and area under curve (AUC), respectively. Then the antigenic correlation network (ACnet) was constructed based on the predicted antigenic relationship for H9N2 AIVs from 1966 to 2022, and ten major antigenic clusters were identified. Of these, four novel clusters were generated in China in the past decade, demonstrating the unique complex situation there. To help tackle this situation, we applied PREDAC-H9 to calculate the cluster-transition determining sites and screen out virus strains with the high cross-protective spectrum, thus providing an in silico reference for vaccine recommendation. The proposed model will reduce the clinical monitoring workload and provide a useful tool for surveillance and control of H9N2 AIVs.
Collapse
Affiliation(s)
- Ke Zhai
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Jinze Dong
- National Key Laboratory of Veterinary Public Health and Safety, Key Laboratory for Prevention and Control of Avian Influenza and Other Major Poultry Diseases, Ministry of Agriculture and Rural Affairs, College of Veterinary Medicine, China Agricultural University, Beijing 100193, PR China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Peiwen Cheng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Xinsheng Wu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Wenjie Han
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Yilin Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Zekai Qiu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Department of Molecular and Radiooncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg 69047, Germany
| | - Yong Zhou
- National Key Laboratory of Veterinary Public Health and Safety, Key Laboratory for Prevention and Control of Avian Influenza and Other Major Poultry Diseases, Ministry of Agriculture and Rural Affairs, College of Veterinary Medicine, China Agricultural University, Beijing 100193, PR China
| | - Juan Pu
- National Key Laboratory of Veterinary Public Health and Safety, Key Laboratory for Prevention and Control of Avian Influenza and Other Major Poultry Diseases, Ministry of Agriculture and Rural Affairs, College of Veterinary Medicine, China Agricultural University, Beijing 100193, PR China.
| | - Taijiao Jiang
- Guangzhou National Laboratory, Guangzhou 510005, PR China; State Key Laboratory of Respiratory Disease, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, PR China; Suzhou Institute of Systems Medicine, Suzhou 215123, PR China.
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosecurity, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou 510030, PR China.
| |
Collapse
|
2
|
Nielsen BF, Berrig C, Grenfell BT, Andreasen V. One hundred years of influenza A evolution. Theor Popul Biol 2024; 159:25-34. [PMID: 39094981 DOI: 10.1016/j.tpb.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 07/05/2024] [Accepted: 07/30/2024] [Indexed: 08/04/2024]
Abstract
Leveraging the simplicity of nucleotide mismatch distributions, we provide an intuitive window into the evolution of the human influenza A 'nonstructural' (NS) gene segment. In an analysis suggested by the eminent Danish biologist Freddy B. Christiansen, we illustrate the existence of a continuous genetic "backbone" of influenza A NS sequences, steadily increasing in nucleotide distance to the 1918 root over more than a century. The 2009 influenza A/H1N1 pandemic represents a clear departure from this enduring genetic backbone. Utilizing nucleotide distance maps and phylogenetic analyses, we illustrate remaining uncertainties regarding the origin of the 2009 pandemic, highlighting the complexity of influenza evolution. The NS segment is interesting precisely because it experiences less pervasive positive selection, and departs less strongly from neutral evolution than e.g. the HA antigen. Consequently, sudden deviations from neutral diversification can indicate changes in other genes via the hitchhiking effect. Our approach employs two measures based on nucleotide mismatch counts to analyze the evolutionary dynamics of the NS gene segment. The rooted Hamming map of distances between a reference sequence and all other sequences over time, and the unrooted temporal Hamming distribution which captures the distribution of genotypic distances between simultaneously circulating viruses, thereby revealing patterns of nucleotide diversity and epi-evolutionary dynamics.
Collapse
Affiliation(s)
- Bjarke Frost Nielsen
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, United States of America; Department of Science and Environment, Roskilde University, Roskilde, Denmark; Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.
| | - Christian Berrig
- Department of Science and Environment, Roskilde University, Roskilde, Denmark.
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America.
| | - Viggo Andreasen
- Department of Science and Environment, Roskilde University, Roskilde, Denmark.
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Markin A, Wagle S, Grover S, Vincent Baker AL, Eulenstein O, Anderson TK. PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny. Syst Biol 2023; 72:1052-1063. [PMID: 37208300 PMCID: PMC10627562 DOI: 10.1093/sysbio/syad028] [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: 09/13/2022] [Revised: 04/26/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023] Open
Abstract
The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires objective subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than existing approaches. To demonstrate its utility, we applied parnas to 1) quantify SARS-CoV-2 genetic diversity over time, 2) select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data, and 3) identify gaps in H3N2 human influenza A virus vaccine coverage. We suggest that our method, through the objective selection of representatives in a phylogeny, provides criteria for quantifying genetic diversity that has application in the the rational design of multivalent vaccines and genomic epidemiology. PARNAS is available at https://github.com/flu-crew/parnas.
Collapse
Affiliation(s)
- Alexey Markin
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
| | - Sanket Wagle
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
| | - Siddhant Grover
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
| | - Amy L Vincent Baker
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
| | - Oliver Eulenstein
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
| | - Tavis K Anderson
- Virus and Prion Research Unit, National Animal Disease Center, USDA-ARS, Ames, IA, 50010, USA
| |
Collapse
|
5
|
Nielsen BF, Saad-Roy CM, Li Y, Sneppen K, Simonsen L, Viboud C, Levin SA, Grenfell BT. Host heterogeneity and epistasis explain punctuated evolution of SARS-CoV-2. PLoS Comput Biol 2023; 19:e1010896. [PMID: 36791146 PMCID: PMC9974118 DOI: 10.1371/journal.pcbi.1010896] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 02/28/2023] [Accepted: 01/25/2023] [Indexed: 02/16/2023] Open
Abstract
Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.
Collapse
Affiliation(s)
- Bjarke Frost Nielsen
- Department of Science and Environment, Roskilde University, Roskilde, Denmark
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Chadi M. Saad-Roy
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Miller Institute for Basic Research in Science, University of California, Berkeley, California, United States of America
| | - Yimei Li
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Lone Simonsen
- Department of Science and Environment, Roskilde University, Roskilde, Denmark
| | - Cécile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
| |
Collapse
|
6
|
Khan T, Raza S. Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review. Curr Top Med Chem 2023; 23:1640-1663. [PMID: 36725827 DOI: 10.2174/1568026623666230201144522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/14/2022] [Accepted: 12/19/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body. METHODS This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review. RESULTS Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes. CONCLUSION Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.
Collapse
Affiliation(s)
- Tahmeena Khan
- Department of Chemistry, Integral University, Lucknow, 226026, U.P., India
| | - Saman Raza
- Department of Chemistry, Isabella Thoburn College, Lucknow, 226007, U.P., India
| |
Collapse
|
7
|
Boyle L, Hletko S, Huang J, Lee J, Pallod G, Tung HR, Durrett R. Selective sweeps in SARS-CoV-2 variant competition. Proc Natl Acad Sci U S A 2022; 119:e2213879119. [PMID: 36383746 PMCID: PMC9704709 DOI: 10.1073/pnas.2213879119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/15/2022] [Indexed: 07/28/2023] Open
Abstract
The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible-Infected-Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation, which models selective sweeps. Fitting the logistic to data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that this correctly predicts the rapid turnover from one dominant variant to another. In addition, our fitting gives sensible estimates of the increase in infectivity. These arguments are applicable to any epidemic modeled by SIR equations.
Collapse
Affiliation(s)
- Laura Boyle
- Department of Mathematics, Duke University, Durham, NC 27708-0320
| | - Sofia Hletko
- Department of Mathematics, Duke University, Durham, NC 27708-0320
| | - Jenny Huang
- Department of Mathematics, Duke University, Durham, NC 27708-0320
| | - June Lee
- Department of Mathematics, Duke University, Durham, NC 27708-0320
| | - Gaurav Pallod
- Department of Mathematics, Duke University, Durham, NC 27708-0320
| | - Hwai-Ray Tung
- Department of Mathematics, Duke University, Durham, NC 27708-0320
| | - Richard Durrett
- Department of Mathematics, Duke University, Durham, NC 27708-0320
| |
Collapse
|
8
|
Nielsen BF, Li Y, Sneppen K, Simonsen L, Viboud C, Levin SA, Grenfell BT. Immune Heterogeneity and Epistasis Explain Punctuated Evolution of SARS-CoV-2. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.07.27.22278129. [PMID: 35982659 PMCID: PMC9387145 DOI: 10.1101/2022.07.27.22278129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.
Collapse
Affiliation(s)
- Bjarke Frost Nielsen
- PandemiX Center, Roskilde University
- Niels Bohr Institute, University of Copenhagen
| | - Yimei Li
- Department of Ecology & Evolutionary Biology, Princeton University
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen
| | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health
| | - Simon A. Levin
- Department of Ecology & Evolutionary Biology, Princeton University
| | | |
Collapse
|
9
|
Metsky HC, Welch NL, Pillai PP, Haradhvala NJ, Rumker L, Mantena S, Zhang YB, Yang DK, Ackerman CM, Weller J, Blainey PC, Myhrvold C, Mitzenmacher M, Sabeti PC. Designing sensitive viral diagnostics with machine learning. Nat Biotechnol 2022; 40:1123-1131. [PMID: 35241837 PMCID: PMC9287178 DOI: 10.1038/s41587-022-01213-5] [Citation(s) in RCA: 24] [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: 04/25/2021] [Accepted: 01/07/2022] [Indexed: 12/20/2022]
Abstract
Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic-target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus's genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT's designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus's variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.
Collapse
Affiliation(s)
- Hayden C Metsky
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.
| | - Nicole L Welch
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Virology Program, Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | | | - Nicholas J Haradhvala
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Biophysics Program, Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Bioinformatics and Integrative Genomics Program, Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sreekar Mantena
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Yibin B Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - David K Yang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cheri M Ackerman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | | | - Paul C Blainey
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
| | - Cameron Myhrvold
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Michael Mitzenmacher
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Pardis C Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| |
Collapse
|
10
|
Suleman S, Farooqui A, Sharma P, Malhotra N, Yadav N, Narang J, Hasnain MS, Nayak AK. Borderline microscopic organism and lockdown impacted across the borders-global shakers. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:8091-8108. [PMID: 34841487 PMCID: PMC8627845 DOI: 10.1007/s11356-021-17641-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 11/16/2021] [Indexed: 06/13/2023]
Abstract
Viruses are the potential cause of several diseases including novel corona virus-19, flu, small pox, chicken pox, acquired immunodeficiency syndrome, severe acute respiratory syndrome etc. The objectives of this review article are to summarize the reasons behind the epidemics caused by several emerging viruses and bacteria, how to control the infection and preventive strategies. We have explained the causes of epidemics along with their preventive measures, the impact of lockdown on the health of people and the economy of a country. Several reports have revealed the transmission of infection during epidemic from the contact of an infected person to the public that can be prevented by implementing the lockdown by the government of a country. Though lockdown has been considered as one of the significant parameters to control the diseases, however, it has some negative consequences on the health of people as they can be more prone to other ailments like obesity, diabetes, cardiac problems etc. and drastic decline in the economy of a country. Therefore, the transmission of diseases can be prevented by warning the people about the severity of diseases, avoiding their public transportation, keeping themselves isolated, strictly following the guidelines of lockdown and encouraging regular exercise.
Collapse
Affiliation(s)
- Shariq Suleman
- Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Asim Farooqui
- Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Pradakshina Sharma
- Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Nitesh Malhotra
- Department of Physiotherapy, Faculty of Allied Health Sciences, Manav Rachna International Institute of Research & Studies, Faridabad, India
| | - Neelam Yadav
- Department of Biotechnology, Deenbandhu Chhotu Ram University of Science and Technology, Sonepat (Haryana), Murthal, 131039, India
- Centre for Biotechnology, Maharshi Dayanand University, Rohtak (Haryana), 124001, India
| | - Jagriti Narang
- Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, Hamdard Nagar, New Delhi, 110062, India
| | - Md Saquib Hasnain
- Department of Pharmacy, Palamau Institute of Pharmacy, Chianki, Daltonganj, Jharkhand, 822102, India.
| | - Amit Kumar Nayak
- Department of Pharmaceutics, Seemanta Institute of Pharmaceutical Sciences, Jharpokharia, Mayurbhanj, Odisha, 757086, India
| |
Collapse
|
11
|
A statistical analysis of antigenic similarity among influenza A (H3N2) viruses. Heliyon 2021; 7:e08384. [PMID: 34825090 PMCID: PMC8605065 DOI: 10.1016/j.heliyon.2021.e08384] [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: 03/29/2021] [Revised: 04/21/2021] [Accepted: 11/10/2021] [Indexed: 11/20/2022] Open
Abstract
An accurate assessment of antigenic similarity between influenza viruses is important for vaccine strain recommendations and influenza surveillance. Due to the mechanisms that result in frequent changes in the antigenicities of strains, it is desirable to obtain an antigenic similarity measure that accounts for specific changes in strains that are of epidemiological importance in influenza. Empirically grounded statistical models best achieve this. In this study, an interpretable machine-learning model was developed using distinguishing features of antigenic variants to analyze antigenic similarity. The features comprised of cluster information, amino acid sequences located in known antigenic and receptor-binding sites of influenza A (H3N2). In order to assess validity of parameters, accuracy and relevance of model to vaccine effectiveness, the model was applied to influenza A (H3N2) viruses due to their abundant genetic data and epidemiological relevance to influenza surveillance. An application of the model revealed that all model parameters were statistically significant to determining antigenic similarity between strains. Furthermore, upon evaluating the model for predicting antigenic similarity between strains, it achieved 95% area under Receiver Operating Characteristic curve (AUC), 94% accuracy, 76% precision, 97% specificity, 68% sensitivity and a diagnostic odds ratio (DOR) of 83.19. Above all, the model was found to be strongly related to influenza vaccine effectiveness to indicate the correlation between vaccine effectiveness and antigenic similarity between vaccine and circulating strains in an epidemic. The study predicts probabilities of antigenic similarity and estimates changes in strains that lead to antigenic variants. A successful application of the methods presented in this study would complement the global efforts in influenza surveillance.
Collapse
|
12
|
Wang MH, Lou J, Cao L, Zhao S, Chan RW, Chan PK, Chan MCW, Chong MK, Wu WK, Wei Y, Zhang H, Zee BC, Yeoh EK. Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin. J Infect 2021; 83:671-677. [PMID: 34627840 DOI: 10.1016/j.jinf.2021.09.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 06/10/2021] [Accepted: 09/30/2021] [Indexed: 10/20/2022]
Abstract
The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in Southeast Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.
Collapse
Affiliation(s)
- Maggie Haitian Wang
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; CUHK Shenzhen Research Institute, Shenzhen, China.
| | - Jingzhi Lou
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; CUHK Shenzhen Research Institute, Shenzhen, China
| | - Lirong Cao
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; CUHK Shenzhen Research Institute, Shenzhen, China
| | - Shi Zhao
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; CUHK Shenzhen Research Institute, Shenzhen, China
| | - Renee Wy Chan
- CUHK-UMCU Joint Research Laboratory of Respiratory Virus & Immunobiology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; Department of Paediatrics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
| | - Paul Ks Chan
- Department of Microbiology, Stanley Ho Center for Emerging Infectious Diseases, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
| | - Martin Chi-Wai Chan
- Department of Microbiology, Stanley Ho Center for Emerging Infectious Diseases, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
| | - Marc Kc Chong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; CUHK Shenzhen Research Institute, Shenzhen, China
| | - William Kk Wu
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
| | - Yuchen Wei
- Department of Microbiology, Stanley Ho Center for Emerging Infectious Diseases, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
| | - Haoyang Zhang
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
| | - Benny Cy Zee
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China; CUHK Shenzhen Research Institute, Shenzhen, China
| | - Eng-Kiong Yeoh
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China.
| |
Collapse
|
13
|
Anomalous influenza seasonality in the United States and the emergence of novel influenza B viruses. Proc Natl Acad Sci U S A 2021; 118:2012327118. [PMID: 33495348 DOI: 10.1073/pnas.2012327118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The 2019/2020 influenza season in the United States began earlier than any season since the 2009 H1N1 pandemic, with an increase in influenza-like illnesses observed as early as August. Also noteworthy was the numerical domination of influenza B cases early in this influenza season, in contrast to their typically later peak in the past. Here, we dissect the 2019/2020 influenza season not only with regard to its unusually early activity, but also with regard to the relative dynamics of type A and type B cases. We propose that the recent expansion of a novel influenza B/Victoria clade may be associated with this shift in the composition and kinetics of the influenza season in the United States. We use epidemiological transmission models to explore whether changes in the effective reproduction number or short-term cross-immunity between these viruses can explain the dynamics of influenza A and B seasonality. We find support for an increase in the effective reproduction number of influenza B, rather than support for cross-type immunity-driven dynamics. Our findings have clear implications for optimal vaccination strategies.
Collapse
|
14
|
Yuan Y, He J, Gong L, Li W, Jiang L, Liu J, Chen Q, Yu J, Hou S, Shi Y, Lu S, Zhang Z, Ge Y, Sa N, He L, Wu J, Sun Y, Liu Z. Molecular epidemiology of SARS-CoV-2 clusters caused by asymptomatic cases in Anhui Province, China. BMC Infect Dis 2020; 20:930. [PMID: 33287717 PMCID: PMC7719853 DOI: 10.1186/s12879-020-05612-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 11/11/2020] [Indexed: 01/25/2023] Open
Abstract
Background COVID-19 is a newly emerging disease caused by a novel coronavirus (SARS-CoV-2), which spread globally in early 2020. Asymptomatic carriers of the virus contribute to the propagation of this disease, and the existence of asymptomatic infection has caused widespread fear and concern in the control of this pandemic. Methods In this study, we investigated the origin and transmission route of SARS-CoV-2 in Anhui’s two clusters, analyzed the role and infectiousness of asymptomatic patients in disease transmission, and characterized the complete spike gene sequences in the Anhui strains. Results We conducted an epidemiological investigation of two clusters caused by asymptomatic infections sequenced the spike gene of viruses isolated from 12 patients. All cases of the two clusters we investigated had clear contact histories, both from Wuhan, Hubei province. The viruses isolated from two outbreaks in Anhui were found to show a genetically close link to the virus from Wuhan. In addition, new single nucleotide variations were discovered in the spike gene. Conclusions Both clusters may have resulted from close contact and droplet-spreading and asymptomatic infections were identified as the initial cause. We also analyzed the infectiousness of asymptomatic cases and the challenges to the current epidemic to provided information for the development of control strategies.
Collapse
Affiliation(s)
- Yuan Yuan
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Jun He
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Lei Gong
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Weiwei Li
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Liangliang Jiang
- Maanshan Center for Disease Control and Prevention, 849, Jiangdong Avenue, Maanshan, China
| | - Jiang Liu
- Huainan Center for Disease Control and Prevention, Linchang Avenue, Huainan, China
| | - Qingqing Chen
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Junling Yu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Sai Hou
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Yonglin Shi
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Siqi Lu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Zhuhui Zhang
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Yinglu Ge
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Nan Sa
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China
| | - Lan He
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Jiabing Wu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China
| | - Yong Sun
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China. .,Key Laboratory for Medical and Health of the 13th Five-Year Plan, 12560, Fanhua Avenue, Hefei, Anhui, China.
| | - Zhirong Liu
- Anhui Provincial Center for Disease Control and Prevention, 12560, Fanhua Avenue, Hefei, China.
| |
Collapse
|
15
|
Hwang HS, Chang M, Kim YA. Influenza-Host Interplay and Strategies for Universal Vaccine Development. Vaccines (Basel) 2020; 8:vaccines8030548. [PMID: 32962304 PMCID: PMC7564814 DOI: 10.3390/vaccines8030548] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 12/24/2022] Open
Abstract
Influenza is an annual epidemic and an occasional pandemic caused by pathogens that are responsible for infectious respiratory disease. Humans are highly susceptible to the infection mediated by influenza A viruses (IAV). The entry of the virus is mediated by the influenza virus hemagglutinin (HA) glycoprotein that binds to the cellular sialic acid receptors and facilitates the fusion of the viral membrane with the endosomal membrane. During IAV infection, virus-derived pathogen-associated molecular patterns (PAMPs) are recognized by host intracellular specific sensors including toll-like receptors (TLRs), C-type lectin receptors, retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs), and nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) either on the cell surface or intracellularly in endosomes. Herein, we comprehensively review the current knowledge available on the entry of the influenza virus into host cells and the molecular details of the influenza virus–host interface. We also highlight certain strategies for the development of universal influenza vaccines.
Collapse
Affiliation(s)
- Hye Suk Hwang
- Alan G. MacDiarmid Energy Research Institute, Chonnam National University, Gwangju 61186, Korea;
| | - Mincheol Chang
- Alan G. MacDiarmid Energy Research Institute, Chonnam National University, Gwangju 61186, Korea;
- Department of Polymer Engineering, Graduate School, Chonnam National University, Gwangju 61186, Korea
- School of Polymer Science and Engineering, Chonnam National University, Gwangju 61186, Korea
- Correspondence: (M.C.); (Y.A.K.); Tel.: +82-62-530-1771 (M.C.); +82-62-530-1871 (Y.A.K.)
| | - Yoong Ahm Kim
- Alan G. MacDiarmid Energy Research Institute, Chonnam National University, Gwangju 61186, Korea;
- Department of Polymer Engineering, Graduate School, Chonnam National University, Gwangju 61186, Korea
- School of Polymer Science and Engineering, Chonnam National University, Gwangju 61186, Korea
- Correspondence: (M.C.); (Y.A.K.); Tel.: +82-62-530-1771 (M.C.); +82-62-530-1871 (Y.A.K.)
| |
Collapse
|
16
|
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.
Collapse
|
17
|
Georgieva M, Buckee CO, Lipsitch M. Models of immune selection for multi-locus antigenic diversity of pathogens. Nat Rev Immunol 2019; 19:55-62. [PMID: 30479379 DOI: 10.1038/s41577-018-0092-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
It is well accepted that pathogens can evade recognition and elimination by the host immune system by varying their antigenic targets. Thus, it has become a truism that host immunity is a major driver and determinant of the antigenic diversity of pathogens. However, it remains puzzling how host immunity selects for antigenic diversity at the level of the pathogen population, given that hosts have acquired immune responses to multiple antigens of most pathogens - sometimes through multiple effectors of both humoral and cellular immunity. In this Opinion article, we address this puzzle and the related question of why pathogens often have diversity at multiple antigenic loci. Here, we describe five hypotheses to explain the polymorphism of multiple antigens in a single pathogen species and highlight research relevant to our current models of thinking about multi-locus antigenic diversity.
Collapse
Affiliation(s)
- Maria Georgieva
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Department of Physiology, University of Lausanne, Lausanne, Switzerland.
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
18
|
Gilbert PB, Fong Y, Juraska M, Carpp LN, Monto AS, Martin ET, Petrie JG. HAI and NAI titer correlates of inactivated and live attenuated influenza vaccine efficacy. BMC Infect Dis 2019; 19:453. [PMID: 31117986 PMCID: PMC6530189 DOI: 10.1186/s12879-019-4049-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 04/30/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND High hemagglutination inhibition (HAI) and neuraminidase inhibition (NAI) titers are generally associated with reduced influenza risk. While repeated influenza vaccination reduces seroresponse, vaccine effectiveness is not always reduced. METHODS During the 2007-2008 influenza season, a randomized, placebo-controlled trial (FLUVACS) evaluated the efficacies of live-attenuated (LAIV) and inactivated influenza vaccines (IIV) among healthy adults aged 18-49 in Michigan; IIV vaccine efficacy (VE) and LAIV VE against influenza disease were estimated at 68% and 36%. Using the principal stratification/VE moderation framework, we analyzed data from this trial to assess how each VE varied by HAI or NAI responses to vaccination observed for vaccinated individuals and predicted counterfactually for placebo recipients. We also assessed how each VE varied with pre-vaccination/baseline variables including HAI titer, NAI titer, and vaccination history. RESULTS IIV VE appeared to increase with Day 30 post-vaccination HAI titer, albeit not significantly (p=0.20 and estimated VE 14.4%, 70.5%, and 85.5% at titer below the assay lower quantification limit, 512, and 4096 (maximum)). Moreover, IIV VE increased significantly with Day 30 post-vaccination NAI titer (p=0.040), with estimated VE zero at titer 10 and 92.2% at highest titer 640. There was no evidence that fold-change in post-vaccination HAI or NAI titer associated with IIV VE (p=0.76, 0.38). For LAIV, there was no evidence that VE associated with post-vaccination or fold-rise HAI or NAI titers (p-values >0.40). For IIV, VE increased with increasing baseline NAI titer in those previously vaccinated, but VE decreased with increasing baseline NAI titer in those previously unvaccinated. In contrast, for LAIV, VE did not depend on previous vaccination or baseline HAI or NAI titer. CONCLUSIONS Future efficacy trials should measure baseline and post-vaccination antibody titers in both vaccine and control/placebo recipients, enabling analyses to better elucidate correlates of vaccine- and natural-protection. TRIAL REGISTRATION ClinicalTrials.gov NCT00538512. October 1, 2007.
Collapse
Affiliation(s)
- Peter B Gilbert
- Department of Biostatistics, Bioinformatics, and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, 98109, USA. .,Department of Biostatistics, University of Washington, 1705 NE Pacific St., Seattle, 98195, USA.
| | - Youyi Fong
- Department of Biostatistics, Bioinformatics, and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, 98109, USA.,Department of Biostatistics, University of Washington, 1705 NE Pacific St., Seattle, 98195, USA
| | - Michal Juraska
- Department of Biostatistics, Bioinformatics, and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, 98109, USA
| | - Lindsay N Carpp
- Department of Biostatistics, Bioinformatics, and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, 98109, USA
| | - Arnold S Monto
- Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, 48109, USA
| | - Emily T Martin
- Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, 48109, USA
| | - Joshua G Petrie
- Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, 48109, USA
| |
Collapse
|
19
|
Halder AK, Dutta P, Kundu M, Basu S, Nasipuri M. Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions. Brief Funct Genomics 2018; 17:381-391. [PMID: 29028879 PMCID: PMC7109800 DOI: 10.1093/bfgp/elx026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.
Collapse
Affiliation(s)
- Anup Kumar Halder
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Pritha Dutta
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mahantapas Kundu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, India
| |
Collapse
|
20
|
Adabor ES, Ndifon W. Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180113. [PMID: 30109067 PMCID: PMC6083687 DOI: 10.1098/rsos.180113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 06/19/2018] [Indexed: 06/08/2023]
Abstract
Haemagglutination inhibition (HI) assays are typically used for comparing and characterizing influenza viruses. Data obtained from the assays (titres) are used quantitatively to determine antigenic differences between influenza strains. However, the use of these titres has been criticized as they sometimes fail to capture accurate antigenic differences between strains. Our previous analytical work revealed how antigenic and non-antigenic variables contribute to the titres. Building on this previous work, we have developed a Bayesian method for decoupling antigenic and non-antigenic contributions to the titres in this paper. We apply this method to a compendium of HI titres of influenza A (H3N2) viruses curated from 1968 to 2016. Remarkably, the results of this fit indicate that the non-antigenic variable, which is inversely correlated with viral avidity for the red blood cells used in HI assays, oscillates during the course of influenza virus evolution, with a period that corresponds roughly to the timescale on which antigenic variants replace each other. Together, the results suggest that the new Bayesian method is applicable to the analysis of long-term dynamics of both antigenic and non-antigenic properties of influenza virus.
Collapse
Affiliation(s)
- Emmanuel S. Adabor
- Research Centre, African Institute for Mathematical Sciences, Cape Town, South Africa
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Wilfred Ndifon
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
- Research Department, African Institute for Mathematical Sciences, Next Einstein Initiative, Kigali, Rwanda
| |
Collapse
|
21
|
Du X, King AA, Woods RJ, Pascual M. Evolution-informed forecasting of seasonal influenza A (H3N2). Sci Transl Med 2018; 9:9/413/eaan5325. [PMID: 29070700 DOI: 10.1126/scitranslmed.aan5325] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 05/26/2017] [Accepted: 08/16/2017] [Indexed: 12/13/2022]
Abstract
Interpandemic or seasonal influenza A, currently subtypes H3N2 and H1N1, exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus' antigenic evolution. We propose a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States for more than 10 years, we demonstrate the feasibility of skillful prediction for total cases ahead of season, with a tendency to underpredict monthly peak epidemic size, and an accurate real-time forecast for the 2016/2017 influenza season.
Collapse
Affiliation(s)
- Xiangjun Du
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA
| | - Aaron A King
- Departments of Ecology and Evolutionary Biology and Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert J Woods
- University of Michigan Health System, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mercedes Pascual
- Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA. .,Santa Fe Institute, Santa Fe, NM 87501, USA
| |
Collapse
|
22
|
Abstract
The basic reproduction ratio, R0, is a fundamental concept in epidemiology. It is defined as the total number of secondary infections brought on by a single primary infection, in a totally susceptible population. The value of R0 indicates whether a starting epidemic reaches a considerable part of the population and causes a lot of damage, or whether it remains restricted to a relatively small number of individuals. To calculate R0 one has to evaluate an integral that ranges over the duration of the infection of the host. This duration is, of course, limited by remaining host longevity. So, R0 depends on remaining host longevity and in this paper we show that for long-lived hosts this aspect may not be ignored for long-lasting infections. We investigate in particular how this epidemiological measure of pathogen fitness depends on host longevity. For our analyses we adopt and combine a generic within- and between-host model from the literature. To find the optimal strategy for a pathogen from an evolutionary point of view, we focus on the indicator \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$R_0^{{opt}}$$\end{document}R0opt, i.e., the optimum of R0 as a function of its replication and mutation rates. These are the within-host parameters that the pathogen has at its disposal to optimize its strategy. We show that \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$R_0^{{opt}}$$\end{document}R0opt is highly influenced by remaining host longevity in combination with the contact rate between hosts in a susceptible population. In addition, these two parameters determine whether a killer-like or a milker-like strategy is optimal for a given pathogen. In the killer-like strategy the pathogen has a high rate of reproduction within the host in a short time span causing a relatively short disease, whereas in the milker-like strategy the pathogen multiplies relatively slowly, producing a continuous small amount of offspring over time with a small effect on host health. The present research allows for the determination of a bifurcation line in the plane of host longevity versus contact rate that forms the boundary between the milker-like and killer-like regions. This plot shows that for short remaining host longevities the killer-like strategy is optimal, whereas for very long remaining host longevities the milker-like strategy is advantageous. For in-between values of host longevity, the contact rate determines which of both strategies is optimal.
Collapse
Affiliation(s)
- L. M. Viljoen
- Department of Mathematics and Applied Mathematics, North West University, Potchefstroom, North West South Africa
| | - L. Hemerik
- Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Wageningen, The Netherlands
| | - J. Molenaar
- Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Wageningen, The Netherlands
| |
Collapse
|
23
|
Ustinov NB, Zavyalova EG, Smirnova IG, Kopylov AM. The Power and Limitations of Influenza Virus Hemagglutinin Assays. BIOCHEMISTRY (MOSCOW) 2018; 82:1234-1248. [PMID: 29223151 DOI: 10.1134/s0006297917110025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Influenza virus hemagglutinins (HAs) are surface proteins that bind to sialic acid residues at the host cell surface and ensure further virus internalization. Development of methods for the inhibition of these processes drives progress in the design of new antiviral drugs. The state of the isolated HA (i.e. combining tertiary structure and extent of oligomerization) is defined by multiple factors, like the HA source and purification method, posttranslational modifications, pH, etc. The HA state affects HA functional activity and significantly impacts the results of numerous HA assays. In this review, we analyze the power and limitations of currently used HA assays regarding the state of HA.
Collapse
Affiliation(s)
- N B Ustinov
- Lomonosov Moscow State University, Faculty of Chemistry, Moscow, 119991, Russia.
| | | | | | | |
Collapse
|
24
|
Abstract
Influenza A virus (IAV) is an RNA virus with a segmented genome. These viral properties allow for the rapid evolution of IAV under selective pressure, due to mutation occurring from error-prone replication and the exchange of gene segments within a co-infected cell, termed reassortment. Both mutation and reassortment give rise to genetic diversity, but constraints shape their impact on viral evolution: just as most mutations are deleterious, most reassortment events result in genetic incompatibilities. The phenomenon of segment mismatch encompasses both RNA- and protein-based incompatibilities between co-infecting viruses and results in the production of progeny viruses with fitness defects. Segment mismatch is an important determining factor of the outcomes of mixed IAV infections and has been addressed in multiple risk assessment studies undertaken to date. However, due to the complexity of genetic interactions among the eight viral gene segments, our understanding of segment mismatch and its underlying mechanisms remain incomplete. Here, we summarize current knowledge regarding segment mismatch and discuss the implications of this phenomenon for IAV reassortment and diversity.
Collapse
Affiliation(s)
- Maria C White
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| | - Anice C Lowen
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
| |
Collapse
|
25
|
Hill SC, Manvell RJ, Schulenburg B, Shell W, Wikramaratna PS, Perrins C, Sheldon BC, Brown IH, Pybus OG. Antibody responses to avian influenza viruses in wild birds broaden with age. Proc Biol Sci 2017; 283:rspb.2016.2159. [PMID: 28003449 PMCID: PMC5204166 DOI: 10.1098/rspb.2016.2159] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 11/16/2016] [Indexed: 11/12/2022] Open
Abstract
For viruses such as avian influenza, immunity within a host population can drive the emergence of new strains by selecting for viruses with novel antigens that avoid immune recognition. The accumulation of acquired immunity with age is hypothesized to affect how influenza viruses emerge and spread in species of different lifespans. Despite its importance for understanding the behaviour of avian influenza viruses, little is known about age-related accumulation of immunity in the virus's primary reservoir, wild birds. To address this, we studied the age structure of immune responses to avian influenza virus in a wild swan population (Cygnus olor), before and after the population experienced an outbreak of highly pathogenic H5N1 avian influenza in 2008. We performed haemagglutination inhibition assays on sampled sera for five avian influenza strains and show that breadth of response accumulates with age. The observed age-related distribution of antibody responses to avian influenza strains may explain the age-dependent mortality observed during the highly pathogenic H5N1 outbreak. Age structures and species lifespan are probably important determinants of viral epidemiology and virulence in birds.
Collapse
Affiliation(s)
- Sarah C Hill
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Ruth J Manvell
- Department of Virology, Animal and Plant Health Agency (APHA), Weybridge KT15 3NB, UK
| | | | - Wendy Shell
- Department of Virology, Animal and Plant Health Agency (APHA), Weybridge KT15 3NB, UK
| | | | - Christopher Perrins
- Edward Grey Institute, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Ben C Sheldon
- Edward Grey Institute, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Ian H Brown
- Department of Virology, Animal and Plant Health Agency (APHA), Weybridge KT15 3NB, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| |
Collapse
|
26
|
Raghwani J, Thompson RN, Koelle K. Selection on non-antigenic gene segments of seasonal influenza A virus and its impact on adaptive evolution. Virus Evol 2017; 3:vex034. [PMID: 29250432 PMCID: PMC5724400 DOI: 10.1093/ve/vex034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Most studies on seasonal influenza A/H3N2 virus adaptation have focused on the main antigenic gene, hemagglutinin. However, there is increasing evidence that the genome-wide genetic background of novel antigenic variants can influence these variants’ emergence probabilities and impact their patterns of dominance in the population. This suggests that non-antigenic genes may be important in shaping the viral evolutionary dynamics. To better understand the role of selection on non-antigenic genes in the adaptive evolution of seasonal influenza viruses, we have developed a simple population genetic model that considers a virus with one antigenic and one non-antigenic gene segment. By simulating this model under different regimes of selection and reassortment, we find that the empirical patterns of lineage turnover for the antigenic and non-antigenic gene segments are best captured when there is both limited viral coinfection and selection operating on both gene segments. In contrast, under a scenario of only neutral evolution in the non-antigenic gene segment, we see persistence of multiple lineages for long periods of time in that segment, which is not compatible with observed molecular evolutionary patterns. Further, we find that reassortment, occurring in coinfected individuals, can increase the speed of viral adaptive evolution by primarily reducing selective interference and genetic linkage effects. Together, these findings suggest that, for influenza, with six internal or non-antigenic gene segments, the evolutionary dynamics of novel antigenic variants are likely to be influenced by the genome-wide genetic background as a result of linked selection among both beneficial and deleterious mutations.
Collapse
Affiliation(s)
- Jayna Raghwani
- Department of Zoology, University of Oxford, Oxford, OX1 3SY, UK
| | - Robin N Thompson
- Department of Zoology, University of Oxford, Oxford, OX1 3SY, UK
| | - Katia Koelle
- Department of Biology, Duke University, Durham, NC 27708, USA
| |
Collapse
|
27
|
Rougeron V, Tiedje KE, Chen DS, Rask TS, Gamboa D, Maestre A, Musset L, Legrand E, Noya O, Yalcindag E, Renaud F, Prugnolle F, Day KP. Evolutionary structure of Plasmodium falciparum major variant surface antigen genes in South America: Implications for epidemic transmission and surveillance. Ecol Evol 2017; 7:9376-9390. [PMID: 29187975 PMCID: PMC5696401 DOI: 10.1002/ece3.3425] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 07/07/2017] [Accepted: 08/19/2017] [Indexed: 11/11/2022] Open
Abstract
Strong founder effects resulting from human migration out of Africa have led to geographic variation in single nucleotide polymorphisms (SNPs) and microsatellites (MS) of the malaria parasite, Plasmodium falciparum. This is particularly striking in South America where two major founder populations of P. falciparum have been identified that are presumed to have arisen from the transatlantic slave trade. Given the importance of the major variant surface antigen of the blood stages of P. falciparum as both a virulence factor and target of immunity, we decided to investigate the population genetics of the genes encoding “Plasmodium falciparum Erythrocyte Membrane Protein 1” (PfEMP1) among several countries in South America, in order to evaluate the transmission patterns of malaria in this continent. Deep sequencing of the DBLα domain of var genes from 128 P. falciparum isolates from five locations in South America was completed using a 454 high throughput sequencing protocol. Striking geographic variation in var DBLα sequences, similar to that seen for SNPs and MS markers, was observed. Colombia and French Guiana had distinct var DBLα sequences, whereas Peru and Venezuela showed an admixture. The importance of such geographic variation to herd immunity and malaria vaccination is discussed.
Collapse
Affiliation(s)
- Virginie Rougeron
- Department of Microbiology Division of Parasitology New York University School of Medicine New York NY USA.,MIVEGEC (Laboratoire Maladies Infectieuses et Vecteurs, Ecologie, Génétique, Evolution et Contrôle), UMR CNRS 5290/IRD 224 Université Montpellier 1 Université Montpellier 2 Montpellier France
| | - Kathryn E Tiedje
- Department of Microbiology Division of Parasitology New York University School of Medicine New York NY USA.,School of BioSciences Bio21 Institute/University of Melbourne Parkville Vic. Australia
| | - Donald S Chen
- Department of Microbiology Division of Parasitology New York University School of Medicine New York NY USA
| | - Thomas S Rask
- Department of Microbiology Division of Parasitology New York University School of Medicine New York NY USA.,School of BioSciences Bio21 Institute/University of Melbourne Parkville Vic. Australia
| | - Dionicia Gamboa
- Instituto de Medicina Tropical Alexander Von Humboldt and Departamento de Ciencias Celulares y Moleculares Facultad de Ciencias y Filosofia Universidad Peruana Cayetano Heredia Lima Peru
| | - Amanda Maestre
- Grupo Salud y Comunidad Facultad de Medicina Universidad de Antioquía Medellín Colombia
| | - Lise Musset
- Parasitology UnitInstitut Pasteur de Guyane Cayenne Cedex French Guiana
| | - Eric Legrand
- Parasitology UnitInstitut Pasteur de Guyane Cayenne Cedex French Guiana.,Unit of Genetics and Genomics on Insect Vectors Institut Pasteur Paris France
| | - Oscar Noya
- Centro para Estudios Sobre Malaria Instituto de Altos Estudios en Salud "Dr. Arnoldo Gabaldón" Ministerio del Poder Popular para la Salud and Instituto de Medicina Tropical Universidad Central de Venezuela Caracas Venezuela
| | - Erhan Yalcindag
- MIVEGEC (Laboratoire Maladies Infectieuses et Vecteurs, Ecologie, Génétique, Evolution et Contrôle), UMR CNRS 5290/IRD 224 Université Montpellier 1 Université Montpellier 2 Montpellier France
| | - François Renaud
- MIVEGEC (Laboratoire Maladies Infectieuses et Vecteurs, Ecologie, Génétique, Evolution et Contrôle), UMR CNRS 5290/IRD 224 Université Montpellier 1 Université Montpellier 2 Montpellier France
| | - Franck Prugnolle
- MIVEGEC (Laboratoire Maladies Infectieuses et Vecteurs, Ecologie, Génétique, Evolution et Contrôle), UMR CNRS 5290/IRD 224 Université Montpellier 1 Université Montpellier 2 Montpellier France
| | - Karen P Day
- Department of Microbiology Division of Parasitology New York University School of Medicine New York NY USA.,School of BioSciences Bio21 Institute/University of Melbourne Parkville Vic. Australia
| |
Collapse
|
28
|
Construction of Multilevel Structure for Avian Influenza Virus System Based on Granular Computing. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5404180. [PMID: 28191464 PMCID: PMC5278516 DOI: 10.1155/2017/5404180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 12/01/2016] [Accepted: 12/14/2016] [Indexed: 12/03/2022]
Abstract
Exploring the genetic structure of influenza viruses attracts the attention in the field of molecular ecology and medical genetics, whose epidemics cause morbidity and mortality worldwide. The rapid variations in RNA strand and changes of protein structure of the virus result in low-accuracy subtyping identification and make it difficult to develop effective drugs and vaccine. This paper constructs the evolutionary structure of avian influenza virus system considering both hemagglutinin and neuraminidase protein fragments. An optimization model was established to determine the rational granularity of the virus system for exploring the intrinsic relationship among the subtypes based on the fuzzy hierarchical evaluation index. Thus, an algorithm was presented to extract the rational structure. Furthermore, to reduce the systematic and computational complexity, the granular signatures of virus system were identified based on the coarse-grained idea and then its performance was evaluated through a designed classifier. The results showed that the obtained virus signatures could approximate and reflect the whole avian influenza virus system, indicating that the proposed method could identify the effective virus signatures. Once a new molecular virus is detected, it is efficient to identify the homologous virus hierarchically.
Collapse
|
29
|
Chen H, Zhou X, Zheng J, Kwoh CK. Rules of co-occurring mutations characterize the antigenic evolution of human influenza A/H3N2, A/H1N1 and B viruses. BMC Med Genomics 2016; 9:69. [PMID: 28117657 PMCID: PMC5260787 DOI: 10.1186/s12920-016-0230-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The human influenza viruses undergo rapid evolution (especially in hemagglutinin (HA), a glycoprotein on the surface of the virus), which enables the virus population to constantly evade the human immune system. Therefore, the vaccine has to be updated every year to stay effective. There is a need to characterize the evolution of influenza viruses for better selection of vaccine candidates and the prediction of pandemic strains. Studies have shown that the influenza hemagglutinin evolution is driven by the simultaneous mutations at antigenic sites. Here, we analyze simultaneous or co-occurring mutations in the HA protein of human influenza A/H3N2, A/H1N1 and B viruses to predict potential mutations, characterizing the antigenic evolution. METHODS We obtain the rules of mutation co-occurrence using association rule mining after extracting HA1 sequences and detect co-mutation sites under strong selective pressure. Then we predict the potential drifts with specific mutations of the viruses based on the rules and compare the results with the "observed" mutations in different years. RESULTS The sites under frequent mutations are in antigenic regions (epitopes) or receptor binding sites. CONCLUSIONS Our study demonstrates the co-occurring site mutations obtained by rule mining can capture the evolution of influenza viruses, and confirms that cooperative interactions among sites of HA1 protein drive the influenza antigenic evolution.
Collapse
Affiliation(s)
- Haifen Chen
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore
| | - Xinrui Zhou
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore
| | - Jie Zheng
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore
- Genome Institute of Singapore, A*STAR, Biopolis, 138672, Singapore, Singapore
| | - Chee-Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| |
Collapse
|
30
|
Abstract
For infectious diseases, a genetic cluster is a group of closely related infections that is usually interpreted as representing a recent outbreak of transmission. Genetic clustering methods are becoming increasingly popular for molecular epidemiology, especially in the context of HIV where there is now considerable interest in applying these methods to prioritize groups for public health resources such as pre-exposure prophylaxis. To date, genetic clustering has generally been performed with ad hoc algorithms, only some of which have since been encoded and distributed as free software. These algorithms have seldom been validated on simulated data where clusters are known, and their interpretation and similarities are not transparent to users outside of the field. Here, I provide a brief overview on the development and inter-relationships of genetic clustering methods, and an evaluation of six methods on data simulated under an epidemic model in a risk-structured population. The simulation analysis demonstrates that the majority of clustering methods are systematically biased to detect variation in sampling rates among subpopulations, not variation in transmission rates. I discuss these results in the context of previous work and the implications for public health applications of genetic clustering.
Collapse
Affiliation(s)
- Art F Y Poon
- Department of Pathology and Laboratory Medicine, Western University, London, Canada
| |
Collapse
|
31
|
Furuse Y, Oshitani H. Mechanisms of replacement of circulating viruses by seasonal and pandemic influenza A viruses. Int J Infect Dis 2016; 51:6-14. [PMID: 27569827 DOI: 10.1016/j.ijid.2016.08.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Revised: 08/10/2016] [Accepted: 08/21/2016] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND Seasonal influenza causes annual epidemics by the accumulation of antigenic changes. Pandemic influenza occurs through a major antigenic change of the influenza A virus, which can originate from other hosts. Although new antigenic variants of the influenza A virus replace formerly circulating seasonal and pandemic viruses, replacement mechanisms remain poorly understood. METHODS A stochastic individual-based SEIR (susceptible-exposed-infectious-recovered) model with two viral strains (formerly circulating old strain and newly emerged strain) was developed for simulations to elucidate the replacement mechanisms. RESULTS Factors and conditions of virus and host populations affecting the replacement were identified. Replacement is more likely to occur in tropical regions than temperate regions. The magnitude of the ongoing epidemic by the old strain, herd immunity against the old strain, and timing of appearance of the new strain are not that important for replacement. It is probable that the frequency of replacement by a pandemic virus is higher than a seasonal virus because of the high initial susceptibility and high basic reproductive number of the pandemic virus. CONCLUSIONS The findings of this study on replacement mechanisms could lead to a better understanding of virus transmission dynamics and may possibly be helpful in establishing an effective strategy to mitigate the impact of seasonal and pandemic influenza.
Collapse
Affiliation(s)
- Yuki Furuse
- Department of Virology, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Japan.
| | - Hitoshi Oshitani
- Department of Virology, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Japan
| |
Collapse
|
32
|
Gandon S, Day T, Metcalf CJE, Grenfell BT. Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases. Trends Ecol Evol 2016; 31:776-788. [PMID: 27567404 DOI: 10.1016/j.tree.2016.07.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 07/20/2016] [Accepted: 07/21/2016] [Indexed: 10/21/2022]
Abstract
Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.
Collapse
Affiliation(s)
- Sylvain Gandon
- CEFE UMR 5175, CNRS-Université de Montpellier-Université Paul-Valéry Montpellier-EPHE, 1919 route de Mende, 34293 Montpellier cedex 5, France.
| | - Troy Day
- Department of Biology, Queen's University, Kingston, Canada
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| |
Collapse
|
33
|
Uekermann F, Sneppen K. A cross-immunization model for the extinction of old influenza strains. Sci Rep 2016; 6:25907. [PMID: 27174658 PMCID: PMC4865727 DOI: 10.1038/srep25907] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 04/22/2016] [Indexed: 11/25/2022] Open
Abstract
Given the frequent mutation of antigenic features, the constancy of genetic and antigenic diversity of influenza within a subtype is surprising. While the emergence of new strains and antigenic features is commonly attributed to selection by the human immune system, the mechanism that ensures the extinction of older strains remains controversial. To replicate this dynamics of replacement current models utilize mechanisms such as short-lived strain-transcending immunity, a direct competition for hosts, stochastic extinction or constrained antigenic evolution. Building on the idea of short-lived immunity we introduce a minimal model that exhibits the aforementioned dynamics of replacement. Our model relies only on competition due to an antigen specific immune-response in an unconstrained antigenic space. Furthermore the model explains the size of typical influenza epidemics as well as the tendency that new epidemics are associated with mutations of old antigens.
Collapse
Affiliation(s)
| | - Kim Sneppen
- Niels Bohr Institute, University of Copenhagen, Denmark
| |
Collapse
|
34
|
Labonté K, Aris-Brosou S. Automatic detection of rate change in large data sets with an unsupervised approach: the case of influenza viruses. Genome 2016; 59:253-62. [PMID: 26966881 DOI: 10.1139/gen-2015-0163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Influenza viruses evolve at such a high rate that vaccine recommendations need to be changed, but not quite on a regular basis. This observation suggests that the rate of evolution of these viruses is not constant through time, which begs the question as to when such rate changes occur, if they do so independently of the host in which they circulate and (or) independently of their subtype. To address these outstanding questions, we introduce a novel heuristics, Mclust*, that is based on a two-tier clustering approach in a phylogenetic context to estimate (i) absolute rates of evolution and (ii) when rate change occurs. We employ the novel approach to compare the two influenza surface proteins, hemagglutinin and neuraminidase, that circulated in avian, human, and swine hosts between 1960 and 2014 in two subtypes: H3N2 and H1N1. We show that the algorithm performs well in most conditions, accounting for phylogenetic uncertainty by means of bootstrapping and scales up to analyze very large data sets. Our results show that our approach is robust to the time-dependent artifact of rate estimation, and confirm pervasive punctuated evolution across hosts and subtypes. As such, the novel approach can potentially detect when vaccine composition needs to be updated.
Collapse
Affiliation(s)
- Kasandra Labonté
- a Department of Biology, Centre for Advanced Research in Environmental Genomics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Stéphane Aris-Brosou
- a Department of Biology, Centre for Advanced Research in Environmental Genomics, University of Ottawa, Ottawa, ON K1N 6N5, Canada.,b Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| |
Collapse
|
35
|
He F, Leyrer S, Kwang J. Strategies towards universal pandemic influenza vaccines. Expert Rev Vaccines 2015; 15:215-25. [DOI: 10.1586/14760584.2016.1115352] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Fang He
- Animal Health Biotechnology, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Sonja Leyrer
- Emergent Product Development Germany GmbH, Munich, Germany
| | - Jimmy Kwang
- Animal Health Biotechnology, Temasek Life Sciences Laboratory, Singapore, Singapore
- Department of Microbiology, Faculty of Medicine, National University of Singapore, Singapore, Singapore
| |
Collapse
|
36
|
Belanov SS, Bychkov D, Benner C, Ripatti S, Ojala T, Kankainen M, Kai Lee H, Wei-Tze Tang J, Kainov DE. Genome-Wide Analysis of Evolutionary Markers of Human Influenza A(H1N1)pdm09 and A(H3N2) Viruses May Guide Selection of Vaccine Strain Candidates. Genome Biol Evol 2015; 7:3472-83. [PMID: 26615216 PMCID: PMC4700966 DOI: 10.1093/gbe/evv240] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Here we analyzed whole-genome sequences of 3,969 influenza A(H1N1)pdm09 and 4,774 A(H3N2) strains that circulated during 2009–2015 in the world. The analysis revealed changes at 481 and 533 amino acid sites in proteins of influenza A(H1N1)pdm09 and A(H3N2) strains, respectively. Many of these changes were introduced as a result of random drift. However, there were 61 and 68 changes that were present in relatively large number of A(H1N1)pdm09 and A(H3N2) strains, respectively, that circulated during relatively long time. We named these amino acid substitutions evolutionary markers, as they seemed to contain valuable information regarding the viral evolution. Interestingly, influenza A(H1N1)pdm09 and A(H3N2) viruses acquired non-overlapping sets of evolutionary markers. We next analyzed these characteristic markers in vaccine strains recommended by the World Health Organization for the past five years. Our analysis revealed that vaccine strains carried only few evolutionary markers at antigenic sites of viral hemagglutinin (HA) and neuraminidase (NA). The absence of these markers at antigenic sites could affect the recognition of HA and NA by human antibodies generated in response to vaccinations. This could, in part, explain moderate efficacy of influenza vaccines during 2009–2014. Finally, we identified influenza A(H1N1)pdm09 and A(H3N2) strains, which contain all the evolutionary markers of influenza A strains circulated in 2015, and which could be used as vaccine candidates for the 2015/2016 season. Thus, genome-wide analysis of evolutionary markers of influenza A(H1N1)pdm09 and A(H3N2) viruses may guide selection of vaccine strain candidates.
Collapse
Affiliation(s)
- Sergei S Belanov
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Dmitrii Bychkov
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Christian Benner
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland Department of Public Health, Hjelt Institute, University of Helsinki, Helsinki, Finland Welcome Trust Sanger Institute, Cambridgeshire, United Kingdom
| | - Teija Ojala
- Institute of Biomedicine, Pharmacology, University of Helsinki, Helsinki, Finland
| | - Matti Kankainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Hong Kai Lee
- Department of Laboratory Medicine, National University Hospital, National University Health System, Singapore
| | - Julian Wei-Tze Tang
- Clinical Microbiology, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - Denis E Kainov
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| |
Collapse
|
37
|
Koelle K, Rasmussen DA. The effects of a deleterious mutation load on patterns of influenza A/H3N2's antigenic evolution in humans. eLife 2015; 4:e07361. [PMID: 26371556 PMCID: PMC4611170 DOI: 10.7554/elife.07361] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 09/14/2015] [Indexed: 11/19/2022] Open
Abstract
Recent phylogenetic analyses indicate that RNA virus populations carry a significant deleterious mutation load. This mutation load has the potential to shape patterns of adaptive evolution via genetic linkage to beneficial mutations. Here, we examine the effect of deleterious mutations on patterns of influenza A subtype H3N2's antigenic evolution in humans. By first analyzing simple models of influenza that incorporate a mutation load, we show that deleterious mutations, as expected, act to slow the virus's rate of antigenic evolution, while making it more punctuated in nature. These models further predict three distinct molecular pathways by which antigenic cluster transitions occur, and we find phylogenetic patterns consistent with each of these pathways in influenza virus sequences. Simulations of a more complex phylodynamic model further indicate that antigenic mutations act in concert with deleterious mutations to reproduce influenza's spindly hemagglutinin phylogeny, co-circulation of antigenic variants, and high annual attack rates. DOI:http://dx.doi.org/10.7554/eLife.07361.001 Each year, up to 15% of the world's population experience symptoms of an influenza infection, also commonly known as flu. The most common culprit is a strain of the virus called influenza type A subtype H3N2. One reason that so many people become infected each year is that this virus evolves rapidly. Within a few years, proteins on the surface of the virus known as antigens become less recognizable to the immune system of a person who has been previously infected. This means that the person can become ill with the virus again because their immune system cannot mount an effective response to the evolved virus strain. Influenza virus strains evolve rapidly because their genetic material accumulates mutations quickly. Although some of these mutations are beneficial to the virus, other mutations are harmful and reduce the ability of the virus to spread. Sometimes beneficial mutations may occur alongside harmful ones, but it is not known how the harmful mutations affect the evolution of the virus. Here, Koelle and Rasmussen used computer models of H3N2 influenza to examine the effect of harmful mutations on the evolution of this virus population. The models show that harmful mutations limit how quickly the antigens can evolve. Also, the presence of these harmful mutations effectively acts as a sieve: they allow only large changes in the antigens to establish in the virus population. The models suggest that there are three routes by which large changes in the antigens on H3N2 viruses may occur. The first is by a single mutation that has a big effect on the antigens in viruses that only carry a few harmful mutations, but these large mutations would not happen very often. Another route may be through more common mutations that have only a small or moderate benefit, which would allow the virus to become more common in the population before it acquires a beneficial mutation with a much greater effect. The third possibility is that a large beneficial mutation may arise in viruses that have many harmful mutations. These harmful mutations may initially limit the ability of the virus to spread, but over time, some of these harmful mutations may then be lost. Koelle and Rasmussen found that the computer models could recreate the patterns of virus evolution that have been observed in real strains of H3N2. Researchers use predictions of influenza evolution to help them decide which virus strains should be included in flu vaccines each year. Koelle and Rasmussen findings indicate that harmful mutations should be considered when making these predictions. DOI:http://dx.doi.org/10.7554/eLife.07361.002
Collapse
Affiliation(s)
- Katia Koelle
- Department of Biology, Duke University, Durham, United States.,Fogarty International Center, National Institutes of Health, Bethesda, United States
| | - David A Rasmussen
- Department of Biology, Duke University, Durham, United States.,Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule Zürich, Basel, Switzerland
| |
Collapse
|
38
|
Li D, Cui J, Liu M, Liu S. The Evolutionary Dynamics of Stochastic Epidemic Model with Nonlinear Incidence Rate. Bull Math Biol 2015; 77:1705-43. [PMID: 26369670 PMCID: PMC7088780 DOI: 10.1007/s11538-015-0101-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Accepted: 09/01/2015] [Indexed: 11/26/2022]
Abstract
A stochastic SIRS epidemic model with nonlinear incidence rate and varying population size is formulated to investigate the effect of stochastic environmental variability on inter-pandemic transmission dynamics of influenza A. Sufficient conditions for extinction and persistence of the disease are established. In the case of persistence, the existence of endemic stationary distribution is proved and the distance between stochastic solutions and the endemic equilibrium of the corresponding deterministic system in the time mean sense is estimated. Based on realistic parameters of influenza A in humans, numerical simulations have been performed to verify/extend our analytical results. It is found that: (i) the deterministic threshold of the influenza A extinction R(S)0 may exist and the threshold parameter will be overestimated in case of neglecting the impaction of environmental noises; (ii) the presence of environmental noises is capable of supporting the irregular recurrence of influenza epidemic, and the average level of the number of infected individuals I(t) always decreases with the increase in noise intensity; and (iii) if R(S)0 > 1, the volatility of I(t) increases with the increase of noise intensity, while the volatility of I(t) decreases with the increase in noise intensity if R(S)0 < 1.
Collapse
Affiliation(s)
- Dan Li
- Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, China
| | - Jing'an Cui
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China
| | - Meng Liu
- School of Mathematical Science, Huaiyin Normal University, Huai'an, 223300, China
| | - Shengqiang Liu
- Academy of Fundamental and Interdisciplinary Sciences, Harbin Institute of Technology, 3041 #, 2 Yi-Kuang Street, Nan-Gang District, Harbin, 150080, China.
| |
Collapse
|
39
|
Neverov AD, Kryazhimskiy S, Plotkin JB, Bazykin GA. Coordinated Evolution of Influenza A Surface Proteins. PLoS Genet 2015; 11:e1005404. [PMID: 26247472 PMCID: PMC4527594 DOI: 10.1371/journal.pgen.1005404] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 06/30/2015] [Indexed: 11/18/2022] Open
Abstract
The surface proteins hemagglutinin (HA) and neuraminidase (NA) of human influenza A virus evolve under selection pressures to escape adaptive immune responses and antiviral drug treatments. In addition to these external selection pressures, some mutations in HA are known to affect the adaptive landscape of NA, and vice versa, because these two proteins are physiologically interlinked. However, the extent to which evolution of one protein affects the evolution of the other one is unknown. Here we develop a novel phylogenetic method for detecting the signatures of such genetic interactions between mutations in different genes – that is, inter-gene epistasis. Using this method, we show that influenza surface proteins evolve in a coordinated way, with mutations in HA affecting subsequent spread of mutations in NA and vice versa, at many sites. Of particular interest is our finding that the oseltamivir-resistance mutations in NA in subtype H1N1 were likely facilitated by prior mutations in HA. Our results illustrate that the adaptive landscape of a viral protein is remarkably sensitive to its genomic context and, more generally, that the evolution of any single protein must be understood within the context of the entire evolving genome. The fitness of an organism depends on the coordinated function of many genes. Thus, how a mutation in one gene affects fitness often depends on what mutations are present in other genes. This dependence is called “genetic interaction” or “epistasis”. The prevalence and type of such interactions are not well understood. Epistasis can be inferred from time-series sequencing data when a mutation in one gene is observed to facilitate the spread of a mutation in another gene. However, the situation is much more complicated when new combinations of genes are formed by processes such as recombination or reassortment. In such cases, deducing the time and order of genetic changes is difficult. Here, we devise a method to infer pairs of mutations in different genes which closely follow one another in the presence of reassortment. We apply it to evolution of two surface proteins of influenza A virus, hemagglutinin and neuraminidase, which are important targets for the human immune system and drugs. We show that mutations in one of these proteins are often facilitated by prior mutations, or compensated by subsequent mutations, in the other protein. In particular, drug-resistance mutations in neuraminidase were likely made possible by prior mutation in hemagglutinin. Knowledge of such interactions is necessary to fully understand and predict evolution.
Collapse
Affiliation(s)
| | - Sergey Kryazhimskiy
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
- FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Joshua B. Plotkin
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Georgii A. Bazykin
- Institute for Information Transmission Problems (Kharkevich Institute) of the Russian Academy of Sciences, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russia
- Pirogov Russian National Research Medical University, Moscow, Russia
- * E-mail:
| |
Collapse
|
40
|
Cherif A. Mathematical analysis of a multiple strain, multi-locus-allele system for antigenically variable infectious diseases revisited. Math Biosci 2015; 267:24-40. [PMID: 26116427 DOI: 10.1016/j.mbs.2015.06.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 06/04/2015] [Accepted: 06/05/2015] [Indexed: 10/23/2022]
Abstract
Many important pathogens such as HIV/AIDS, influenza, malaria, dengue and meningitis generally exist in phenotypically distinct serotypes that compete for hosts. Models used to study these diseases appear as meta-population systems. Herein, we revisit one of the multiple strain models that have been used to investigate the dynamics of infectious diseases with co-circulating serotypes or strains, and provide analytical results underlying the numerical investigations. In particular, we establish the necessary conditions for the local asymptotic stability of the steady states and for the existence of oscillatory behaviors via Hopf bifurcation. In addition, we show that the existence of discrete antigenic forms among pathogens can either fully or partially self-organize, where (i) strains exhibit no strain structures and coexist or (ii) antigenic variants sort into non-overlapping or minimally overlapping clusters that either undergo the principle of competitive exclusion exhibiting discrete strain structures, or co-exist cyclically.
Collapse
Affiliation(s)
- Alhaji Cherif
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, United Kingdom.
| |
Collapse
|
41
|
Schmier S, Mostafa A, Haarmann T, Bannert N, Ziebuhr J, Veljkovic V, Dietrich U, Pleschka S. In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses. Sci Rep 2015; 5:11434. [PMID: 26091504 PMCID: PMC4473683 DOI: 10.1038/srep11434] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 05/27/2015] [Indexed: 12/01/2022] Open
Abstract
Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.
Collapse
Affiliation(s)
- Sonja Schmier
- Georg-Speyer-Haus, Institute for Tumor Biology and Experimental Therapy, Paul-Ehrlich-Str. 42-44, Frankfurt, Germany
| | - Ahmed Mostafa
- Institute of Medical Virology, Justus Liebig University Giessen, Schubertstrasse 81, Giessen, Germany.,Center of Scientific Excellence for Influenza Viruses, National Research Centre (NRC), Dokki, Giza, Egypt
| | - Thomas Haarmann
- Georg-Speyer-Haus, Institute for Tumor Biology and Experimental Therapy, Paul-Ehrlich-Str. 42-44, Frankfurt, Germany
| | - Norbert Bannert
- Robert-Koch-Institute, Division for HIV and other Retroviruses, Nordufer 20, Berlin, Germany
| | - John Ziebuhr
- Institute of Medical Virology, Justus Liebig University Giessen, Schubertstrasse 81, Giessen, Germany
| | - Veljko Veljkovic
- Centre for Multidisciplinary Research, Institute of Nuclear Sciences VINCA, Mihaila Petrovica 14, Belgrade, Serbia
| | - Ursula Dietrich
- Georg-Speyer-Haus, Institute for Tumor Biology and Experimental Therapy, Paul-Ehrlich-Str. 42-44, Frankfurt, Germany
| | - Stephan Pleschka
- Institute of Medical Virology, Justus Liebig University Giessen, Schubertstrasse 81, Giessen, Germany
| |
Collapse
|
42
|
Meyer AG, Wilke CO. Geometric Constraints Dominate the Antigenic Evolution of Influenza H3N2 Hemagglutinin. PLoS Pathog 2015; 11:e1004940. [PMID: 26020774 PMCID: PMC4447415 DOI: 10.1371/journal.ppat.1004940] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 05/07/2015] [Indexed: 11/18/2022] Open
Abstract
We have carried out a comprehensive analysis of the determinants of human influenza A H3 hemagglutinin evolution. We consider three distinct predictors of evolutionary variation at individual sites: solvent accessibility (as a proxy for protein fold stability and/or conservation), Immune Epitope Database (IEDB) epitope sites (as a proxy for host immune bias), and proximity to the receptor-binding region (as a proxy for one of the functions of hemagglutinin-to bind sialic acid). Individually, these quantities explain approximately 15% of the variation in site-wise dN/dS. In combination, solvent accessibility and proximity explain 32% of the variation in dN/dS; incorporating IEDB epitope sites into the model adds only an additional 2 percentage points. Thus, while solvent accessibility and proximity perform largely as independent predictors of evolutionary variation, they each overlap with the epitope-sites predictor. Furthermore, we find that the historical H3 epitope sites, which date back to the 1980s and 1990s, only partially overlap with the experimental sites from the IEDB, and display similar overlap in predictive power when combined with solvent accessibility and proximity. We also find that sites with dN/dS > 1, i.e., the sites most likely driving seasonal immune escape, are not correctly predicted by either historical or IEDB epitope sites, but only by proximity to the receptor-binding region. In summary, a simple geometric model of HA evolution outperforms a model based on epitope sites. These results suggest that either the available epitope sites do not accurately represent the true influenza antigenic sites or that host immune bias may be less important for influenza evolution than commonly thought.
Collapse
MESH Headings
- Antibodies, Viral/immunology
- Antigens, Viral/immunology
- Binding Sites
- Databases, Factual
- Epitope Mapping
- Epitopes/immunology
- Evolution, Molecular
- Genetic Variation/genetics
- Hemagglutinin Glycoproteins, Influenza Virus/chemistry
- Hemagglutinin Glycoproteins, Influenza Virus/genetics
- Hemagglutinin Glycoproteins, Influenza Virus/immunology
- Humans
- Influenza A Virus, H3N2 Subtype/immunology
- Influenza, Human/genetics
- Influenza, Human/immunology
- Influenza, Human/virology
- Protein Folding
- Protein Stability
- Sialic Acids/metabolism
- Solvents/chemistry
Collapse
Affiliation(s)
- Austin G. Meyer
- Department of Integrative Biology, Institute for Cellular and Molecular Biology and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America
- School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, United States of America
| | - Claus O. Wilke
- Department of Integrative Biology, Institute for Cellular and Molecular Biology and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America
| |
Collapse
|
43
|
Kim K, Kim Y. Episodic Nucleotide Substitutions in Seasonal Influenza Virus H3N2 Can Be Explained by Stochastic Genealogical Process without Positive Selection. Mol Biol Evol 2014; 32:704-10. [DOI: 10.1093/molbev/msu332] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
44
|
Neher RA, Russell CA, Shraiman BI. Predicting evolution from the shape of genealogical trees. eLife 2014; 3. [PMID: 25385532 PMCID: PMC4227306 DOI: 10.7554/elife.03568] [Citation(s) in RCA: 119] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 09/30/2014] [Indexed: 12/29/2022] Open
Abstract
Given a sample of genome sequences from an asexual population, can one predict its evolutionary future? Here we demonstrate that the branching patterns of reconstructed genealogical trees contains information about the relative fitness of the sampled sequences and that this information can be used to predict successful strains. Our approach is based on the assumption that evolution proceeds by accumulation of small effect mutations, does not require species specific input and can be applied to any asexual population under persistent selection pressure. We demonstrate its performance using historical data on seasonal influenza A/H3N2 virus. We predict the progenitor lineage of the upcoming influenza season with near optimal performance in 30% of cases and make informative predictions in 16 out of 19 years. Beyond providing a tool for prediction, our ability to make informative predictions implies persistent fitness variation among circulating influenza A/H3N2 viruses. DOI:http://dx.doi.org/10.7554/eLife.03568.001 When viruses multiply, they copy their genetic material to make clones of themselves. However, the genetic material in the clone is often slightly different from the genetic material in the original virus. These mutations can be caused by mistakes made during copying or by radiation or chemicals. Further mutations arise when the clones multiply, which means that, after many generations, there will be quite large differences in the genetic material carried by many members of the population. Most mutations have little or no effect on the ‘fitness’ of an individual - that is, on its ability to survive and multiply - but some mutations do have an influence. Some viruses, like seasonal influenza (flu) viruses, can mutate so rapidly that the most common strains change from year to year. This is why new flu vaccines are needed every year. To date most attempts to predict the evolution of seasonal flu viruses have focused on identifying specific features within the genetic sequences that might indicate fitness. However, such approaches require lots of information about the viruses, and this information is often not available. To address this problem, Neher, Russell and Shraiman have developed a more general method to predict fitness from virus genetic sequences. First, a ‘family tree’ for a virus population - which shows how each strain of the virus is related to other strains - was constructed by comparing the genetic sequences. The next step was based on the observation that as long as differences in fitness arise from the accumulation of multiple mutations, the branching structure of this family tree will bear a visible imprint of the natural selection process as it unfolds. Using this insight and methods borrowed from statistical physics, Neher et al. then analyzed the shape and branching pattern of the tree to work out the fitness of the different strains relative to each other. Neher et al. tested the method using historical influenza A virus data. In 16 of the 19 years studied, the family tree approach made meaningful predictions about which viruses were most likely to give rise to future epidemics. The ability to predict influenza virus evolution from tree shape alone suggests that influenza virus evolution may be more predictable than previously expected. DOI:http://dx.doi.org/10.7554/eLife.03568.002
Collapse
Affiliation(s)
- Richard A Neher
- Evolutionary Dynamics and Biophysics, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Colin A Russell
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Boris I Shraiman
- Kavli Institute for Theoretical Physics, University of California, Santa Barbara, Santa Barbara, United States
| |
Collapse
|
45
|
Alderson RG, Barker D, Mitchell JBO. One origin for metallo-β-lactamase activity, or two? An investigation assessing a diverse set of reconstructed ancestral sequences based on a sample of phylogenetic trees. J Mol Evol 2014; 79:117-29. [PMID: 25185655 PMCID: PMC4185109 DOI: 10.1007/s00239-014-9639-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Accepted: 08/11/2014] [Indexed: 01/04/2023]
Abstract
Bacteria use metallo-β-lactamase enzymes to hydrolyse lactam rings found in many antibiotics, rendering them ineffective. Metallo-β-lactamase activity is thought to be polyphyletic, having arisen on more than one occasion within a single functionally diverse homologous superfamily. Since discovery of multiple origins of enzymatic activity conferring antibiotic resistance has broad implications for the continued clinical use of antibiotics, we test the hypothesis of polyphyly further; if lactamase function has arisen twice independently, the most recent common ancestor (MRCA) is not expected to possess lactam-hydrolysing activity. Two major problems present themselves. Firstly, even with a perfectly known phylogeny, ancestral sequence reconstruction is error prone. Secondly, the phylogeny is not known, and in fact reconstructing a single, unambiguous phylogeny for the superfamily has proven impossible. To obtain a more statistical view of the strength of evidence for or against MRCA lactamase function, we reconstructed a sample of 98 MRCAs of the metallo-β-lactamases, each based on a different tree in a bootstrap sample of reconstructed phylogenies. InterPro sequence signatures and homology modelling were then used to assess our sample of MRCAs for lactamase functionality. Only 5 % of these models conform to our criteria for metallo-β-lactamase functionality, suggesting that the ancestor was unlikely to have been a metallo-β-lactamase. On the other hand, given that ancestral proteins may have had metallo-β-lactamase functionality with variation in sequence and structural properties compared with extant enzymes, our criteria are conservative, estimating a lower bound of evidence for metallo-β-lactamase functionality but not an upper bound.
Collapse
Affiliation(s)
- Rosanna G. Alderson
- Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, KY16 9ST Scotland, UK
| | - Daniel Barker
- Sir Harold Mitchell Building, School of Biology, University of St Andrews, St Andrews, KY16 9TH Scotland, UK
| | - John B. O. Mitchell
- Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, Purdie Building, University of St Andrews, North Haugh, St Andrews, KY16 9ST Scotland, UK
| |
Collapse
|
46
|
Computational prediction of vaccine strains for human influenza A (H3N2) viruses. J Virol 2014; 88:12123-32. [PMID: 25122778 DOI: 10.1128/jvi.01861-14] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. Importance: Human influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.
Collapse
|
47
|
Dynamically correlated mutations drive human Influenza A evolution. Sci Rep 2014; 3:2705. [PMID: 24048220 PMCID: PMC3776956 DOI: 10.1038/srep02705] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 08/22/2013] [Indexed: 12/02/2022] Open
Abstract
Human Influenza A virus undergoes recurrent changes in the hemagglutinin (HA) surface protein, primarily involved in the human antibody recognition. Relevant antigenic changes, enabling the virus to evade host immune response, have been recognized to occur in parallel to multiple mutations at antigenic sites in HA. Yet, the role of correlated mutations (epistasis) in driving the molecular evolution of the virus still represents a challenging puzzle. Further, though circulation at a global geographic level is key for the survival of Influenza A, its role in shaping the viral phylodynamics remains largely unexplored. Here we show, through a sequence based epidemiological model, that epistatic effects between amino acids substitutions, coupled with a reservoir that mimics worldwide circulating viruses, are key determinants that drive human Influenza A evolution. Our approach explains all the up-to-date observations characterizing the evolution of H3N2 subtype, including phylogenetic properties, nucleotide fixation patterns, and composition of antigenic clusters.
Collapse
|
48
|
Abstract
Mounting evidence suggests that natural populations can harbor extensive fitness diversity with numerous genomic loci under selection. It is also known that genealogical trees for populations under selection are quantifiably different from those expected under neutral evolution and described statistically by Kingman's coalescent. While differences in the statistical structure of genealogies have long been used as a test for the presence of selection, the full extent of the information that they contain has not been exploited. Here we demonstrate that the shape of the reconstructed genealogical tree for a moderately large number of random genomic samples taken from a fitness diverse, but otherwise unstructured, asexual population can be used to predict the relative fitness of individuals within the sample. To achieve this we define a heuristic algorithm, which we test in silico, using simulations of a Wright-Fisher model for a realistic range of mutation rates and selection strength. Our inferred fitness ranking is based on a linear discriminator that identifies rapidly coalescing lineages in the reconstructed tree. Inferred fitness ranking correlates strongly with actual fitness, with a genome in the top 10% ranked being in the top 20% fittest with false discovery rate of 0.1-0.3, depending on the mutation/selection parameters. The ranking also enables us to predict the genotypes that future populations inherit from the present one. While the inference accuracy increases monotonically with sample size, samples of 200 nearly saturate the performance. We propose that our approach can be used for inferring relative fitness of genomes obtained in single-cell sequencing of tumors and in monitoring viral outbreaks.
Collapse
|
49
|
Klein EY, Serohijos AWR, Choi JM, Shakhnovich EI, Pekosz A. Influenza A H1N1 pandemic strain evolution--divergence and the potential for antigenic drift variants. PLoS One 2014; 9:e93632. [PMID: 24699432 PMCID: PMC3974778 DOI: 10.1371/journal.pone.0093632] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Accepted: 03/04/2014] [Indexed: 12/19/2022] Open
Abstract
The emergence of a novel A(H1N1) strain in 2009 was the first influenza pandemic of the genomic age, and unprecedented surveillance of the virus provides the opportunity to better understand the evolution of influenza. We examined changes in the nucleotide coding regions and the amino acid sequences of the hemagglutinin (HA), neuraminidase (NA), and nucleoprotein (NP) segments of the A(H1N1)pdm09 strain using publicly available data. We calculated the nucleotide and amino acid hamming distance from the vaccine strain A/California/07/2009 for each sequence. We also estimated Pepitope-a measure of antigenic diversity based on changes in the epitope regions-for each isolate. Finally, we compared our results to A(H3N2) strains collected over the same period. Our analysis found that the mean hamming distance for the HA protein of the A(H1N1)pdm09 strain increased from 3.6 (standard deviation [SD]: 1.3) in 2009 to 11.7 (SD: 1.0) in 2013, while the mean hamming distance in the coding region increased from 7.4 (SD: 2.2) in 2009 to 28.3 (SD: 2.1) in 2013. These trends are broadly similar to the rate of mutation in H3N2 over the same time period. However, in contrast to H3N2 strains, the rate of mutation accumulation has slowed in recent years. Our results are notable because, over the course of the study, mutation rates in H3N2 similar to that seen with A(H1N1)pdm09 led to the emergence of two antigenic drift variants. However, while there has been an H1N1 epidemic in North America this season, evidence to date indicates the vaccine is still effective, suggesting the epidemic is not due to the emergence of an antigenic drift variant. Our results suggest that more research is needed to understand how viral mutations are related to vaccine effectiveness so that future vaccine choices and development can be more predictive.
Collapse
Affiliation(s)
- Eili Y. Klein
- Center for Advanced Modeling in the Social, Behavioral, and Health Sciences, Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
- Center for Disease Dynamics, Economics, and Policy, Washington, DC, United States of America
| | - Adrian W. R. Serohijos
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Jeong-Mo Choi
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Eugene I. Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Andrew Pekosz
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| |
Collapse
|
50
|
He F, Prabakaran M, Rajesh Kumar S, Tan Y, Kwang J. Monovalent H5 vaccine based on epitope-chimeric HA provides broad cross-clade protection against variant H5N1 viruses in mice. Antiviral Res 2014; 105:143-51. [PMID: 24637255 DOI: 10.1016/j.antiviral.2014.03.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 02/27/2014] [Accepted: 03/05/2014] [Indexed: 12/14/2022]
Abstract
H5N1 HPAI virus continues to be a severe threat for public health, as well as for the poultry industry, due to its high mortality and antigenic drift rate. There is no monovalent vaccine available which provides broad protection against those major circulating strains. In the present study, a monovalent H5 vaccine strain was developed with antigenic sequence analysis and epitope mutations. H5 from Indonesia strain (A/Indonesia/CDC669/2006) was used as backbone sequence. Three amino acids were mutated to express immunogenic epitopes from other circulating H5N1s in the backbone. RG influenza virus expressing the epitope-chimeric H5 can react in HI with multiple H5 monoclonal antibodies which fail to neutralize wild type CDC669. High titers in HI and virus neutralization against different clades H5N1s (clade 1, 2, 4 and 7) were detected using sera from mice immunized with the epitope-chimeric H5N1. The monovalent vaccine with RG-epitope-chimeric H5N1 protected mice from lethal challenge with H5N1s of different clades, including clade 1.0, 2.1, 2.2 and 2.3. This study indicates that the broad immune response elicited by this single H5N1 virus allows it to be a promising candidate for a monovalent H5 universal vaccine.
Collapse
Affiliation(s)
- Fang He
- Animal Health Biotechnology, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Mookkan Prabakaran
- Animal Health Biotechnology, Temasek Life Sciences Laboratory, Singapore, Singapore
| | | | - Yunrui Tan
- Animal Health Biotechnology, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Jimmy Kwang
- Animal Health Biotechnology, Temasek Life Sciences Laboratory, Singapore, Singapore; Department of Microbiology Faculty of Medicine, National University of Singapore, Singapore, Singapore.
| |
Collapse
|