1
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Chokkakula S, Oh S, Choi WS, Kim CI, Jeong JH, Kim BK, Park JH, Min SC, Kim EG, Baek YH, Choi YK, Song MS. Mammalian adaptation risk in HPAI H5N8: a comprehensive model bridging experimental data with mathematical insights. Emerg Microbes Infect 2024; 13:2339949. [PMID: 38572657 PMCID: PMC11022924 DOI: 10.1080/22221751.2024.2339949] [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: 11/20/2023] [Accepted: 04/03/2024] [Indexed: 04/05/2024]
Abstract
Understanding the mammalian pathogenesis and interspecies transmission of HPAI H5N8 virus hinges on mapping its adaptive markers. We used deep sequencing to track these markers over five passages in murine lung tissue. Subsequently, we evaluated the growth, selection, and RNA load of eight recombinant viruses with mammalian adaptive markers. By leveraging an integrated non-linear regression model, we quantitatively determined the influence of these markers on growth, adaptation, and RNA expression in mammalian hosts. Furthermore, our findings revealed that the interplay of these markers can lead to synergistic, additive, or antagonistic effects when combined. The elucidation distance method then transformed these results into distinct values, facilitating the derivation of a risk score for each marker. In vivo tests affirmed the accuracy of scores. As more mutations were incorporated, the overall risk score of virus heightened, and the optimal interplay between markers became essential for risk augmentation. Our study provides a robust model to assess risk from adaptive markers of HPAI H5N8, guiding strategies against future influenza threats.
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Affiliation(s)
- Santosh Chokkakula
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Sol Oh
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Won-Suk Choi
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Chang Il Kim
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Ju Hwan Jeong
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Beom Kyu Kim
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Ji-Hyun Park
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Seong Cheol Min
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Eung-Gook Kim
- Department of Biochemistry, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Yun Hee Baek
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
| | - Young Ki Choi
- Center for Study of Emerging and Re-emerging Viruses, Korea Virus Research Institute, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Min-Suk Song
- Department of Microbiology, College of Medicine and Medical Research Institute, Chungbuk National University, Cheongju, Republic of Korea
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2
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Fontdevila Pareta N, Khalili M, Maachi A, Rivarez MPS, Rollin J, Salavert F, Temple C, Aranda MA, Boonham N, Botermans M, Candresse T, Fox A, Hernando Y, Kutnjak D, Marais A, Petter F, Ravnikar M, Selmi I, Tahzima R, Trontin C, Wetzel T, Massart S. Managing the deluge of newly discovered plant viruses and viroids: an optimized scientific and regulatory framework for their characterization and risk analysis. Front Microbiol 2023; 14:1181562. [PMID: 37323908 PMCID: PMC10265641 DOI: 10.3389/fmicb.2023.1181562] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/25/2023] [Indexed: 06/17/2023] Open
Abstract
The advances in high-throughput sequencing (HTS) technologies and bioinformatic tools have provided new opportunities for virus and viroid discovery and diagnostics. Hence, new sequences of viral origin are being discovered and published at a previously unseen rate. Therefore, a collective effort was undertaken to write and propose a framework for prioritizing the biological characterization steps needed after discovering a new plant virus to evaluate its impact at different levels. Even though the proposed approach was widely used, a revision of these guidelines was prepared to consider virus discovery and characterization trends and integrate novel approaches and tools recently published or under development. This updated framework is more adapted to the current rate of virus discovery and provides an improved prioritization for filling knowledge and data gaps. It consists of four distinct steps adapted to include a multi-stakeholder feedback loop. Key improvements include better prioritization and organization of the various steps, earlier data sharing among researchers and involved stakeholders, public database screening, and exploitation of genomic information to predict biological properties.
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Affiliation(s)
| | - Maryam Khalili
- Univ. Bordeaux, INRAE, UMR BFP, Villenave d'Ornon, France
- EGFV, Univ. Bordeaux, INRAE, ISVV, Villenave d’Ornon, France
| | | | - Mark Paul S. Rivarez
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
- College of Agriculture and Agri-Industries, Caraga State University, Butuan, Philippines
| | - Johan Rollin
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
- DNAVision (Belgium), Charleroi, Belgium
| | - Ferran Salavert
- School of Natural and Environmental Sciences, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Coline Temple
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Miguel A. Aranda
- Department of Stress Biology and Plant Pathology, Center for Edaphology and Applied Biology of Segura, Spanish National Research Council (CSIC), Murcia, Spain
| | - Neil Boonham
- School of Natural and Environmental Sciences, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Marleen Botermans
- Netherlands Institute for Vectors, Invasive Plants and Plant Health (NIVIP), Wageningen, Netherlands
| | | | - Adrian Fox
- School of Natural and Environmental Sciences, Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
- Fera Science Ltd, York Biotech Campus, York, United Kingdom
| | | | - Denis Kutnjak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
| | - Armelle Marais
- Univ. Bordeaux, INRAE, UMR BFP, Villenave d'Ornon, France
| | | | - Maja Ravnikar
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
| | - Ilhem Selmi
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Rachid Tahzima
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
- Plant Sciences Unit, Institute for Agricultural, Fisheries and Food Research (ILVO), Merelbeke, Belgium
| | - Charlotte Trontin
- European and Mediterranean Plant Protection Organization, Paris, France
| | - Thierry Wetzel
- DLR Rheinpfalz, Institute of Plant Protection, Neustadt an der Weinstrasse, Germany
| | - Sebastien Massart
- Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
- Bioversity International, Montpellier, France
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3
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Iuchi H, Kawasaki J, Kubo K, Fukunaga T, Hokao K, Yokoyama G, Ichinose A, Suga K, Hamada M. Bioinformatics approaches for unveiling virus-host interactions. Comput Struct Biotechnol J 2023; 21:1774-1784. [PMID: 36874163 PMCID: PMC9969756 DOI: 10.1016/j.csbj.2023.02.044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.
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Affiliation(s)
- Hitoshi Iuchi
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan
| | - Junna Kawasaki
- Faculty of Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Kento Kubo
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Nishi Waseda, Shinjuku-ku, Tokyo 169-0051, Japan
| | - Koki Hokao
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Gentaro Yokoyama
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Akiko Ichinose
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kanta Suga
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Michiaki Hamada
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.,Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan
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4
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Zhang Y, Eskridge KM, Zhang S, Lu G. Identifying host-specific amino acid signatures for influenza A viruses using an adjusted entropy measure. BMC Bioinformatics 2022; 23:333. [PMID: 35962315 PMCID: PMC9372975 DOI: 10.1186/s12859-022-04885-7] [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: 06/14/2021] [Accepted: 08/02/2022] [Indexed: 11/29/2022] Open
Abstract
Background Influenza A viruses (IAV) exhibit vast genetic mutability and have great zoonotic potential to infect avian and mammalian hosts and are known to be responsible for a number of pandemics. A key computational issue in influenza prevention and control is the identification of molecular signatures with cross-species transmission potential. We propose an adjusted entropy-based host-specific signature identification method that uses a similarity coefficient to incorporate the amino acid substitution information and improve the identification performance. Mutations in the polymerase genes (e.g., PB2) are known to play a major role in avian influenza virus adaptation to mammalian hosts. We thus focus on the analysis of PB2 protein sequences and identify host specific PB2 amino acid signatures. Results Validation with a set of H5N1 PB2 sequences from 1996 to 2006 results in adjusted entropy having a 40% false negative discovery rate compared to a 60% false negative rate using unadjusted entropy. Simulations across different levels of sequence divergence show a false negative rate of no higher than 10% while unadjusted entropy ranged from 9 to 100%. In addition, under all levels of divergence adjusted entropy never had a false positive rate higher than 9%. Adjusted entropy also identifies important mutations in H1N1pdm PB2 previously identified in the literature that explain changes in divergence between 2008 and 2009 which unadjusted entropy could not identify. Conclusions Based on these results, adjusted entropy provides a reliable and widely applicable host signature identification approach useful for IAV monitoring and vaccine development.
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Affiliation(s)
- Yixiang Zhang
- Department of Statistics, University of Nebraska - Lincoln, Lincoln, NE, USA
| | - Kent M Eskridge
- Department of Statistics, University of Nebraska - Lincoln, Lincoln, NE, USA.
| | - Shunpu Zhang
- Department of Statistics, University of Central Florida, Orlando, USA
| | - Guoqing Lu
- Department of Biology, University of Nebraska - Omaha, Omaha, NE, USA
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5
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Albery GF, Becker DJ, Brierley L, Brook CE, Christofferson RC, Cohen LE, Dallas TA, Eskew EA, Fagre A, Farrell MJ, Glennon E, Guth S, Joseph MB, Mollentze N, Neely BA, Poisot T, Rasmussen AL, Ryan SJ, Seifert S, Sjodin AR, Sorrell EM, Carlson CJ. The science of the host-virus network. Nat Microbiol 2021; 6:1483-1492. [PMID: 34819645 DOI: 10.1038/s41564-021-00999-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/18/2021] [Indexed: 01/21/2023]
Abstract
Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington DC, USA.
| | - Daniel J Becker
- Department of Biology, University of Oklahoma, Norman, OK, USA
| | - Liam Brierley
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Cara E Brook
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | - Lily E Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad A Dallas
- Department of Biological Sciences, University of South Carolina, Columbia, SC, USA
| | - Evan A Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna Fagre
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Maxwell J Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Emma Glennon
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Sarah Guth
- Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Maxwell B Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, CO, USA
| | - Nardus Mollentze
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.,MRC - University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Benjamin A Neely
- National Institute of Standards and Technology, Charleston, SC, USA
| | - Timothée Poisot
- Québec Centre for Biodiversity Sciences, Montréal, Québec, Canada.,Département de Sciences Biologiques, Université de Montréal, Montréal, Québec, Canada
| | - Angela L Rasmussen
- Vaccine and Infectious Disease Organization, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.,Department of Biochemistry, Microbiology, and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Sadie J Ryan
- Department of Geography, University of Florida, Gainesville, FL, USA.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.,School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Stephanie Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Anna R Sjodin
- Department of Biological Sciences, University of Idaho, Moscow, ID, USA
| | - Erin M Sorrell
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA.,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA
| | - Colin J Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC, USA. .,Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA.
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6
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Carlson CJ, Farrell MJ, Grange Z, Han BA, Mollentze N, Phelan AL, Rasmussen AL, Albery GF, Bett B, Brett-Major DM, Cohen LE, Dallas T, Eskew EA, Fagre AC, Forbes KM, Gibb R, Halabi S, Hammer CC, Katz R, Kindrachuk J, Muylaert RL, Nutter FB, Ogola J, Olival KJ, Rourke M, Ryan SJ, Ross N, Seifert SN, Sironen T, Standley CJ, Taylor K, Venter M, Webala PW. The future of zoonotic risk prediction. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200358. [PMID: 34538140 PMCID: PMC8450624 DOI: 10.1098/rstb.2020.0358] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2021] [Indexed: 01/26/2023] Open
Abstract
In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Colin J. Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Maxwell J. Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Zoe Grange
- Public Health Scotland, Glasgow G2 6QE, UK
| | - Barbara A. Han
- Cary Institute of Ecosystem Studies, Millbrook, NY 12545, USA
| | - Nardus Mollentze
- Medical Research Council, University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Alexandra L. Phelan
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- O'Neill Institute for National and Global Health Law, Georgetown University Law Center, Washington, DC 20001, USA
| | - Angela L. Rasmussen
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Gregory F. Albery
- Department of Biology, Georgetown University, Washington, DC 20007, USA
| | - Bernard Bett
- Animal and Human Health Program, International Livestock Research Institute, PO Box 30709-00100, Nairobi, Kenya
| | - David M. Brett-Major
- Department of Epidemiology, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
| | - Lily E. Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad Dallas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70806, USA
| | - Evan A. Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna C. Fagre
- Department of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
| | - Kristian M. Forbes
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Rory Gibb
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sam Halabi
- O'Neill Institute for National and Global Health Law, Georgetown University Law Center, Washington, DC 20001, USA
| | - Charlotte C. Hammer
- Centre for the Study of Existential Risk, University of Cambridge, Cambridge, UK
| | - Rebecca Katz
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Jason Kindrachuk
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada R3E 0J9
| | - Renata L. Muylaert
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
| | - Felicia B. Nutter
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 01536, USA
- Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | | | | | - Michelle Rourke
- Law Futures Centre, Griffith Law School, Griffith University, Nathan, Queensland 4111, Australia
| | - Sadie J. Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Noam Ross
- EcoHealth Alliance, New York, NY 10018, USA
| | - Stephanie N. Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Tarja Sironen
- Department of Virology, University of Helsinki, Helsinki, Finland
- Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland
| | - Claire J. Standley
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Kishana Taylor
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Marietjie Venter
- Zoonotic Arbo and Respiratory Virus Program, Centre for Viral Zoonoses, Department of Medical Virology, University of Pretoria, Pretoria, South Africa
| | - Paul W. Webala
- Department of Forestry and Wildlife Management, Maasai Mara University, Narok 20500, Kenya
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7
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Borkenhagen LK, Allen MW, Runstadler JA. Influenza virus genotype to phenotype predictions through machine learning: a systematic review. Emerg Microbes Infect 2021; 10:1896-1907. [PMID: 34498543 PMCID: PMC8462836 DOI: 10.1080/22221751.2021.1978824] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. As the body of research where these techniques are applied for influenza A virus phenotype prediction continues to grow, it is useful to consider the strengths and weaknesses of these approaches to understand what has prevented these models from seeing widespread use by surveillance laboratories and to identify gaps that are underexplored with this technology. Methods and Results: We present a systematic review of English literature published through 15 April 2021 of studies employing machine learning methods to generate predictions of influenza A virus phenotypes from genomic or proteomic input. Forty-nine studies were included in this review, spanning the topics of host discrimination, human adaptability, subtype and clade assignment, pandemic lineage assignment, characteristics of infection, and antiviral drug resistance. Conclusions: Our findings suggest that biases in model design and a dearth of wet laboratory follow-up may explain why these models often go underused. We, therefore, offer guidance to overcome these limitations, aid in improving predictive models of previously studied influenza A virus phenotypes, and extend those models to unexplored phenotypes in the ultimate pursuit of tools to enable the characterization of virus isolates across surveillance laboratories.
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Affiliation(s)
- Laura K Borkenhagen
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA
| | - Martin W Allen
- Department of Computer Science, School of Engineering, Tufts University, Medford, MA, USA
| | - Jonathan A Runstadler
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA
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8
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Avian Influenza in Wild Birds and Poultry: Dissemination Pathways, Monitoring Methods, and Virus Ecology. Pathogens 2021; 10:pathogens10050630. [PMID: 34065291 PMCID: PMC8161317 DOI: 10.3390/pathogens10050630] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/13/2021] [Accepted: 05/14/2021] [Indexed: 12/21/2022] Open
Abstract
Avian influenza is one of the largest known threats to domestic poultry. Influenza outbreaks on poultry farms typically lead to the complete slaughter of the entire domestic bird population, causing severe economic losses worldwide. Moreover, there are highly pathogenic avian influenza (HPAI) strains that are able to infect the swine or human population in addition to their primary avian host and, as such, have the potential of being a global zoonotic and pandemic threat. Migratory birds, especially waterfowl, are a natural reservoir of the avian influenza virus; they carry and exchange different virus strains along their migration routes, leading to antigenic drift and antigenic shift, which results in the emergence of novel HPAI viruses. This requires monitoring over time and in different locations to allow for the upkeep of relevant knowledge on avian influenza virus evolution and the prevention of novel epizootic and epidemic outbreaks. In this review, we assess the role of migratory birds in the spread and introduction of influenza strains on a global level, based on recent data. Our analysis sheds light on the details of viral dissemination linked to avian migration, the viral exchange between migratory waterfowl and domestic poultry, virus ecology in general, and viral evolution as a process tightly linked to bird migration. We also provide insight into methods used to detect and quantify avian influenza in the wild. This review may be beneficial for the influenza research community and may pave the way to novel strategies of avian influenza and HPAI zoonosis outbreak monitoring and prevention.
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9
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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10
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Li J, Zhang S, Li B, Hu Y, Kang XP, Wu XY, Huang MT, Li YC, Zhao ZP, Qin CF, Jiang T. Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions. Mol Biol Evol 2021; 37:1224-1236. [PMID: 31750915 PMCID: PMC7086167 DOI: 10.1093/molbev/msz276] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs involved in the next pandemic are currently unpredictable. We aim to build machine learning (ML) models to predict human-adaptive IAV nucleotide composition. A total of 217,549 IAV full-length coding sequences of the PB2 (polymerase basic protein-2), PB1, PA (polymerase acidic protein), HA (hemagglutinin), NP (nucleoprotein), and NA (neuraminidase) segments were decomposed for their codon position-based mononucleotides (12 nts) and dinucleotides (48 dnts). A total of 68,742 human sequences and 68,739 avian sequences (1:1) were resampled to characterize the human adaptation-associated (d)nts with principal component analysis (PCA) and other ML models. Then, the human adaptation of IAV sequences was predicted based on the characterized (d)nts. Respectively, 9, 12, 11, 13, 10 and 9 human-adaptive (d)nts were optimized for the six segments. PCA and hierarchical clustering analysis revealed the linear separability of the optimized (d)nts between the human-adaptive and avian-adaptive sets. The results of the confusion matrix and the area under the receiver operating characteristic curve indicated a high performance of the ML models to predict human adaptation of IAVs. Our model performed well in predicting the human adaptation of the swine/avian IAVs before and after the 2009 H1N1 pandemic. In conclusion, we identified the human adaptation-associated genomic composition of IAV segments. ML models for IAV human adaptation prediction using large IAV genomic data sets can facilitate the identification of key viral factors that affect virus transmission/pathogenicity. Most importantly, it allows the prediction of pandemic influenza.
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Affiliation(s)
- Jing Li
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Sen Zhang
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Bo Li
- Department of Clinical Laboratory, the Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China
| | - Yi Hu
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xiao-Ping Kang
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xiao-Yan Wu
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Meng-Ting Huang
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.,Graduate School, Anhui Medical University, Hefei, China
| | - Yu-Chang Li
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Zhong-Peng Zhao
- Department of Infection and Immunology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Cheng-Feng Qin
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Tao Jiang
- Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.,Graduate School, Anhui Medical University, Hefei, China
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11
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Ghabeshi S, Ebrahimie E, Salimi V, Ghanizadeh A, Khodakhah F, Yavarian J, Norouzbabaei Z, Sasani F, Rezaie F, Azad TM. Experimental direct-contact transmission of influenza A/H9N2 virus in the guinea pig model in Iran. Future Virol 2020. [DOI: 10.2217/fvl-2019-0141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Aim: The present study aims to evaluate risk factors for the transmission of A/H9N2 viruses in guinea pig model. Materials & methods: Lung tissue samples were collected from the chicken clinically infected with influenza A/H9N2 virus in 2018. Next, virus isolation and titration, as well as reverse transcription PCR were performed. Then, hemagglutnation and neuraminidase genes was sequenced to identify different positions (hotspots) involved in transmission and host adaptation. Results: Influenza A/H9N2 virus could replicate in low titers in the nasal turbinate and transmit from infected to noninfected guinea pigs. Conclusion: Hotspots on the surface glycoproteins had the potential to alter transmission properties in the new host.
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Affiliation(s)
- Soad Ghabeshi
- Virology Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Ebrahimie
- School of Animal and VeterinarySciences, The University of Adelaide, South Australia, Adelaide, Australia
- Genomics Research Platform, Schoolof Life Sciences, La Trobe University, Melbourne, Victoria, Australia
| | - Vahid Salimi
- Virology Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Arash Ghanizadeh
- Department of Biotechnology, Razi Vaccine & Serum Research Institute, Karaj, Alborz, Iran
| | - Farshad Khodakhah
- Virology Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Jila Yavarian
- Virology Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Norouzbabaei
- Virology Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Farhang Sasani
- Department of Pathology, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Farhad Rezaie
- Virology Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Talat Mokhtari Azad
- Virology Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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12
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Yin R, Zhou X, Rashid S, Kwoh CK. HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC Med Genomics 2020; 13:9. [PMID: 31973709 PMCID: PMC6979075 DOI: 10.1186/s12920-019-0656-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 12/26/2019] [Indexed: 12/29/2022] Open
Abstract
Background Influenza reassortment, a mechanism where influenza viruses exchange their RNA segments by co-infecting a single cell, has been implicated in several major pandemics since 19th century. Owing to the significant impact on public health and social stability, great attention has been received on the identification of influenza reassortment. Methods We proposed a novel computational method named HopPER (Host-prediction-based Probability Estimation of Reassortment), that sturdily estimates reassortment probabilities through host tropism prediction using 147 new features generated from seven physicochemical properties of amino acids. We conducted the experiments on a range of real and synthetic datasets and compared HopPER with several state-of-the-art methods. Results It is shown that 280 out of 318 candidate reassortants have been successfully identified. Additionally, not only can HopPER be applied to complete genomes but its effectiveness on incomplete genomes is also demonstrated. The analysis of evolutionary success of avian, human and swine viruses generated through reassortment across different years using HopPER further revealed the reassortment history of the influenza viruses. Conclusions Our study presents a novel method for the prediction of influenza reassortment. We hope this method could facilitate rapid reassortment detection and provide novel insights into the evolutionary patterns of influenza viruses.
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Affiliation(s)
- Rui Yin
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
| | - Xinrui Zhou
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Shamima Rashid
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
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13
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Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data. Sci Rep 2019; 9:4049. [PMID: 30858412 PMCID: PMC6411942 DOI: 10.1038/s41598-019-40346-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 02/04/2019] [Indexed: 12/12/2022] Open
Abstract
Streptococcus pneumoniae, a normal commensal of the upper respiratory tract, is a major public health concern, responsible for substantial global morbidity and mortality due to pneumonia, meningitis and sepsis. Why some pneumococci invade the bloodstream or CSF (so-called invasive pneumococcal disease; IPD) is uncertain. In this study we identify genes associated with IPD. We transform whole genome sequence (WGS) data into a sequence typing scheme, while avoiding the caveat of using an arbitrary genome as a reference by substituting it with a constructed pangenome. We then employ a random forest machine-learning algorithm on the transformed data, and find 43 genes consistently associated with IPD across three geographically distinct WGS data sets of pneumococcal carriage isolates. Of the genes we identified as associated with IPD, we find 23 genes previously shown to be directly relevant to IPD, as well as 18 uncharacterized genes. We suggest that these uncharacterized genes identified by us are also likely to be relevant for IPD.
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14
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Bagdonaite I, Vakhrushev SY, Joshi HJ, Wandall HH. Viral glycoproteomes: technologies for characterization and outlook for vaccine design. FEBS Lett 2018; 592:3898-3920. [PMID: 29961944 DOI: 10.1002/1873-3468.13177] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 06/13/2018] [Accepted: 06/26/2018] [Indexed: 12/27/2022]
Abstract
It has long been known that surface proteins of most enveloped viruses are covered with glycans. It has furthermore been demonstrated that glycosylation is essential for propagation and immune evasion for many viruses. The recent development of high-resolution mass spectrometry techniques has enabled identification not only of the precise structures but also the positions of such post-translational modifications on viruses, revealing substantial differences in extent of glycosylation and glycan maturation for different classes of viruses. In-depth characterization of glycosylation and other post-translational modifications of viral envelope glycoproteins is essential for rational design of vaccines and antivirals. In this Review, we provide an overview of techniques used to address viral glycosylation and summarize information on glycosylation of enveloped viruses representing ongoing public health challenges. Furthermore, we discuss how knowledge on glycosylation can be translated to means to prevent and combat viral infections.
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Affiliation(s)
- Ieva Bagdonaite
- Department of Cellular and Molecular Medicine, Copenhagen Center for Glycomics, University of Copenhagen, Denmark
| | - Sergey Y Vakhrushev
- Department of Cellular and Molecular Medicine, Copenhagen Center for Glycomics, University of Copenhagen, Denmark
| | - Hiren J Joshi
- Department of Cellular and Molecular Medicine, Copenhagen Center for Glycomics, University of Copenhagen, Denmark
| | - Hans H Wandall
- Department of Cellular and Molecular Medicine, Copenhagen Center for Glycomics, University of Copenhagen, Denmark
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15
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Lau SKP. Molecular Research on Emerging Viruses: Evolution, Diagnostics, Pathogenesis, and Therapeutics. Int J Mol Sci 2018; 19:ijms19020398. [PMID: 29385690 PMCID: PMC5855620 DOI: 10.3390/ijms19020398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 01/17/2018] [Accepted: 01/26/2018] [Indexed: 11/16/2022] Open
Affiliation(s)
- Susanna K P Lau
- State Key Laboratory of Emerging Infectious Diseases, Hong Kong, China.
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
- Carol Yu Centre for Infection, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
- Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
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Natural Reassortants of Potentially Zoonotic Avian Influenza Viruses H5N1 and H9N2 from Egypt Display Distinct Pathogenic Phenotypes in Experimentally Infected Chickens and Ferrets. J Virol 2017; 91:JVI.01300-17. [PMID: 28931674 DOI: 10.1128/jvi.01300-17] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 09/11/2017] [Indexed: 12/22/2022] Open
Abstract
The cocirculation of zoonotic highly pathogenic avian influenza virus (HPAIV) of subtype H5N1 and avian influenza virus (AIV) of subtype H9N2 among poultry in Egypt for at least 6 years should render that country a hypothetical hot spot for the emergence of reassortant, phenotypically altered viruses, yet no reassortants have been detected in Egypt. The present investigations proved that reassortants of the Egyptian H5N1 clade 2.2.1.2 virus and H9N2 virus of the G1-B lineage can be generated by coamplification in embryonated chicken eggs. Reassortants were restricted to the H5N1 subtype and acquired between two and all six of the internal segments of the H9N2 virus. Five selected plaque-purified reassortant clones expressed a broad phenotypic spectrum both in vitro and in vivo Two groups of reassortants were characterized to have retarded growth characteristics in vitro compared to the H5N1 parent virus. One clone provoked reduced mortality in inoculated chickens, although the characteristics of a highly pathogenic phenotype were retained. Enhanced zoonotic properties were not predicted for any of these clones, and this prediction was confirmed by ferret inoculation experiments: neither the H5N1 parent virus nor two selected clones induced severe clinical symptoms or were transmitted to sentinel ferrets by contact. While the emergence of reassortants of Egyptian HPAIV of subtype H5N1 with internal gene segments of cocirculating H9N2 viruses is possible in principle, the spread of such viruses is expected to be governed by their fitness to outcompete the parental viruses in the field. The eventual spread of attenuated phenotypes, however, would negatively impact syndrome surveillance on poultry farms and might foster enzootic virus circulation.IMPORTANCE Despite almost 6 years of the continuous cocirculation of highly pathogenic avian influenza virus H5N1 and avian influenza virus H9N2 in poultry in Egypt, no reassortants of the two subtypes have been reported. Here, the principal compatibility of the two subtypes is shown by forcing the reassortment between copassaged H5N1 und H9N2 viruses in embryonated chicken eggs. The resulting reassortant viruses displayed a wide range of pathogenicity including attenuated phenotypes in chickens, but did not show enhanced zoonotic propensities in the ferret model.
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