MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production

Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, re...

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Published inNature communications Vol. 15; no. 1; p. 1128
Main Authors Gao, Cheng, Wen, Feng, Guan, Minhui, Hatuwal, Bijaya, Li, Lei, Praena, Beatriz, Tang, Cynthia Y., Zhang, Jieze, Luo, Feng, Xie, Hang, Webby, Richard, Tao, Yizhi Jane, Wan, Xiu-Feng
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 06.02.2024
Nature Publishing Group
Nature Portfolio
Subjects
101/1
631/114/1305
631/114/2397
631/114/469
631/61/24/590
692/699/255/1578
Antigenicity
Health risks
Humanities and Social Sciences
Influenza
Learning algorithms
Machine learning
multidisciplinary
Public health
Science
Science (multidisciplinary)
Vaccine efficacy
Vaccines
Viruses
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Abstract Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines. Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses. Here, authors introduce MAIVeSS, a machine learning assisted framework to streamline vaccine seed virus selection using genomic sequence, expediting seasonal flu vaccine production and supply.
AbstractList Abstract Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.
Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.
Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines. Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses. Here, authors introduce MAIVeSS, a machine learning assisted framework to streamline vaccine seed virus selection using genomic sequence, expediting seasonal flu vaccine production and supply.
Abstract Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.
Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed viruses with matched antigenicity between vaccine antigen and circulating viruses and with high yield underscore vaccine efficacy and supply, respectively. Current methods for selecting influenza seed vaccines are labor intensive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccine strains directly from clinical samples by using molecular signatures of antigenicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine candidates and experimentally confirm that these candidates have optimal antigenicity and growth in cells and eggs. Our framework can potentially reduce the optimal vaccine candidate selection time from months to days and thus facilitate timely supply of seasonal vaccines.Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses. Here, authors introduce MAIVeSS, a machine learning assisted framework to streamline vaccine seed virus selection using genomic sequence, expediting seasonal flu vaccine production and supply.
ArticleNumber 1128
Author Tao, Yizhi Jane
Hatuwal, Bijaya
Xie, Hang
Gao, Cheng
Wen, Feng
Zhang, Jieze
Praena, Beatriz
Luo, Feng
Webby, Richard
Guan, Minhui
Wan, Xiu-Feng
Li, Lei
Tang, Cynthia Y.
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  surname: Gao
  fullname: Gao, Cheng
  organization: Center for Influenza and Emerging Infectious Diseases, University of Missouri, Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Bond Life Sciences Center, University of Missouri
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  givenname: Feng
  surname: Wen
  fullname: Wen, Feng
  organization: Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University
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  surname: Guan
  fullname: Guan, Minhui
  organization: Center for Influenza and Emerging Infectious Diseases, University of Missouri, Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Bond Life Sciences Center, University of Missouri
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  surname: Hatuwal
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  organization: Center for Influenza and Emerging Infectious Diseases, University of Missouri, Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Bond Life Sciences Center, University of Missouri
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  givenname: Lei
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  organization: Department of Chemistry, Georgia State University, Center for Diagnostics & Therapeutics, Georgia State University
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  surname: Praena
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  organization: Center for Influenza and Emerging Infectious Diseases, University of Missouri, Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Bond Life Sciences Center, University of Missouri
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  givenname: Cynthia Y.
  orcidid: 0000-0001-5272-7170
  surname: Tang
  fullname: Tang, Cynthia Y.
  organization: Center for Influenza and Emerging Infectious Diseases, University of Missouri, Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Bond Life Sciences Center, University of Missouri, Institute for Data Science and Informatics, University of Missouri
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  givenname: Jieze
  orcidid: 0000-0001-8084-6310
  surname: Zhang
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  organization: Department of Bioengineering, Rice University
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  givenname: Feng
  orcidid: 0000-0002-4813-2403
  surname: Luo
  fullname: Luo, Feng
  organization: University School of Computing, Clemson University
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  givenname: Hang
  orcidid: 0000-0001-8318-5554
  surname: Xie
  fullname: Xie, Hang
  organization: Laboratory of Respiratory Viral Diseases, Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, US Food and Drug Administration
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  organization: Department of Infectious Diseases, St. Jude Children’s Research Hospital
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  givenname: Yizhi Jane
  surname: Tao
  fullname: Tao, Yizhi Jane
  organization: Department of BioSciences, Rice University
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  surname: Wan
  fullname: Wan, Xiu-Feng
  email: wanx@missouri.edu
  organization: Center for Influenza and Emerging Infectious Diseases, University of Missouri, Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Bond Life Sciences Center, University of Missouri, Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Institute for Data Science and Informatics, University of Missouri
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Snippet Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal seed...
Abstract Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal...
Abstract Vaccines are the main pharmaceutical intervention used against the global public health threat posed by influenza viruses. Timely selection of optimal...
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SubjectTerms 101/1
631/114/1305
631/114/2397
631/114/469
631/61/24/590
692/699/255/1578
Antigenicity
Health risks
Humanities and Social Sciences
Influenza
Learning algorithms
Machine learning
multidisciplinary
Public health
Science
Science (multidisciplinary)
Vaccine efficacy
Vaccines
Viruses
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Title MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production
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Volume 15
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