tetano
Editor, Senior Moderator
Nat Commun
. 2024 Feb 6;15(1):1128.
doi: 10.1038/s41467-024-45145-x. MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production
Cheng Gao[SUP] #[/SUP][SUP] 1 2 3 4 [/SUP], Feng Wen[SUP] #[/SUP][SUP] 5 [/SUP], Minhui Guan[SUP] 1 3 4 [/SUP], Bijaya Hatuwal[SUP] 1 2 3 4 [/SUP], Lei Li[SUP] 6 7 [/SUP], Beatriz Praena[SUP] 1 3 4 [/SUP], Cynthia Y Tang[SUP] 1 3 4 8 [/SUP], Jieze Zhang[SUP] 9 [/SUP], Feng Luo[SUP] 10 [/SUP], Hang Xie[SUP] 11 [/SUP], Richard Webby[SUP] 12 [/SUP], Yizhi Jane Tao[SUP] 13 [/SUP], Xiu-Feng Wan[SUP] 14 15 16 17 18 19 [/SUP]
Affiliations
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.
. 2024 Feb 6;15(1):1128.
doi: 10.1038/s41467-024-45145-x. MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production
Cheng Gao[SUP] #[/SUP][SUP] 1 2 3 4 [/SUP], Feng Wen[SUP] #[/SUP][SUP] 5 [/SUP], Minhui Guan[SUP] 1 3 4 [/SUP], Bijaya Hatuwal[SUP] 1 2 3 4 [/SUP], Lei Li[SUP] 6 7 [/SUP], Beatriz Praena[SUP] 1 3 4 [/SUP], Cynthia Y Tang[SUP] 1 3 4 8 [/SUP], Jieze Zhang[SUP] 9 [/SUP], Feng Luo[SUP] 10 [/SUP], Hang Xie[SUP] 11 [/SUP], Richard Webby[SUP] 12 [/SUP], Yizhi Jane Tao[SUP] 13 [/SUP], Xiu-Feng Wan[SUP] 14 15 16 17 18 19 [/SUP]
Affiliations
- PMID: 38321021
- DOI: 10.1038/s41467-024-45145-x
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.