MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs
Seasonal Influenza H3N2 virus poses a great threat to public health, but its vaccine efficacy remains suboptimal. One critical step in influenza vaccine production is the viral passage in embryonated eggs. Recently, the strength of egg passage adaptation was found to be rapidly increasing with time...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Vaccines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-393X/10/6/907 |
| _version_ | 1797481666318434304 |
|---|---|
| author | Hui Chen Junqiu Wang Yunsong Liu Ivy Quek Ee Ling Chih Chuan Shih Dafei Wu Zhiyan Fu Raphael Tze Chuen Lee Miao Xu Vincent T. Chow Sebastian Maurer-Stroh Da Zhou Jianjun Liu Weiwei Zhai |
| author_facet | Hui Chen Junqiu Wang Yunsong Liu Ivy Quek Ee Ling Chih Chuan Shih Dafei Wu Zhiyan Fu Raphael Tze Chuen Lee Miao Xu Vincent T. Chow Sebastian Maurer-Stroh Da Zhou Jianjun Liu Weiwei Zhai |
| author_sort | Hui Chen |
| collection | DOAJ |
| description | Seasonal Influenza H3N2 virus poses a great threat to public health, but its vaccine efficacy remains suboptimal. One critical step in influenza vaccine production is the viral passage in embryonated eggs. Recently, the strength of egg passage adaptation was found to be rapidly increasing with time driven by convergent evolution at a set of functionally important codons in the hemagglutinin (HA1). In this study, we aim to take advantage of the negative correlation between egg passage adaptation and vaccine effectiveness (VE) and develop a computational tool for selecting the best candidate vaccine virus (CVV) for vaccine production. Using a probabilistic approach known as mutational mapping, we characterized the pattern of sequence evolution driven by egg passage adaptation and developed a new metric known as the adaptive distance (AD) which measures the overall strength of egg passage adaptation. We found that AD is negatively correlated with the influenza H3N2 vaccine effectiveness (VE) and ~75% of the variability in VE can be explained by AD. Based on these findings, we developed a computational package that can Measure the Adaptive Distance and predict vaccine Effectiveness (MADE). MADE provides a powerful tool for the community to calibrate the effect of egg passage adaptation and select more reliable strains with minimum egg-passaged changes as the seasonal A/H3N2 influenza vaccine. |
| first_indexed | 2024-03-09T22:17:48Z |
| format | Article |
| id | doaj.art-fd2e38b07daa4f109e6c7c72bc9c41e8 |
| institution | Directory Open Access Journal |
| issn | 2076-393X |
| language | English |
| last_indexed | 2024-03-09T22:17:48Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vaccines |
| spelling | doaj.art-fd2e38b07daa4f109e6c7c72bc9c41e82023-11-23T19:21:14ZengMDPI AGVaccines2076-393X2022-06-0110690710.3390/vaccines10060907MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated EggsHui Chen0Junqiu Wang1Yunsong Liu2Ivy Quek Ee Ling3Chih Chuan Shih4Dafei Wu5Zhiyan Fu6Raphael Tze Chuen Lee7Miao Xu8Vincent T. Chow9Sebastian Maurer-Stroh10Da Zhou11Jianjun Liu12Weiwei Zhai13Human Genomics, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, SingaporeKey Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, ChinaBioinformatics Core, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, SingaporeBioinformatics Core, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, SingaporeKey Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, ChinaIHiS—Integrated Health Information Systems, Singapore 554910, SingaporeBioinformatics Institute, Agency for Science, Technology and Research, Singapore 138671, SingaporeState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou 510060, ChinaNUHS Infectious Diseases Translational Research Program, Department of Microbiology & Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117545, SingaporeBioinformatics Institute, Agency for Science, Technology and Research, Singapore 138671, SingaporeSchool of Mathematical Science, Xiamen University, Xiamen 361005, ChinaHuman Genomics, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, SingaporeHuman Genomics, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, SingaporeSeasonal Influenza H3N2 virus poses a great threat to public health, but its vaccine efficacy remains suboptimal. One critical step in influenza vaccine production is the viral passage in embryonated eggs. Recently, the strength of egg passage adaptation was found to be rapidly increasing with time driven by convergent evolution at a set of functionally important codons in the hemagglutinin (HA1). In this study, we aim to take advantage of the negative correlation between egg passage adaptation and vaccine effectiveness (VE) and develop a computational tool for selecting the best candidate vaccine virus (CVV) for vaccine production. Using a probabilistic approach known as mutational mapping, we characterized the pattern of sequence evolution driven by egg passage adaptation and developed a new metric known as the adaptive distance (AD) which measures the overall strength of egg passage adaptation. We found that AD is negatively correlated with the influenza H3N2 vaccine effectiveness (VE) and ~75% of the variability in VE can be explained by AD. Based on these findings, we developed a computational package that can Measure the Adaptive Distance and predict vaccine Effectiveness (MADE). MADE provides a powerful tool for the community to calibrate the effect of egg passage adaptation and select more reliable strains with minimum egg-passaged changes as the seasonal A/H3N2 influenza vaccine.https://www.mdpi.com/2076-393X/10/6/907egg passage adaptationvaccine effectivenessinfluenza H3N2 virusadaptive evolutionvaccine production |
| spellingShingle | Hui Chen Junqiu Wang Yunsong Liu Ivy Quek Ee Ling Chih Chuan Shih Dafei Wu Zhiyan Fu Raphael Tze Chuen Lee Miao Xu Vincent T. Chow Sebastian Maurer-Stroh Da Zhou Jianjun Liu Weiwei Zhai MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs Vaccines egg passage adaptation vaccine effectiveness influenza H3N2 virus adaptive evolution vaccine production |
| title | MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs |
| title_full | MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs |
| title_fullStr | MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs |
| title_full_unstemmed | MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs |
| title_short | MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs |
| title_sort | made a computational tool for predicting vaccine effectiveness for the influenza a h3n2 virus adapted to embryonated eggs |
| topic | egg passage adaptation vaccine effectiveness influenza H3N2 virus adaptive evolution vaccine production |
| url | https://www.mdpi.com/2076-393X/10/6/907 |
| work_keys_str_mv | AT huichen madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT junqiuwang madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT yunsongliu madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT ivyquekeeling madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT chihchuanshih madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT dafeiwu madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT zhiyanfu madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT raphaeltzechuenlee madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT miaoxu madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT vincenttchow madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT sebastianmaurerstroh madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT dazhou madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT jianjunliu madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs AT weiweizhai madeacomputationaltoolforpredictingvaccineeffectivenessfortheinfluenzaah3n2virusadaptedtoembryonatedeggs |