tetano
Editor, Senior Moderator
J Med Virol
. 2024 May;96(5):e29657.
doi: 10.1002/jmv.29657. Characterizing the antigenic evolution of pandemic influenza A (H1N1) pdm09 from 2009 to 2023
Peiwen Cheng[SUP] 1 2 [/SUP], Ke Zhai[SUP] 1 2 [/SUP], Wenjie Han[SUP] 1 2 [/SUP], Jinfeng Zeng[SUP] 1 2 [/SUP], Zekai Qiu[SUP] 1 2 [/SUP], Yilin Chen[SUP] 1 2 [/SUP], Kang Tang[SUP] 3 [/SUP], Jing Tang[SUP] 1 2 [/SUP], Haoyu Long[SUP] 1 2 [/SUP], Taijiao Jiang[SUP] 4 5 6 [/SUP], Xiangjun Du[SUP] 1 2 7 8 [/SUP]
Affiliations
The H1N1pdm09 virus has been a persistent threat to public health since the 2009 pandemic. Particularly, since the relaxation of COVID-19 pandemic mitigation measures, the influenza virus and SARS-CoV-2 have been concurrently prevalent worldwide. To determine the antigenic evolution pattern of H1N1pdm09 and develop preventive countermeasures, we collected influenza sequence data and immunological data to establish a new antigenic evolution analysis framework. A machine learning model (XGBoost, accuracy = 0.86, area under the receiver operating characteristic curve = 0.89) was constructed using epitopes, physicochemical properties, receptor binding sites, and glycosylation sites as features to predict the antigenic similarity relationships between influenza strains. An antigenic correlation network was constructed, and the Markov clustering algorithm was used to identify antigenic clusters. Subsequently, the antigenic evolution pattern of H1N1pdm09 was analyzed at the global and regional scales across three continents. We found that H1N1pdm09 evolved into around five antigenic clusters between 2009 and 2023 and that their antigenic evolution trajectories were characterized by cocirculation of multiple clusters, low-level persistence of former dominant clusters, and local heterogeneity of cluster circulations. Furthermore, compared with the seasonal H1N1 virus, the potential cluster-transition determining sites of H1N1pdm09 were restricted to epitopes Sa and Sb. This study demonstrated the effectiveness of machine learning methods for characterizing antigenic evolution of viruses, developed a specific model to rapidly identify H1N1pdm09 antigenic variants, and elucidated their evolutionary patterns. Our findings may provide valuable support for the implementation of effective surveillance strategies and targeted prevention efforts to mitigate the impact of H1N1pdm09.
Keywords: H1N1pdm09; XGBoost; antigenic correlation network; evolutionary patterns; potential cluster‐transition determining sites.
. 2024 May;96(5):e29657.
doi: 10.1002/jmv.29657. Characterizing the antigenic evolution of pandemic influenza A (H1N1) pdm09 from 2009 to 2023
Peiwen Cheng[SUP] 1 2 [/SUP], Ke Zhai[SUP] 1 2 [/SUP], Wenjie Han[SUP] 1 2 [/SUP], Jinfeng Zeng[SUP] 1 2 [/SUP], Zekai Qiu[SUP] 1 2 [/SUP], Yilin Chen[SUP] 1 2 [/SUP], Kang Tang[SUP] 3 [/SUP], Jing Tang[SUP] 1 2 [/SUP], Haoyu Long[SUP] 1 2 [/SUP], Taijiao Jiang[SUP] 4 5 6 [/SUP], Xiangjun Du[SUP] 1 2 7 8 [/SUP]
Affiliations
- PMID: 38727035
- DOI: 10.1002/jmv.29657
The H1N1pdm09 virus has been a persistent threat to public health since the 2009 pandemic. Particularly, since the relaxation of COVID-19 pandemic mitigation measures, the influenza virus and SARS-CoV-2 have been concurrently prevalent worldwide. To determine the antigenic evolution pattern of H1N1pdm09 and develop preventive countermeasures, we collected influenza sequence data and immunological data to establish a new antigenic evolution analysis framework. A machine learning model (XGBoost, accuracy = 0.86, area under the receiver operating characteristic curve = 0.89) was constructed using epitopes, physicochemical properties, receptor binding sites, and glycosylation sites as features to predict the antigenic similarity relationships between influenza strains. An antigenic correlation network was constructed, and the Markov clustering algorithm was used to identify antigenic clusters. Subsequently, the antigenic evolution pattern of H1N1pdm09 was analyzed at the global and regional scales across three continents. We found that H1N1pdm09 evolved into around five antigenic clusters between 2009 and 2023 and that their antigenic evolution trajectories were characterized by cocirculation of multiple clusters, low-level persistence of former dominant clusters, and local heterogeneity of cluster circulations. Furthermore, compared with the seasonal H1N1 virus, the potential cluster-transition determining sites of H1N1pdm09 were restricted to epitopes Sa and Sb. This study demonstrated the effectiveness of machine learning methods for characterizing antigenic evolution of viruses, developed a specific model to rapidly identify H1N1pdm09 antigenic variants, and elucidated their evolutionary patterns. Our findings may provide valuable support for the implementation of effective surveillance strategies and targeted prevention efforts to mitigate the impact of H1N1pdm09.
Keywords: H1N1pdm09; XGBoost; antigenic correlation network; evolutionary patterns; potential cluster‐transition determining sites.