tetano
Editor, Senior Moderator
Front Cell Infect Microbiol
. 2024 Feb 7:14:1243586.
doi: 10.3389/fcimb.2024.1243586. eCollection 2024. Leveraging baseline transcriptional features and information from single-cell data to power the prediction of influenza vaccine response
Xiangyu Ye[SUP] #[/SUP][SUP] 1 [/SUP], Sheng Yang[SUP] #[/SUP][SUP] 2 [/SUP], Junlan Tu[SUP] #[/SUP][SUP] 1 [/SUP], Lei Xu[SUP] 1 [/SUP], Yifan Wang[SUP] 3 [/SUP], Hongbo Chen[SUP] 3 [/SUP], Rongbin Yu[SUP] 1 [/SUP], Peng Huang[SUP] 4 [/SUP]
Affiliations
Introduction: Vaccination is still the primary means for preventing influenza virus infection, but the protective effects vary greatly among individuals. Identifying individuals at risk of low response to influenza vaccination is important. This study aimed to explore improved strategies for constructing predictive models of influenza vaccine response using gene expression data.
Methods: We first used gene expression and immune response data from the Immune Signatures Data Resource (IS2) to define influenza vaccine response-related transcriptional expression and alteration features at different time points across vaccination via differential expression analysis. Then, we mapped these features to single-cell resolution using additional published single-cell data to investigate the possible mechanism. Finally, we explored the potential of these identified transcriptional features in predicting influenza vaccine response. We used several modeling strategies and also attempted to leverage the information from single-cell RNA sequencing (scRNA-seq) data to optimize the predictive models.
Results: The results showed that models based on genes showing differential expression (DEGs) or fold change (DFGs) at day 7 post-vaccination performed the best in internal validation, while models based on DFGs had a better performance in external validation than those based on DEGs. In addition, incorporating baseline predictors could improve the performance of models based on days 1-3, while the model based on the expression profile of plasma cells deconvoluted from the model that used DEGs at day 7 as predictors showed an improved performance in external validation.
Conclusion: Our study emphasizes the value of using combination modeling strategy and leveraging information from single-cell levels in constructing influenza vaccine response predictive models.
Keywords: gene expression; influenza; predictive model; single-cell; vaccine response.
. 2024 Feb 7:14:1243586.
doi: 10.3389/fcimb.2024.1243586. eCollection 2024. Leveraging baseline transcriptional features and information from single-cell data to power the prediction of influenza vaccine response
Xiangyu Ye[SUP] #[/SUP][SUP] 1 [/SUP], Sheng Yang[SUP] #[/SUP][SUP] 2 [/SUP], Junlan Tu[SUP] #[/SUP][SUP] 1 [/SUP], Lei Xu[SUP] 1 [/SUP], Yifan Wang[SUP] 3 [/SUP], Hongbo Chen[SUP] 3 [/SUP], Rongbin Yu[SUP] 1 [/SUP], Peng Huang[SUP] 4 [/SUP]
Affiliations
- PMID: 38384303
- PMCID: PMC10879619
- DOI: 10.3389/fcimb.2024.1243586
Introduction: Vaccination is still the primary means for preventing influenza virus infection, but the protective effects vary greatly among individuals. Identifying individuals at risk of low response to influenza vaccination is important. This study aimed to explore improved strategies for constructing predictive models of influenza vaccine response using gene expression data.
Methods: We first used gene expression and immune response data from the Immune Signatures Data Resource (IS2) to define influenza vaccine response-related transcriptional expression and alteration features at different time points across vaccination via differential expression analysis. Then, we mapped these features to single-cell resolution using additional published single-cell data to investigate the possible mechanism. Finally, we explored the potential of these identified transcriptional features in predicting influenza vaccine response. We used several modeling strategies and also attempted to leverage the information from single-cell RNA sequencing (scRNA-seq) data to optimize the predictive models.
Results: The results showed that models based on genes showing differential expression (DEGs) or fold change (DFGs) at day 7 post-vaccination performed the best in internal validation, while models based on DFGs had a better performance in external validation than those based on DEGs. In addition, incorporating baseline predictors could improve the performance of models based on days 1-3, while the model based on the expression profile of plasma cells deconvoluted from the model that used DEGs at day 7 as predictors showed an improved performance in external validation.
Conclusion: Our study emphasizes the value of using combination modeling strategy and leveraging information from single-cell levels in constructing influenza vaccine response predictive models.
Keywords: gene expression; influenza; predictive model; single-cell; vaccine response.