Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations - Zimmermann, Michael T.; Kennedy, Richard B.; Grill, Diane E.; Oberg, Ann L.; Goergen, Krista M.; Ovsyannikova, Inna G.; Haralambieva, Iana H.; Poland, Gregory A. (Frontiers in Immunology, 2017)
The development of a humoral immune response to influenza vaccines occurs on a multi-systems-level. Due to the orchestration required for robust immune responses when multiple genes and their regulatory components across multiple cell types are involved, we examined an influenza vaccination cohort using multiple high-throughput technologies. In this study, we sought a more thorough understanding of how immune cell composition and gene expression relate to each other and contribute to inter-individual variation in response to influenza vaccination. We first hypothesized that many of the differentially expressed genes observed after influenza vaccination result from changes in the composition of participants’ PBMCs, which were assessed using flow cytometry. We demonstrated that differentially expressed genes in our study are correlated with changes in PBMC composition. We gathered differentially expressed genes from 128 other publically available PBMC-based vaccine studies and identified that an average of 57% correlated with specific cell subset levels in our study (permutation used to control false discovery), suggesting that the associations we’ve identified are likely general features of PBMC-based transcriptomics. Second, we hypothesized that more robust models of vaccine response could be generated by accounting for the interplay between PBMC composition, gene expression, and gene regulation. We employed machine learning to generate predictive models of B-cell ELISPOT response outcomes and hemagglutination inhibition (HAI) antibody titers. The top HAI and B-cell ELISPOT model achieved an area under the receiver operating curve of 0.64 and 0.79, respectively, with linear model coefficients of determination of 0.08 and 0.28. For the B-cell ELISPOT outcomes, CpG methylation had the greatest predictive ability, highlighting potentially novel regulatory features important for immune response. B-cell ELISOT models using only PBMC composition had lower performance (AUC = 0.67), but highlighted well-known mechanisms. Our analysis demonstrated that each of the three datasets (cell composition, mRNA-Seq, and DNA methylation) may provide distinct information for the prediction of humoral immune response outcomes. We believe these findings are important for the interpretation of current omics-based studies and set the stage for a more thorough understanding of inter-individual immune responses to influenza vaccination.