Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses

Abstract

Annual influenza vaccinations are currently recommended for all individuals 6 months and older. Antibodies induced by vaccination are an important mechanism of protection against infection. Despite the overall public health success of influenza vaccination, many individuals fail to induce a substantial antibody response. Systems-level immune profiling studies have discerned associations between transcriptional and cell subset signatures with the success of antibody responses. However, existing signatures have relied on small cohorts and have not been validated in large independent studies. We leveraged multiple influenza vaccination cohorts spanning distinct geographical locations and seasons from the Human Immunology Project Consortium (HIPC) and the Center for Human Immunology (CHI) to identify baseline (i.e., before vaccination) predictive transcriptional signatures of influenza vaccination responses. Our multicohort analysis of HIPC data identified nine genes (RAB24, GRB2, DPP3, ACTB, MVP, DPP7, ARPC4, PLEKHB2, and ARRB1) and three gene modules that were significantly associated with the magnitude of the antibody response, and these associations were validated in the independent CHI cohort. These signatures were specific to young individuals, suggesting that distinct mechanisms underlie the lower vaccine response in older individuals. We found an inverse correlation between the effect size of signatures in young and older individuals. Although the presence of an inflammatory gene signature, for example, was associated with better antibody responses in young individuals, it was associated with worse responses in older individuals. These results point to the prospect of predicting antibody responses before vaccination and provide insights into the biological mechanisms underlying successful vaccination responses.

Pregnancy has a minimal impact on the acute transcriptional signature to vaccination

“…We also determined the baseline transcriptome prior to vaccination: previous studies have investigated whether there is a link between baseline gene signatures and immunogenicity. When the gene signature in the current study was compared to a meta-analysis of influenza vaccine studies 15 , we did not see a consistent difference between pregnant and non-pregnant women in genes that were associated with increased expression in high responders to the vaccine. Interestingly, of the genes with reduced expression associated with high responders measured in the current study, PTPN22, PURA, CASP6 and PPIB all had lower levels of expression in pregnancy.…”

“…While we did not analyse the immune responses to the vaccines, a recent study has looked at the transcript levels as baseline predictors of immunogenicity 15 . When comparing the expression of the suggested marker genes from the published study in pregnancy, we observed the following: among the genes that were associated with a higher response when upregulated, the genes ARRB1, DPP3 and ACTB were significantly higher expressed in non-pregnant compared to pregnant women; there was no difference in expression for MVP, PLEKHB2 or ARPC4 genes and gene expression for GRB2 and RAB24 was higher in pregnant women.…”

Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus

“…By computing effect sizes for each data set independently, MetaIntegrator embraces heterogeneity and avoids the limitations of batch effect correction. We have demonstrated application of this framework across a broad spectrum of diseases, including cancer (16,17), solid organ transplant (13), sepsis (18), viral infection (19), tuberculosis (20), neurodegenerative diseases (21), vaccination (22), and SSc (23). Here, we applied the framework to analyze 40 publicly available whole transcriptome profile data sets containing 7471 samples from patients with SLE, individuals with other autoimmune diseases or infections, and healthy volunteers.…”

Vaccine innovations for emerging infectious diseases—a symposium report

“…Koff is particularly interested in the hypothesis that each person has an innate immune setpoint that may be used to predict the outcome of immunization. [28][29][30][31][32] Finding ways to modulate that setpoint could increase vaccine efficacy.…”