Cross-omics integration to identify modulators for improving vaccine efficacy

Influenza is a significant public health threat and vaccines are crucial for preventing infections at population-level. The efficacy of vaccination per individual, however, is highly variable. The causes for this broad variability in vaccine response between individuals remain poorly understood.

In this proposal, I hypothesize that genetic variants and their downstream pathways underlie the heterogeneity in vaccine response between individuals.

This ERC project aims to for the first time, systematically investigate the interactions between genetic, non-genetic host and environmental factors, and the response to vaccination in order to build reliable models for predicting vaccine efficacy.

The outcomes of this research will pinpoint key deterministic factors and identify modulators that can be used to improve vaccination strategies.

This project is based on the expertise that my research group has built up for identifying the downstream consequences of genetic variants, and for predictive modelling through integration of large cross-omics datasets.

Given the rapid evolution of influenza virus, I will use seasonal trivalent inactivated influenza vaccines as prototype responses within two cohorts of 500 individuals from the Netherlands and 200 individuals from Germany.

I will systematically generate, analyse, and integrate the cross-omics data (six layers of information from genome, epigenome, transcriptome, proteome, metabolome, and microbiome) with immune phenotypes (e.g. antibody titers, an important indicator of protection) using novel computational methods.

This research will reveal the previously unknown cell-types, molecules, and pathways involved in vaccine-induced immune response and provide mathematical models for predicting individual variation in immune response, a crucial first step towards personalized prevention. The key molecules I identify will provide leads for pharmacological modulators for improving vaccine efficacy.