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Multi-omic data integration allows baseline immune signatures to predict hepatitis B vaccine response in a small cohort

ABSTRACT: Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Climate change is projected to lead to an increase in the burden of vector- and water-borne, as well as zoonotic infectious diseases including COVID-19. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. We applied cutting edge multi-omics approaches to characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final hepatitis B antibody titres. Using both a molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we overcame the p>>>n problem and uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Correlations were identified with signalling pathways such as JAK-STAT and interleukin signalling, Toll-Like Receptor Cascades, interferon signalling and Th17 cell differentiation. This study provides further evidence that baseline cellular and molecular characteristics of an individual’s immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.

ORGANISM(S): Homo sapiens

PROVIDER: GSE155198 | GEO | 2020/12/21

REPOSITORIES: GEO

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Multi-omic data integration allows baseline immune signatures to predict hepatitis B vaccine response in a small cohort

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