학술논문
Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort
Document Type
article
Author
Shannon, Casey P; Blimkie, Travis M; Ben-Othman, Rym; Gladish, Nicole; Amenyogbe, Nelly; Drissler, Sibyl; Edgar, Rachel D; Chan, Queenie; Krajden, Mel; Foster, Leonard J; Kobor, Michael S; Mohn, William W; Brinkman, Ryan R; Le Cao, Kim-Anh; Scheuermann, Richard H; Tebbutt, Scott J; Hancock, Robert EW; Koff, Wayne C; Kollmann, Tobias R; Sadarangani, Manish; Lee, Amy Huei-Yi
Source
Subject
Language
Abstract
BackgroundVaccination remains one of the most effective means of reducing the burden of infectious diseases globally. 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.MethodsWe applied cutting edge multi-omics approaches to extensively 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 HBV antibody titres.ResultsUsing both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response.ConclusionThis 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.