Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort

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.

Machine learning-based single cell and integrative analysis reveals that baseline mDC predisposition predicts protective Hepatitis B vaccine response

“…Descriptions of all methods not detailed below have recently been published in [Ben-Othman 2020, Shannon 2020].…”

“…Various omics studies were performed as described [Ben-Othman 2020, Shannon 2020]. Briefly, peripheral whole blood cells were profiled by flow cytometry, genome-wide DNA methylation (Illumina Infinium MethylationEPIC BeadChip), transcript abundance (bulk RNA-Seq), and proteome-wide protein abundance (mass spectrometry) at various time points.…”

“…However, the connection between these molecular signatures and the underlying immunological mechanisms remains tenuous. Further, the application of large-scale multi-omics assessment of large vaccination cohorts is cost-prohibitive, raising the question of whether advanced computational and machine learning methods may allow for the discovery of predictive mechanistic signatures in studies with smaller sample sizes [Shannon 2020].…”

“…We recently applied a series of validated multi-omics assays to measure the full range of cellular and molecular components of the immune system, including immune cell composition, DNA methylation, gene expression, protein abundance, and fecal 16S microbiome, to provide an exhaustive picture of the immune response to the HBV vaccine [Ben-Othman 2020]. Multi-omics integrative analysis on these data sets identified a number of candidate baseline predictors of vaccine response using serum antibody titers to the HBV surface antigen following three vaccine doses as the quantitatively-defined endpoint in a relatively small cohort of 15 vaccine recipients [Shannon 2020]. While these candidate predictive signatures could be identified using this systems-level approach in a relatively small cohort, a unifying mechanistic driver did not emerge.…”

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

“…These approaches have been employed to predict vaccine response [ 23 ] or to link complex phenotypes with multi-omics profiles in genome-wide association studies (GWAS) [ 24 , 25 ].…”