Table_11_Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort.xlsx (5.63 kB)
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Table_11_Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort.xlsx

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posted on 30.11.2020, 04:33 by Casey P. Shannon, Travis M. Blimkie, Rym Ben-Othman, Nicole Gladish, Nelly Amenyogbe, Sibyl Drissler, Rachel D. Edgar, Queenie Chan, Mel Krajden, Leonard J. Foster, Michael S. Kobor, William W. Mohn, Ryan R. Brinkman, Kim-Anh Le Cao, Richard H. Scheuermann, Scott J. Tebbutt, Robert E.W. Hancock, Wayne C. Koff, Tobias R. Kollmann, Manish Sadarangani, Amy Huei-Yi Lee
Background

Vaccination 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.

Methods

We 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.

Results

Using 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.

Conclusion

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.

History

References