Systems biological approaches to measure and understand vaccine immunity in humans

Abstract

Recent studies have demonstrated the utility of using systems approaches to identify molecular signatures that can be used to predict vaccine immunity in humans. Such approaches are now being used extensively in vaccinology, and are beginning to yield novel insights about the molecular networks driving vaccine immunity. In this review, we present a broad review of the methodologies involved in these studies, and discuss the promise and challenges involved in this emerging field of "systems vaccinology."

Keywords: Systems biology; Systems vaccinology; Vaccines.

Copyright © 2013 Elsevier Ltd. All rights reserved.

Figures

Figure 1. Overview of the problems and methodologies of systems vaccinology

An immune response involves many players at the levels of organs, tissues, cells, macromolecules to metabolites. The cells are at the scale of 10−5 meter and metabolites 10−10. High-throughput technologies are employed to collect molecular data, which are used in combination with conventional immunological assays for predicting vaccine immunogenicity and for unraveling mechanisms of vaccination.

Figure 2. Antibody correlation analysis of a gene module/pathway

(A) Gene module that was learned from previous data (Li et al., in preparation). Genes are connected by significant coexpression in previous studies. This module links the CAMK4 activity to T cell activation, a possible mechanism for the observed Camk4−/− phenotype in Nakaya et al [12]. (B) Example computed from flu TIV data [12]. Each column represents a subject. Top: antibody response after a month (max fold change of hemagglutination-inhibition antibody titers or HAI) and bottom: gene expression change after three days (log2 scale). Module activity is taken as the mean value of member genes (last row of bottom). (C) Pearson correlation between module activity and antibody response. (D) The significance of antibody correlation can also be tested via positional statistics, e.g. the implementation in GSEA. All genes are ranked by their Pearson correlation to antibody, and the positions of module member genes are shown as horizontal bars.