Vaccinomics, adversomics, and the immune response network theory: individualized vaccinology in the 21st century

Abstract

Vaccines, like drugs and medical procedures, are increasingly amenable to individualization or personalization, often based on novel data resulting from high throughput "omics" technologies. As a result of these technologies, 21st century vaccinology will increasingly see the abandonment of a "one size fits all" approach to vaccine dosing and delivery, as well as the abandonment of the empiric "isolate-inactivate-inject" paradigm for vaccine development. In this review, we discuss the immune response network theory and its application to the new field of vaccinomics and adversomics, and illustrate how vaccinomics can lead to new vaccine candidates, new understandings of how vaccines stimulate immune responses, new biomarkers for vaccine response, and facilitate the understanding of what genetic and other factors might be responsible for rare side effects due to vaccines. Perhaps most exciting will be the ability, at a systems biology level, to integrate increasingly complex high throughput data into descriptive and predictive equations for immune responses to vaccines. Herein, we discuss the above with a view toward the future of vaccinology.

Keywords: Adaptive immunity; Adversomics; Biotechnology; Computational biology; Genomics; Immune response network theory; Immunogenetics; Individualized medicine; Individualized vaccinology; Modeling; Predictive equation; Proteomics; Systems biology; Vaccination; Vaccines; Vaccinomics.

Fig. 1

High-dimensional “omics” technologies. (A) An overview of high dimensional approaches commonly utilized to comprehensively assess biological systems in response to various stimuli (i.e., vaccination). The resulting datasets provide the “puzzle pieces” necessary to developing a systems level understanding of the biological response being investigated. (B) An overview of the biostatistical and informatics tools required to “assemble the puzzle pieces” in order to make biological sense out of the large datasets generated in panel A.

Fig. 2

Vaccinomics and the development of the Immune Response Network Theory for directed vaccine design. The host response to vaccination leads to the sequential activation of complex and poorly understood biological pathways and networks (black box). The outcome of this response is a consequence of the cascade of biological pathways that are initiated. Because of the inherent complexities of the diseases for which we lack vaccines, it is no longer feasible to take an empirical approach to vaccine development. A logical, directed approach is needed. This begins with the administration of a vaccine to an appropriate animal or human model. Biological specimens consisting of the relevant immune cells or bodily fluids (sera, mucosal secretions) are collected at specified time-points in order to assess immune outcomes over time. High throughput “omics” technologies are utilized in order to comprehensively assess biological processes. This is analogous to opening a puzzle box and spreading out all of the pieces, with each “piece” of the puzzle being a separate assay. This is followed by the use of advanced statistical and bioinformatics tools to make logical sense of the high-throughput data (i.e., putting the pieces together to form a coherent picture of the development of an immune response over time). The resulting systems-level understanding of immune function following vaccination allows us to identify predictive biomarkers, essential immune pathways, select appropriate adjuvants, alter vaccine composition to avoid side effects, include essential epitopes, and circumvent escape variants to deliver a personalized vaccine treatment. The resulting knowledge is also applied to refining the experimental systems used to assess immune function, resulting in an iterative cycle of discover, validate, characterize, and apply this new knowledge.

Fig. 3

Genetic association epistasis network for human antibody response to smallpox vaccine. Data-driven, vaccine-specific interaction networks will be an important component of personalized, predictive vaccinomics. The properties of such networks can provide target information for vaccine or therapeutic design, and the connectivity of such networks can guide causal and mechanistic models of vaccine immune response. This network was inferred from a genetic association study of individuals who received the smallpox vaccine. The nodes and edges carry genetic variation that explains an individual’s response to smallpox vaccine. The most influential hub gene in the network is retinoid-X receptor-alpha (RXRA), labeled in red at the bottom. This gene is involved in mediating vitamin D signaling and innate immune response. However, the importance of RXRA would not be recognized in this data by a univariate analysis. The identification of this gene through a genome-wide network analysis indicates the importance of statistical modeling that reflects the underlying complexity of the biological system. Adapted from [149].