Systems vaccinology: learning to compute the behavior of vaccine induced immunity

Abstract

The goal of systems biology is to access and integrate information about the parts (e.g., genes, proteins, cells) of a biological system with a view to computing and predicting the behavior of the system. The past decade has witnessed technological revolutions in the capacity to make high throughput measurements about the behavior of genes, proteins, and cells. Such technologies are widely used in biological research and in medicine, such as toward prognosis and therapy response prediction in cancer patients. More recently, systems biology is being applied to vaccinology, with the goal of: (1) understanding the mechanisms by which vaccines stimulate protective immunity, and (2) predicting the immunogenicity or efficacy of vaccines. Here, we review the recent advances in this area, and highlight the biological and computational challenges posed.

FIGURE 1

The tool kit of Systems Vaccinologists. Low and high-throughput technologies that can be used by systems vaccinologists to investigate the mechanisms of vaccines. Antibody response, cytokine profiling, metabolome, and proteome of vaccinees can be assessed in the blood plasma. T and B cell responses can be determined by flow cytometry and ELISPOT in the fraction containing peripheral blood mononuclear cells. Different separation methods can be used to collect from these cells the DNA, RNA, proteins, or metabolites which can be screened by high-throughput techinologies, such as microarrays and mass spectrometry. ELISA, enzyme-linked immunosorbent assay; ELISPOT, enzyme-linked immunosorbent spot; HAI, haemagglutination inhibition; ChIP-seq, chromatin-immunoprecipitation followed by high-throughput sequencing; ChIP-chip, chromatin-immunoprecipitation followed by hybridization to microarrays; MPSS, Massively Parallel Signature Sequencing; SNP, single-nucleotide polymorphism.

FIGURE 2

Identifying molecular signatures of vaccination. Multiple microarray analyses can be performed to identify genes and pathways whose expression is modulated in response to vaccination or to identify signatures that classify subjects based on their immune responses.

FIGURE 3

Yellow fever vaccine response network. Microarray analysis of blood of subjects before and after YF17D vaccination generated a list of differentially expressed genes (labeled in red). Genes were subsequently added to the network based on their interactions with those differentially expressed genes and their connections in the interactome. Among these additional genes, the yellow ones still have p-value < 0.05 in the transcriptomic analysis; Genes in blue have no statistical significance in the transcriptomic analysis, but may be required for gene regulation.

FIGURE 4

Challenges of systems vaccinology.