Systems vaccinology: Enabling rational vaccine design with systems biological approaches

Abstract

Vaccines have drastically reduced the mortality and morbidity of many diseases. However, vaccines have historically been developed empirically, and recent development of vaccines against current pandemics such as HIV and malaria has been met with difficulty. The advent of high-throughput technologies, coupled with systems biological methods of data analysis, has enabled researchers to interrogate the entire complement of a variety of molecular components within cells, and characterize the myriad interactions among them in order to model and understand the behavior of the system as a whole. In the context of vaccinology, these tools permit exploration of the molecular mechanisms by which vaccines induce protective immune responses. Here we review the recent advances, challenges, and potential of systems biological approaches in vaccinology. If the challenges facing this developing field can be overcome, systems vaccinology promises to empower the identification of early predictive signatures of vaccine response, as well as novel and robust correlates of protection from infection. Such discoveries, along with the improved understanding of immune responses to vaccination they impart, will play an instrumental role in development of the next generation of rationally designed vaccines.

Keywords: Systems biology; Systems vaccinology; Vaccines.

Figure 1

Biological and technological challenges in systems vaccinology.

Figure 2. Predictive analysis of vaccine response

The goal of predictive analyses is to identify early transcriptional signatures capable of predicting vaccine-induced immune responses. Integration of transcriptomic measurements with gene co-expression networks or biological pathways provides improved functional understanding of the immune processes activated in response to vaccination. These gene or module-level features can then be used as inputs to feature selection and machine learning algorithms. Such tools enable identification of molecular signatures that are predictive of responses to vaccination, such as antibody or T cell responses. Once predictive signatures are verified through blind prediction of independent datasets, they can be employed in a clinical setting to rapidly identify vaccinees with deficient responses and assess vaccine efficacy.