Successful immune response to infection involves coordinated interactions among a myriad of cell types located across multiple tissues in the body. As a result of the highly complex nature of this biological system, many of the mechanisms involved in generating immune responses, particularly at a molecular level, remain unknown. Recent advances in high-throughput technologies, along with the development of computational tools to integrate diverse data types and build comprehensive models of biological responses, are enabling insight into these molecular mechanisms at greater depth than ever before.
This dissertation details a systems biology approach to identifying reliable molecular signatures of immune response to vaccination and infection and utilizing these signatures to predict responsiveness to vaccination and improve understanding of the molecular processes underlying the immune system. Chapter 1 provides an overview of the field of systems vaccinology, which employs systems biology approaches to study vaccine response. Chapter 2 describes the development of a predictive classifier of influenza vaccine response, capable of discriminating between responders and nonresponders to vaccination through identification of distinguishing transcriptional signatures post-vaccination. In Chapter 3, the analysis of immune response to influenza vaccine is extended to examine age-associated changes in response, signatures of response longevity, and miRNA regulation of transcription post-vaccination. Chapter 4 presents an integrated transcriptional and metabolic analysis of dendritic cells, an important innate immune cell, stimulated in vitro with LPS, a bacterial endotoxin that stimulates immune responses to infection. Lastly, Chapter 5 discusses the potential impact of systems biology and predictive analysis on vaccine use and development, as well as future directions and challenges for this field.
