Vaccines are an important public health tool but some vaccines, such as the seasonal influenza vaccine, are only effective in a limited subset of individuals. This is particularly true in high-risk populations such as older adults. The advent of high-throughput methods for characterizing genome-wide transcription and cellular phenotypes allows for broad profiling of the immune system in response to disease or perturbations, such as influenza infection and vaccination. However, analyzing these data to understand the mechanisms underlying changes in transcription or cell composition remains a challenge because of the sheer number of parameters measured. In tins dissertation, we develop methods to address this issue in order to gain insights into the human immune response to influenza infection and vaccination. We begin by applying statistical models to predict influenza vaccination response and show that commonly used methods can be uninterpretable or present only one of many equally valid models, leading to a narrow understanding of the underlying biology. We propose and apply a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), which improves biological interpretability by incorporating prior knowledge from gene-gene networks. We next profile the transcriptional landscape of nearly 300 young and older adults collected at multiple time points over five consecutive vaccination seasons to identify shared signatures of vaccine response as well as important seasonal differences. While vaccine-induced transcriptional signatures were generally correlated between young and older adults, signatures of influenza-specific antibody responses 28 days post-vaccination were inversely related between young and older adults. Finally, we examine differential in vitro host responses to infection with four influenza A viral strains. We identify a core transcriptional response shared across strains as well as timing differences in induction of the antiviral response program. We also observed divergent trajectories between pandemic and seasonal viruses caused by widespread mRNA loss post-infection with seasonal, but not pandemic, viruses. The methods and analyses presented in this dissertation allow us to better understand the mechanisms underlying influenza infection and vaccination responses and can be applied to other biological systems to provide new insights.
|Advisor:||Kleinstein, Steven H|
|School Location:||United States -- Connecticut|
|Source:||DAI-B 79/05(E), Dissertation Abstracts International|
|Keywords:||Immunology, Influenza, Prior Knowledge, Systems Biology, Transcription, Vaccine|
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