The power of biological tools available to immunologists at the dawn of the 21 st century is unprecedented. However, fundamental questions remain that limit the development of more effective vaccines. The mechanisms that integrate the innate immune response with the development of immunologic memory remain incompletely understood, and the qualitative parameters of the human immune response that correlate with protective immunity are not known. To help answer these questions, the Genomics and Computational Biology Core will provide expertise, analysis and experimental platforms to systematically interrogate the immune response to three commonly used vaccines. The overarching goals of these collaborative studies are two-fold: 1) to develop gene expression-based predictors of vaccine response in humans;and 2) to use genomic techniques as discovery tools so as to better understand the human immune innate and adaptive immune response to vaccines. The Computational Biology Core will play a central role in supporting both of these activities in the following aims:
Aim 1. Assist with generation and standard analyses of genomic data. The Core will work in partnership with the Projects to execute all aspects of genomic data analysis required, including both computational and infrastructure needs. Computational activities of the Core will include the use of existing analytic tools and training of Project members on the use of relevant analyses.
Aim 2. Provide functional annotation of gene expression signatures. The Core will assist the Projects with analyses of gene expression datasets aimed at gaining insight into the fundamental mechanisms of the innate immune response to vaccines. These tools involve gene set enrichment analysis to identify coordinate upregulation of gene-sets of interest, and the application of network theory to reconstruct signaling and transcriptional networks.
Aim 3. Develop gene expression-based predictors of vaccine response. The Core will implement a range of advanced machine-learning approaches to build classifiers that predict the vaccine response. These approaches will integrate complex data sources including transcriptional profiling, microRNA and cytokine data from vaccinated patients to build predictive models that correlate with the immune response to vaccination.
A centralized resource for data analysis will ensure that synergistic interactions between Projects are enabled, and new connections between diverse datasets identified. All Projects will use the Core's expertise in the design, execution and analysis of genomic data as a critical tool for dissecting human Immune responses to vaccination.
|Ho, Irvin Y; Bunker, Jeffrey J; Erickson, Steven A et al. (2016) Refined protocol for generating monoclonal antibodies from single human and murine B cells. J Immunol Methods 438:67-70|
|Nakaya, Helder I; Clutterbuck, Elizabeth; Kazmin, Dmitri et al. (2016) Systems biology of immunity to MF59-adjuvanted versus nonadjuvanted trivalent seasonal influenza vaccines in early childhood. Proc Natl Acad Sci U S A 113:1853-8|
|Quicke, Kendra M; Bowen, James R; Johnson, Erica L et al. (2016) Zika Virus Infects Human Placental Macrophages. Cell Host Microbe 20:83-90|
|Godec, Jernej; Tan, Yan; Liberzon, Arthur et al. (2016) Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation. Immunity 44:194-206|
|Li, Shuzhao; Todor, Andrei; Luo, Ruiyan (2016) Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 14:1-7|
|Ravindran, Rajesh; Loebbermann, Jens; Nakaya, Helder I et al. (2016) The amino acid sensor GCN2 controls gut inflammation by inhibiting inflammasome activation. Nature 531:523-7|
|Peng, Hesen; Ma, Junjie; Bai, Yun et al. (2015) MeDiA: Mean Distance Association and Its Applications in Nonlinear Gene Set Analysis. PLoS One 10:e0124620|
|Wang, Kai; Zhao, Qing; Lu, Jianwei et al. (2015) K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data. Biomed Res Int 2015:918954|
|Cobey, Sarah; Wilson, Patrick; Matsen 4th, Frederick A (2015) The evolution within us. Philos Trans R Soc Lond B Biol Sci 370:|
|Pulendran, Bali; Maddur, Mohan S (2015) Innate immune sensing and response to influenza. Curr Top Microbiol Immunol 386:23-71|
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