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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Program--Cooperative Agreements (U19)
Project #
5U19AI090023-04
Application #
8495234
Study Section
Special Emphasis Panel (ZAI1-QV-I)
Project Start
Project End
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
4
Fiscal Year
2013
Total Cost
$451,787
Indirect Cost
$130,092
Name
Emory University
Department
Type
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Upadhyay, Amit A; Kauffman, Robert C; Wolabaugh, Amber N et al. (2018) BALDR: a computational pipeline for paired heavy and light chain immunoglobulin reconstruction in single-cell RNA-seq data. Genome Med 10:20
Bowen, James R; Zimmerman, Matthew G; Suthar, Mehul S (2018) Taking the defensive: Immune control of Zika virus infection. Virus Res 254:21-26
Woodruff, Matthew Charles; Kim, Eui Ho; Luo, Wei et al. (2018) B Cell Competition for Restricted T Cell Help Suppresses Rare-Epitope Responses. Cell Rep 25:321-327.e3
Yu, Tianwei (2018) A new dynamic correlation algorithm reveals novel functional aspects in single cell and bulk RNA-seq data. PLoS Comput Biol 14:e1006391
Sullivan, Nicole L; Reuter-Monslow, Morgan A; Sei, Janet et al. (2018) Breadth and Functionality of Varicella-Zoster Virus Glycoprotein-Specific Antibodies Identified after Zostavax Vaccination in Humans. J Virol 92:
Lynn, David J; Pulendran, Bali (2018) The potential of the microbiota to influence vaccine responses. J Leukoc Biol 103:225-231
Yu, Tianwei (2018) Nonlinear variable selection with continuous outcome: a fully nonparametric incremental forward stagewise approach. Stat Anal Data Min 11:188-197
Levin, Myron J; Cai, Guang-Yun; Lee, Katherine S et al. (2018) Varicella-Zoster Virus DNA in Blood After Administration of Herpes Zoster Vaccine. J Infect Dis 217:1055-1059
Hagan, Thomas; Pulendran, Bali (2018) Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines? From Data to Understanding through Systems Biology. Cold Spring Harb Perspect Biol 10:
Kang, Hyun Min; Subramaniam, Meena; Targ, Sasha et al. (2018) Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat Biotechnol 36:89-94

Showing the most recent 10 out of 105 publications