# Computational Core:  System Biological Analyses of Innate and Adaptive Responses to Vaccination

> **NIH NIH U19** · EMORY UNIVERSITY · 2020 · $400,814

## Abstract

Abstract 
Identifying the biological features of the human immune response that correlate with and predict the 
development of an effective immune response to vaccination is an overarching goal of this U19 
consortium. In this core we will apply a suite of computational tools to analyze data from human samples 
of serum, peripheral blood mononuclear cell (PBMC), and individual immune cells obtained before and 
after vaccination to create new knowledge about the biological basis for effective vaccine-mediated 
immunity. Although these datasets will primarily be gene expression profiles of PBMC and single 
immune cells, we will also integrate data from analysis of serum metabolite abundance. To achieve 
these two goals, we have assembled a team of computational biologists and immunologists with deep 
expertise in the generation and analysis of highly complex datasets of transcript abundance and 
metabolic profiles, who will support Projects 1 and 2 in the following aims: 
Aim 1. Identify knowledge-based molecular signatures that predict vaccine immunogenicity. A 
common theme of the proposed analytic approaches is that they use “knowledge-based” approaches to 
identifying features the correlate with vaccination. These knowledge-based approaches have been 
developed as a result of a growing appreciation that analyses that focus on individual, unrelated genes 
that correlate with vaccine outcome often fail to offer mechanistic understanding of how that vaccine 
elicits immunity. Instead, we will use gene-set based analysis that leverage compendia of immune 
signatures generated in the previous funding period. 
Aim 2: Resolve cellular heterogeneity in the innate and adaptive vaccine response to the single 
cell level. Immunologic protection represents the combined activity of dozens of phenotypically and 
functionally distinct cell types. Although much can be learned from the analysis of profiles of aggregates 
of cells (such as expression profiles of PBMC), measurements in bulk populations of cells masks the 
existence and function of individual cells. This aim will provide the tools to generate, analyze and 
interpret global gene expression profiles from individual immune cells using single cell RNA-seq. 
Aim 3: Develop integrative models of vaccine immunity from orthogonal data sources. One of the 
principal challenges to understanding the immune response to vaccination is the complexity of the 
system itself. Thus the challenge of understanding the complexity of the immune response to vaccine lies 
not only in mapping the range of cellular phenotypes, that change following vaccination, but in integrating 
different “views” of the immune response. In this aim we will apply integrative modeling approaches to 
link metabolomic and gene expression profiles in vaccinated subjects.

## Key facts

- **NIH application ID:** 10146540
- **Project number:** 4U19AI090023-11
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** William Nicholas Haining
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $400,814
- **Award type:** 4C
- **Project period:** 2020-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10146540

## Citation

> US National Institutes of Health, RePORTER application 10146540, Computational Core:  System Biological Analyses of Innate and Adaptive Responses to Vaccination (4U19AI090023-11). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10146540. Licensed CC0.

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