# Innovative OMIC integration to predict immunogenicity

> **NIH NIH U19** · BOSTON CHILDREN'S HOSPITAL · 2020 · $215,431

## Abstract

PROJECT SUMMARY
More than 2 million infants die every year from infections, particularly the very young in resource-poor settings.
Moreover, due to distinct immunity, newborns are less apt to mount protective immune responses to most
vaccines. Improvement of newborn immunization thus requires a better biological understanding of vaccine-
induced immune responses that correspond to protection. HIPC Project 1 proposes an innovative systems
biological investigation to gain a more holistic view of vaccine-induced immunity. We will use novel advanced
statistical and computational approaches to analyze very large and unbiased datasets of molecular and cellular
information measured from small samples of blood obtained from newborns undergoing immunization with
hepatitis B vaccine (HBV), given with or without Bacille Calmette-Guérin (BCG). The molecular datasets will
consist of precise measurements of tens of thousands of gene expression read-outs (gene transcripts [RNA]
and proteins) that will be generated using state-of-the-art methods and instruments, such as next generation
sequencing (RNA-Seq) and mass spectrometry (Service Cores 1 & 2, and Data Management Core). Our
preliminary data, derived from analyses of West African cohorts in Guinea Bissau and MRC-Gambia,
demonstrate feasibility of measuring transcriptomic and proteomic endpoints in small volumes of newborn
peripheral blood and suggest Expanded Program on Immunization (EPI) vaccine-induced molecular signatures
in early life. Molecular response signatures and biomarker classifiers that predict subsequent immunogenicity,
especially correlates of protection (CoP) against infection, will be identified. Innovative bioinformatics-based
and data-driven biomarker integration approaches will reveal patterns of gene expression, bionetworks,
molecular pathways and biomarker classifiers associated with successful immunization, and/or sub-optimal
immunogenicity. We will achieve our goal by pursuing the following Specific Aims: in Specific Aim 1, we will
identify blood transcriptomic signatures in human newborns that correlate with effective immunization, using
pre- and post-vaccine whole blood RNA-Seq datasets both in vitro (in vitro modeling HIPC Project 3) and in
vivo; in Specific Aim 2, we will identify human newborn blood plasma and cellular proteomic signatures in vitro
and in vivo that correlate with effective immunization, using proteomic datasets that are study participant- and
time-matched to the RNA-Seq datasets (Specific Aim 1); and in Specific Aim 3, we will further develop and
use statistical and computational approaches to allow integration across the in vitro and in vivo transcriptomic
and proteomic datasets, including with the high-end flow cytometry analyses that will define cellular subtypes in
blood (HIPC Project 2). Innovative bioinformatics and biostatistics will further refine and discover new
molecular/cellular signatures associated with HBV CoP, mechanisms of action of HBV and pote...

## Key facts

- **NIH application ID:** 9822176
- **Project number:** 5U19AI118608-04
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Scott Tebbutt
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $215,431
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9822176

## Citation

> US National Institutes of Health, RePORTER application 9822176, Innovative OMIC integration to predict immunogenicity (5U19AI118608-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9822176. Licensed CC0.

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