# Immune status as a predictor of neonatal vaccine immunogenicity

> **NIH NIH U19** · BOSTON CHILDREN'S HOSPITAL · 2020 · $296,174

## Abstract

PROJECT 2 - SUMMARY
Vaccines save millions of lives each year but the risk of infection remains high early in life. Improvement of
early life immunization requires a better understanding of vaccine-induced molecular pathways that underlie
protection and immunogenicity in the form of Correlates of Protection (CoP). Systems biology approaches
(“OMICs”) applied to vaccinology have provided critical insights into vaccine-mediated protection, but have
not yet been applied to the youngest, despite their great need for improved immunization. Immunization with
Hepatitis B vaccine (HBV) starting at birth is highly effective resulting in protection of > 90%. HBV is one of
the few vaccines that has a well characterized and quantifiable CoP (anti-Hep B surface antigen antigen
(anti-HBs) antibody levels). Importantly, while there is an established minimal protective threshold (anti-HBs
> 10 mIU/ml), the absolute titer reached correlates directly with protection (the higher the titer, the higher and
the more durable the protection). Such high protective efficacy, coupled with a quantifiable CoP yet
significant response-variability in the neonatal and infant population makes HBV an ideal model to define
mechanisms underlying successful neonatal immunization. Accordingly, our HIPC proposal, focuses on
’Systems Biology to Identify Biomarkers of Neonatal Vaccine Immunogenicity’ using HBV as the model. To
this end, newborns will be immunized with nothing (delayed), HBV, BCG or (HBV + BCG) and peripheral
blood pre-/post-immunization collected for transcriptomic and proteomic analyses to identify pathways
associated with CoP. In our Project 2 (“Immune status as a predictor of neonatal vaccine immunogenicity’; PI
Tobias Kollmann; Co-Lead Ryan Brinkman) we will analyze the exact same samples interrogated by OMIC
approaches via detailed immune phenotyping to translate the derived OMICs signatures to host immune
parameters. This will not only help to de-convolute the OMIC message, but generate the fine-granular
detailed view necessary to identify biomarkers predicting a protective immune response following neonatal
HBV vaccination. In Aim 1 we will determine cell composition in blood samples by high-end flow cytometry in
pre- and post immunization samples from newborns and correlate with vaccine outcome. To this end we
have developed a novel automated unsupervised gating platform that is the equivalent of unbiased systems
biology discovery approaches but for flow cytometry. In Aim 2 we will determine the concentration of soluble
immune modulators including cytokines, chemokines, and purine metabolizing enzymes in plasma pre- and
post- immunization and correlate with vaccine outcome. In Aim 3 we will develop novel tools to further
advance high-end immune phenotyping, and apply them to the data generated here on the newborn vaccine
response to HBV. Overall our efforts will provide key insight into immunophenotypes associated with
protective neonatal immunization thereby in...

## Key facts

- **NIH application ID:** 9822177
- **Project number:** 5U19AI118608-04
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Tobias R. Kollmann
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $296,174
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9822177

## Citation

> US National Institutes of Health, RePORTER application 9822177, Immune status as a predictor of neonatal vaccine immunogenicity (5U19AI118608-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9822177. Licensed CC0.

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