# Pan-vaccine Analysis to Test the Impact of Cytomegalovirus on Vaccine Efficacy

> **NIH NIH UH2** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $201,875

## Abstract

PROJECT SUMMARY/ABSTRACT
Cytomegalovirus (CMV) infects around 50% of the US population. Even though the CMV exists in a latent state
in healthy individuals, it profoundly shapes the immune system. Recent studies suggest that the CMV infection
alters the immune response to influenza vaccine. However, the exact effect of CMV on the efficacy of the
influenza vaccine remains controversial. In addition, how CMV shapes the immune responses toward other
vaccines are unknown. We hypothesize that latent CMV infection induces critical changes in the immune
system, which alters the efficacy of multiple types of vaccines. The ImmPort database currently hosts 133
vaccine studies, covering 21 types of vaccines, creating an unprecedented opportunity for us to test our
hypothesis. We will perform a comprehensive meta-analysis to test the relationship between CMV and vaccine
efficacies, and will use state-of-art statistical models (e.g., Dynamic Bayesian Network) to identify the
mechanism by which CMV alters the vaccine response. Leveraging the group's expertise in computational
immunology and rich datasets on ImmPort, we will address the following aims. Aim1: Test the effect of CMV
on influenza vaccine outcome. We will perform a meta-analysis of 60 influenza studies available on ImmPort
to test the impact of CMV. We will quantify and standardize the efficacy of influenza vaccine across studies,
which are measured by hemagglutinin inhibition (HAI) assays before and after the vaccination. We will also
determine the CMV infection status in subjects, either directly from serological tests or indirectly from immune-
phenotyping data using cutting-edge machine learning tools. We will then test if CMV increases the response
to influenza vaccine by analyzing data from all studies in a unified statistical framework while taking the
heterogeneity between studies into account. Aim2: Bayesian network analysis of influenza vaccine
response. We will harmonize multimodal immune-phenotyping data from the influenza vaccine studies,
including transcriptomics data, cytometry data, and cytokine measurements. We will use state-of-art network
analysis methods (e.g., Dynamic Bayesian network) to model the interplay between the immune components
over time. Using the Bayesian network, we will investigate the mechanism by which CMV shapes the outcome
of influenza vaccination. Aim3: Explore the effect of CMV infection on other vaccines. We will extend our
analysis to vaccines other than influenza vaccine, (e.g., West Nile, Hepatitis B, yellow fever, malaria, and
Tuberculosis). We will quantify the vaccine efficacy using assays specific to the vaccine type, such as
Controlled Human Malaria Infection (CHMI) for the malaria vaccine and Plaque Reduction Neutralization Test
for the yellow fever vaccine. We will perform separate network analyses to characterize the relationship
between CMV and the immune response of individual vaccines. We will then perform joint analysis across
vaccine types t...

## Key facts

- **NIH application ID:** 10171553
- **Project number:** 5UH2AI153016-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Zicheng Hu
- **Activity code:** UH2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $201,875
- **Award type:** 5
- **Project period:** 2020-06-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10171553

## Citation

> US National Institutes of Health, RePORTER application 10171553, Pan-vaccine Analysis to Test the Impact of Cytomegalovirus on Vaccine Efficacy (5UH2AI153016-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10171553. Licensed CC0.

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