# Drivers of individual variation in influenza vaccine response and protection from infection

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2022 · $1,561,514

## Abstract

PROJECT SUMMARY
The induction of protective immune responses through vaccination is central to the management of many
pathogens. For antigenically variable pathogens such as influenza, protective immune responses impose a major
selective pressure on viral populations and indirectly influence vaccine strain selection and vaccine
effectiveness. Our poor understanding of the generation and maintenance of protective immunity to influenza
hinders vaccine development and the accuracy of evolutionary forecasts. Antibody titers to the hemagglutinin
(HA) surface protein were established as a correlate of protection 50 years ago, and more recent evidence shows
many anti-HA antibodies directly and indirectly contribute to viral neutralization. However, HA titers remain only
moderately predictive of an individual’s risk of infection on exposure, and the contributions of other immune
responses are less well understood. Understanding the causes in addition to correlates of protection could
increase the accuracy of forecasts of viral fitness and provide reliable endpoints for vaccine development. Here,
we propose complementary approaches to identify the correlates and drivers underlying protection from infection
and heterogeneity in vaccine responses. We will integrate diverse variables, including infection and vaccination
history, baseline antigen-specific and antigen-agnostic immune states, intrinsic characteristics including age,
sex, and body mass to predict responses to influenza vaccination and extract mechanistic insight. In order to
address our specific aims, we will leverage data from existing, longitudinal studies of immune parameters
following influenza virus infections and vaccination in humans. First we will use computational and multimodal
single-cell approaches to investigate how vaccination and infection impact host immune status. Emerging
evidence, including our own data, suggests that vaccination and infection can establish new antigen-agnostic
immune set points that affect future vaccine responses. Next we propose to integrate complementary
computational approaches, spanning machine learning, causal mediation analysis, and mechanistic modeling to
predict and develop causal mechanistic insight into vaccine responsiveness and protection from severe and mild
infection. We will develop and distribute a suite of accompanying tools to make these novel approaches
accessible to bench and computational biologists. Improved prediction of immune responses, especially
protective immune responses, could lead to more effective vaccination strategies that mitigate vaccine failure in
different subpopulations and improve the public health impact of influenza vaccination. The methods and tools
that we develop can provide foundational frameworks to dissect responses to other vaccines and pathogens.

## Key facts

- **NIH application ID:** 10485666
- **Project number:** 1R01AI170116-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** BENJAMIN JOHN COWLING
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,561,514
- **Award type:** 1
- **Project period:** 2022-07-14 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10485666

## Citation

> US National Institutes of Health, RePORTER application 10485666, Drivers of individual variation in influenza vaccine response and protection from infection (1R01AI170116-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10485666. Licensed CC0.

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