# Impact of TcR Signal Strength at the Effector Checkpoint on Protective CD4 T Cell Immunity to Influenza Virus

> **NIH NIH R21** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2021 · $209,375

## Abstract

SUMMARY
Impact of TcR Signal Strength at the Effector Checkpoint on Protective CD4 T Cell Immunity to Influenza
Virus.
Our recent studies show that the development of CD4 memory requires CD4 effectors to again recognize antigen
from influenza virus in order to differentiate to memory. Many non-live vaccines, optimized for safety, induce
strong antigen presentation only for a few days and they fail to induce robust CD4 T cell memory or long-lived B
cell responses, dependent on CD4 helper effectors. Preliminary results indicate high doses of Ag are needed.
We propose to determine the impact of CD4 signal strength, both dose and affinity for the TcR, on the quantity
and quality of CD4 memory and on CD4 effectors specialized for helping B cells. We will determine whether the
CD4 memory obtained is protective and whether providing high signal strength in responses to vaccine can
improve their ability to induce CD4 memory and helper effectors. If high dose and affinity are indeed needed at
the effector stage for protective responses, vaccine strategies would need to be altered to ensure the signals
needed are provided.

## Key facts

- **NIH application ID:** 10187518
- **Project number:** 5R21AI153120-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Susan L Swain
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $209,375
- **Award type:** 5
- **Project period:** 2020-06-09 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10187518

## Citation

> US National Institutes of Health, RePORTER application 10187518, Impact of TcR Signal Strength at the Effector Checkpoint on Protective CD4 T Cell Immunity to Influenza Virus (5R21AI153120-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10187518. Licensed CC0.

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