# VLP-based Vaccines for Targeting Staphylococcus Aureus β-barrel Toxins

> **NIH NIH R21** · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · 2022 · $190,625

## Abstract

PROJECT SUMMARY: Staphylococcus aureus (SA), including methicillin-resistant (MRSA), is
the most common cause of skin and soft tissue infection (SSTI) in the US. SA causes recurrent
infections, particularly in highly susceptible patient populations with reduced immune function. To
date, no vaccine to prevent SA infection has succeeded in human trials. Meanwhile, the need for
a vaccine continues to escalate, as does the ability of this pathogen to acquire antibiotic
resistance. We have developed a virus-like particle (VLP)-based vaccine strategy to control SA
secreted β-barrel pore-forming toxins. In models of skin infection, a vaccine based on this
approach induces antibodies that prevent infection pathogenesis, spares host immune cells and
limits disease. In this proposal, we aim to evaluate the preclinical potential of this vaccine strategy
against a variety of SA β-barrel pore-forming toxins. We will determine vaccine immunogenicity
in vivo, and efficacy in SA colonization. Our results could lay the groundwork for development of
an efficacious vaccine to prevent SA infection and limit pathogenesis. This vaccine could
significantly improve the health of patients who suffer from SA infections.

## Key facts

- **NIH application ID:** 10538909
- **Project number:** 1R21AI167127-01A1
- **Recipient organization:** UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
- **Principal Investigator:** Seth Michael Daly
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $190,625
- **Award type:** 1
- **Project period:** 2022-07-20 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10538909

## Citation

> US National Institutes of Health, RePORTER application 10538909, VLP-based Vaccines for Targeting Staphylococcus Aureus β-barrel Toxins (1R21AI167127-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10538909. Licensed CC0.

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