Implementing Bioinformatic Tools to Predict Vaccine Potential from Prioritized Staphylococcus aureus Antigens

Cierra R. Wheeler, Danielle Scarbrough, Juliette Tinker

Research output: Contribution to conferencePresentation

Abstract

Staphylococcus aureus is a commensal but potentially pathogenic bacteria. Many are exposed to S. aureus at birth and successfully maintain homeostasis after colonization. However, initial exposure does not guarantee immunity (1). What is the underlying mechanism that allows S. aureus to fly under the radar of our innate and adaptive immune system? The challenge today is developing an effective preventative vaccine to protect different populations at risk for chronic and acute S. aureus infection (1,2,3). Among these populations, U.S. dairy cows are at a significant risk of infection. Bovine mastitis, caused by S. aureus , is an almost two-billion-dollar loss to the U.S. dairy industry (4). We previously developed a bovine vaccine from surface antigens of S. aureus and demonstrated that vaccinated cows induced a higher antigen-specific immune response than unvaccinated animals (5). While the vaccine may have reduced bacterial shedding, it did not completely prevent infection (5). Our objective is to improve vaccine efficacy by incorporating additional antigens identified through previous proteomic and transcriptomic approaches. Transcriptomics results will be validated by RT-qPCR. Vaccine candidate antigens will be characterized using bioinformatics tools including NCBI, PsortB, and Vaxign. Antigens will be prioritized based on function, cellular localization, adhesin potential, and presence of B and T cell epitopes. These studies will promote the development of an effective S. aureus vaccine to prevent disease in cows and may also inform human vaccine design.

Original languageAmerican English
StatePublished - 12 Jul 2020

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