Project No. 2464
Prof Paul Skipp – University of Southampton
Dr Rebecca Hall – University of Kent
Dr Benjamin Nicholas – University of Southampton
Dr Sandra Wilks – University of Southampton
Antimicrobial resistance poses a significant global threat to public health, as microbial and fungal pathogens continually develop resistance to existing treatments, rendering them less effective.
While antibiotics struggle to combat diseases caused by drug-resistant bacteria, equally harmful fungi are becoming increasingly resistant to the limited range of available antifungal therapies.
In the face of this dual challenge, vaccines are emerging as a crucial tool to counter the spread of resistance. Fuelled by the urgency to address the COVID-19 pandemic, one of the newest and exciting areas of vaccine technology is the utilisation of mRNA vaccines which can be developed quickly. However, there are still challenges in identifying dominant immune targets that can generate universal CD8+ and CD4+ T cell responses against pathogens. For example, while T cell prediction algorithms are effective for narrowing down potential epitopes, their success, especially in predicting epitopes presented by the Major Histocompatibility Complex Class II (MHC II) is limited.
A more attractive approach to enhance the identification of immunodominant epitopes involves directly identifying pathogen-derived peptide epitopes presented to T cells by both MHC I and II molecules on infected cells. Mass spectrometry-based immunopeptidomics screens offer a means to identify pathogen antigens presented on infected cells. These antigens can be readily integrated into next-generation nucleic acid-based vaccines, serving as innovative tools to combat antimicrobial and fungal resistance.
This project aims to optimize a strategy for identifying novel MHC class I and II vaccine epitopes for E. coli and Candida albicans through a combination of in-vitro infection models, in silico epitope prediction, and immunopeptidomics. Potential epitope candidates will be identified and tested for their immunogenicity and conservation using computational tools. This research will provide a deeper understanding of the host immune response to these pathogens and contribute to the development of more effective vaccines.