Avoiding the immune system: Using Candida albicans as a tool to understand microbial innate immune evasion strategies

Microbial infections result in the death of over 4 million people/year, with the mortality rate of invasive fungal infections ranging between 40-90%. Despite the prominence, high mortality rates, and limited treatment options for invasive fungal infections, our understanding of fungal virulence is in its infancy compared to bacterial and viral infections. With antifungal resistance on the increase (Candida auris has been declared a global health threat by the WHO due to the emergence of multi-drug resistant strains), there is an urgent need to identify novel antifungal drug targets. To be able to cause infections microbes must compete with the host's innate immune system. The continued arms race between microbes and the immune system has led to pathogens evolving mechanisms to evade and escape the actions of the immune system. To date, we have shown that the opportunistic human fungal pathogen Candida albicans have evolved two innate immune evasion strategies. The first is through the concealment of the highly immunogenic carbohydrate beta-glucan, and the second is through the inhibition of the human alternative complement system. However, recent work in the Hall group has identified a new, as yet to be described, innate immune evasion strategy in C. albicans. The aim of this PhD is to characterise and define the molecular mechanism behind this novel innate immune evasion strategy. Combining the fungal molecular biology, and host-pathogen interaction expertise of the Hall lab with global transcriptomics and proteomics approaches from the Skipp lab, you will identify the host environmental conditions that promote fungal innate immune evasion, identify the key proteins involved in the process, and unravel the regulatory signalling pathways that lead to immune evasion. Therefore, this project will provide expensive training in microbiology, molecular biology, microscopy and immunology techniques, as well as bioinformatic analysis of large data sets.