FungalPath · Fungal pathogenicity emerging through functional adaptations to host environments

Fungal infections pose a serious medical threat, especially when they invade the bloodstream. The most common cause of fungal bloodstream infections is invasive candidemia, primarily attributed to a small group of pathogens, including Candida albicans, Nakaseomyces glabratus, Candida tropicalis, and Candida parapsilosis. Such infections are associated with crude mortality rates nearing 40%. Increasing numbers of very closely related yet non-pathogenic species have been sequenced and identified. However, a functional understanding of what differentiates them from their pathogenic counterparts is lacking. As fungal pathogenicity is a multifactorial trait that is influenced by intrinsic and extrinsic factors, it is hard to explain directly from genomic data. This proposal aims to address these challenges by integrating molecular data with physiological contexts across a broad set of species with varying pathogenicity. By leveraging recent advances in mass spectrometry-based analytics that allow for large-scale molecular profiling of both microbial pathogens and human biofluids, combined with high-throughput methods for deep phenotyping of microbial isolates, and by using and identifying scalable yet physiologically relevant growth conditions and infection-relevant phenotypes, we aim to (1) identify adaptations required for pathogenicity and their molecular basis; (2) identify infection-relevant phenotypes and metabolic differences that distinguish pathogenic from non-pathogenic species; and (3) obtain a quantitative understanding of host-pathogen physiology and their interactions. Thus, we aim to uncover traits, phenotypes, and conditions that enable and intersect with pathogenicity. These breakthroughs will be critical for managing and combating these life-threatening infections and predicting the future emergence of human fungal pathogens.