Abstract Candida species are a dominant constituent of the human mycobiome and a better understanding of their metabolism from a fungal perspective can provide key insights into their ability to cause pathogenesis. Here, we have developed the BioFung database – a fungal specific tool for functional annotation using the KEGG database that provides an efficient method for annotation of protein-encoding gene. Analysis of carbohydrate-active enzyme (CAZymes) and BioFung, uncovered core and accessory features across Candida species demonstrating plasticity, adaptation to the environment and acquired features. Integerative functional analysis revealed that all Candida species can employ amino acid metabolism. However, metabolomics revealed that only a specific cluster of species (AGAu species - C. albicans, C. glabrata and C. auris ) utilised amino acid metabolism. We identified critical metabolic pathways in the AGAu clusters with biomarkers and anti-fungal target potential in the CAZyme profile, polyamine, choline and fatty acid biosynthesis pathways. This study, combining genomic analysis, metabolomics and gene expression validation, highlights the metabolic diversity within AGAu species that underlies their remarkable ability to dominate the mycobiome and cause disease.

Integrative functional analysis uncovers metabolic differences between Candida species