Unveiling new features of the human pathogen Cryptococcus neoformans through the reconstruction and exploitation of a dedicated genome-scale metabolic model

Abstract

Cryptococcus neoformans is notorious for causing severe pulmonary and central nervous system infections, particularly in immunocompromised patients. High mortality rates, associated with its tropism and adaptation to the brain microenvironment and its drug resistance profile, make this pathogen a public health threat and a World Health Organization (WHO) priority. This study presents the first reconstructed genome-scale metabolic model (GSMM), iRV890, for C. neoformans var. grubii, which comprises 890 genes, 2598 reactions, and 2047 metabolites across four compartments. The GSMM iRV890 model was reconstructed using the open-source software tool merlin 4.0.2 and is openly available in the well-established systems biology markup language (SBML) format and underwent validation using experimental data for specific growth and glucose consumption rates, and 222 nitrogen and carbon assimilation sources, with a 85% prediction rate. Based on the comparison with GSMMs available for other pathogenic yeasts, unique metabolic features were predicted for C. neoformans, including key pathways shaping dynamics between C. neoformans and human host, as well as its underlying adaptions to the brain environment. Finally, the 96 predicted essential genes from the validated model are investigated as potential novel antifungal drug targetsincluding Erg4, Chs1, Fol1, and Fas1which represent promising candidates for targeted drug development due to their absence in human cells.