iCNG99: a validated genome-scale metabolic model of Cryptococcus neoformans strain H99

As a competent pathogen, Cryptococcus needs to reprogram its metabolism to adapt the drastic differences between environmental niches and host niches. A well-curated genome-scale metabolic model (GEM) is a powerful tool to facilitate the investigation of the metabolic resilience of an organism Here we reconstructed and validated iCNG99, a GEM for C. neoformans reference strain H99, and evaluated its predictive performance across 43 growth conditions and gene essentiality benchmarks.

The model achieved high confidence essential gene prediction (precision = 0.77) and recapitulated pathways targeted by clinically available antifungals. Integration with transcriptomic and metabolomic data enabled iCNG99 to capture condition-specific metabolic adaptations and to identify candidate vulnerabilities in drug tolerance, revealing metabolic adaptations associated with survival within host conditions and drug susceptibility.