GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE327nnn/GSE327336/primaryOK200Transcriptomics Mus musculusCryptococcus neoformansExpression profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE327336GEOGSEfalseiCNG99: a validated genome-scale metabolic model of Cryptococcus neoformans strain H99Cryptococcus neoformans is a ubiquitous environmental fungus that can also cause life-threatening infections in immunocompromised individuals. 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. Together, iCNG99 provides a systems-level computational platform for investigating C. neoformans metabolism and for prioritizing antifungal vulnerabilities.2026/04/13GSE327336GSM9653907GSM9653903GSM9653904GSM9653905GSM9653906GSM9653899GSM9653900GSM9653901GSM9653902GSM96538982745119057327336 Mus musculusCryptococcus neoformans