Project description:Cryptococcus 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.

2026-04-13 | GSE327336 | GEO

Reconstruction of a genome-scale metabolic model for Streptococcus zooepidemicus: Comparison with Corynebacterium glutamicum to study hyaluronic acid production.

Project description: Not available

| S-EPMC12755811 | biostudies-literature

Genome Sequence of Corynebacterium ulcerans Strain 210932.

Project description: Not available

| S-EPMC4246169 | biostudies-literature

A genome-scale metabolic model of parasitic whipworm.

Project description: Not available

| S-EPMC10618284 | biostudies-literature