Project description:We measured protein translation (by ribosome profiling) and RNA levels (by polyA-enriched RNA-seq) in Cryptococcus neoformans strain H99 and Cryptococcus neoformans strain JEC21. This is the first transcriptome-wide map of translation in this species complex.
Project description:Comparison of transcriptional profiles of WT Cryptococcus neoformans (H99) and strain CM126 (pRPL2b-GAT201) which overexpresses the transcription factor GAT201 using a ribosomal protein promoter Keywords: Genetic modification
Project description:Invasive fungal infections (IFIs) are difficult to treat. Few effective antifungal drugs are available and many have problems with toxicity, efficacy and drug-resistance. To overcome these challenges, existing therapies may be enhanced using more than one agent acting in synergy. Previously, we have found amphotericin B (AMB) and the iron chelator, lactoferrin (LF), were synergistic against Cryptococcus neoformans and Saccharomyces cerevisiae. This study investigates the mechanism of AMB+LF synergy using RNA-seq in Cryptococcus neoformans H99.
Project description:Approximately 1 million cells of Cryptococcus neoformans lab strain H99 were spread on YPD plate supplmemented with 3ug/ml amphotericin B. Randomly 30 adaptors (TJ1832 - TJ1861) were chosen. The parent and all the 30 adaptors were sequenced.
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.
