Iron Source Preference and Regulation of Iron Uptake in Cryptococcus neoformans
Bibliographic record
Abstract
The level of available iron in the mammalian host is extremely low, and pathogenic microbes must compete with host proteins such as transferrin for iron. Iron regulation of gene expression, including genes encoding iron uptake functions and virulence factors, is critical for the pathogenesis of the fungus Cryptococcus neoformans. In this study, we characterized the roles of the CFT1 and CFT2 genes that encode C. neoformans orthologs of the Saccharomyces cerevisiae high-affinity iron permease FTR1. Deletion of CFT1 reduced growth and iron uptake with ferric chloride and holo-transferrin as the in vitro iron sources, and the cft1 mutant was attenuated for virulence in a mouse model of infection. A reduction in the fungal burden in the brains of mice infected with the cft1 mutant was observed, thus suggesting a requirement for reductive iron acquisition during cryptococcal meningitis. CFT2 played no apparent role in iron acquisition but did influence virulence. The expression of both CFT1 and CFT2 was influenced by cAMP-dependent protein kinase, and the iron-regulatory transcription factor Cir1 positively regulated CFT1 and negatively regulated CFT2. Overall, these results indicate that C. neoformans utilizes iron sources within the host (e.g., holo-transferrin) that require Cft1 and a reductive iron uptake system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".