The virulence factor urease and its unexplored role in the metabolism of <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Cryptococcal urease is believed to be important for the degradation of exogenous urea that the yeast encounters both in its natural environment and within the human host. Endogenous urea produced by the yeast's own metabolic reactions, however, may also serve as a substrate for the urease enzyme. Using wild-type, urease-deletion mutant and urease-reconstituted strains of Cryptococcus neoformans H99, we studied reactions located up- and downstream from endogenous urea. We demonstrated that urease is important for cryptococcal growth and that, compared to nutrient-rich conditions at 26°C, urease activity is higher under nutrient-limited conditions at 37°C. Compared to cells with a functional urease enzyme, urease-deficient cells had significantly higher intracellular urea levels and also showed more arginase activity, which may act as a potential source of endogenous urea. Metabolic reactions linked to arginase were also affected, since urease-positive and urease-negative cells differed with respect to agmatinase activity, polyamine synthesis, and intracellular levels of proline and reactive oxygen species. Lastly, urease-deficient cells showed higher melanin levels at 26°C than wild-type cells, while the inverse was observed at 37°C. These results suggest that cryptococcal urease is associated with the functioning of key metabolic pathways within the yeast cell.
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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".