<i>Cryptococcus neoformans</i> can utilize ferritin as an iron source
Bibliographic record
Abstract
Ferritin, a major iron storage protein in vertebrates, supplies iron upon iron deficiency. Ferritin is also found extracellularly, and acts as an iron carrier and a contributor to the immune response to invading microbes. Some microbial pathogens take advantage of ferritin as an iron source upon infection. However, no information is currently available on whether the human fungal pathogen Cryptococcus neoformans can acquire iron from ferritin. Here, we found that C. neoformans grew well in the presence of ferritin as a sole iron source. We showed that the binding of ferritin to the surface of C. neoformans is necessary and that acidification may contribute to ferritin-iron utilization by the fungus. Our data also revealed that the high-affinity reductive iron uptake system in C. neoformans is required for ferritin-iron acquisition. Furthermore, phagocytosis of C. neoformans by macrophages led to increased intracellular ferritin levels, suggesting that iron is sequestered by ferritin in infected macrophages. The increase in intracellular ferritin levels was reversed upon infection with a C. neoformans mutant deficient in the high-affinity reductive iron uptake system, indicating that this system plays a major role in iron acquisition in the phagocytosed C. neoformans in macrophages. LAY SUMMARY: Cryptococcus neoformans is an opportunistic fungal pathogen causing life-threatening pulmonary disease and cryptococcal meningitis, mainly in immunocompromised patients. In this study, we found that C. neoformans can use ferritin, a major iron storage protein in vertebrates, as a sole iron source.
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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) | 0.001 | 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".