Granule-Dependent Natural Killer Cell Cytotoxicity to Fungal Pathogens
Bibliographic record
Abstract
Natural killer (NK) cells kill, or inhibit the growth of a number of fungi including Cryptococcus, Candida, Aspergillus, Rhizopus, and Paracoccidioides. Although many fungi are not dangerous, invasive fungal pathogens such as Cryptococcus neoformans, cause life-threatening disease in individuals with impaired cell-mediated immunity. While there are similarities to cell-mediated killing of tumor cells, there are also important differences. Similar to tumor killing, NK cells directly kill fungi in a receptor-mediated, cytotoxic granule-dependent manner. Unlike tumor cell killing where multiple NK cell activating receptors cooperate and signal events that mediate cytotoxicity, only the NKp30 receptor has been described to mediate signaling events that trigger the NK cell to mobilize it’s cytolytic payload to the site of interaction with C. neoformans and Candida albicans, subsequently leading to granule exocytosis and fungal killing. More recently, the NKp46 receptor was reported to bind Candida glabrata adhesins Epa1, 6, and 7 and directly mediate fungal clearance. A number of unanswered questions remain. Is only one NK cell activating receptor sufficient for signaling leading to fungal killing? Are the signaling pathways activated by fungi similar to those activated by tumor cells during NK cell killing? How do the cytolytic granules traffic to the site of interaction with fungi, and how does this process compare with tumor killing? Recent insights into receptor use, intracellular signaling and cytolytic granule trafficking during NK cell-mediated fungal killing will be compared to tumor killing, and the implications for therapeutic approaches will be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".