<i>Cryptococcus neoformans</i>Directly Stimulates Perforin Production and Rearms NK Cells for Enhanced Anticryptococcal Microbicidal Activity
Bibliographic record
Abstract
NK cells, in addition to possessing antitumor and antiviral activity, exhibit perforin-dependent microbicidal activity against the opportunistic pathogen Cryptococcus neoformans. However, the factors controlling this response, particularly whether the pathogen itself provides an activation or rearming signal, are largely unknown. The current studies were performed to determine whether exposure to this fungus alters subsequent NK cell anticryptococcal activity. NK cells lost perforin and mobilized lysosome-associated membrane protein 1 to the cell surface following incubation with the fungus, indicating that degranulation had occurred. Despite a reduced perforin content during killing, NK cells acquired an enhanced ability to kill C. neoformans, as demonstrated using auxotrophs that allowed independent assessment of the killing of two strains. De novo protein synthesis was required for optimal killing; however, there was no evidence that a soluble factor contributed to the enhanced anticryptococcal activity. Exposure of NK cells to C. neoformans caused the cells to rearm, as demonstrated by increased perforin mRNA levels and enhanced loss of perforin when transcription was blocked. Degranulation alone was insufficient to provide the activation signal as NK cells lost anticryptococcal activity following treatment with strontium chloride. However, NK cells regained the activity upon prolonged exposure to C. neoformans, which is consistent with activation by the microbe. The enhanced cytotoxicity did not extend to tumor killing since NK cells exposed to C. neoformans failed to kill NK-sensitive tumor targets (K562 cells). These studies demonstrate that there is contact-mediated microbe-specific rearming and activation of microbicidal activity that are necessary for optimal killing of C. neoformans.
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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".