An Acidic Microenvironment Increases NK Cell Killing of Cryptococcus neoformans and Cryptococcus gattii by Enhancing Perforin Degranulation
Bibliographic record
Abstract
Cryptococcus gattii and Cryptococcus neoformans are encapsulated yeasts that can produce a solid tumor-like mass or cryptococcoma. Analogous to malignant tumors, the microenvironment deep within a cryptococcoma is acidic, which presents unique challenges to host defense. Analogous to malignant cells, NK cells kill Cryptococcus. Thus, as in tumor defense, NK cells must kill yeast cells across a gradient from physiologic pH to less than 6 in the center of the cryptococcoma. As acidic pH inhibits anti-tumor activities of NK cells, we sought to determine if there was a similar reduction in the anticryptococcal activity of NK cells. Surprisingly, we found that both primary human NK cells and the human NK cell line, YT, have preserved or even enhanced killing of Cryptococcus in acidic, compared to physiological, pH. Studies to explore the mechanism of enhanced killing revealed that acidic pH does not increase the effector to target ratio, binding of cytolytic cells to Cryptococcus, or the active perforin content in effector cells. By contrast, perforin degranulation was greater at acidic pH, and increased degranulation was preceded by enhanced ERK1/2 phosphorylation, which is essential for killing. Moreover, using a replication defective ras1 knockout strain of Cryptococcus increased degranulation occurred during more rapid replication of the organisms. Finally, NK cells were found intimately associated with C. gattii within the cryptococcoma of a fatal infection. These results suggest that NK cells have amplified signaling, degranulation, and greater killing at low pH and when the organisms are replicating quickly, which would help maintain microbicidal host defense despite an acidic microenvironment.
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 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) | 0.000 | 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 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".