Distinctive signalling in NK cells during cytotoxicity to the yeast <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Abstract Introduction Cryptococcus is a fungal pathogen that subverts the adaptive immune response. Fortunately, NK cells are capable of directly recognizing, triggering cytotoxicity and killing Cryptococcus. Although, NK cytotoxicity against tumor and virally infected targets are well studied, less is known about anti-fungal signaling. In this study, we investigated a novel NK cytotoxicity pathway involving Rac, Src family kinases (SFK), and integrins. Methods The involvement of Rac, SFK, integrin-linked kinase (ILK), and integrins were explored using small molecule inhibitors, siRNA knockdown, activated Rac pull down assays, and immunoblots. Results Our studies found that Cryptococcus activated both SFK and Rac signaling pathways. Small molecule inhibitors of SFK and Rac blocked Erk and PI3K activity. siRNA knockdown of Rac also inhibited PI3K activity, suggesting that Rac was an upstream non-canonical activator of PI3K. Moreover, individually Rac and SFK were not sufficient for activation of PI3K. Inhibition of Rac also resulted in a 90% loss of anti-cryptococcal cytotoxicity. Additionally, inhibitors of ILK prevented Rac activation and caused an 80% reduction in cryptococcal killing. Lastly, siRNA knockdown of beta-1 integrins caused an inhibition of ILK activation and 50% reduction in cryptococcal killing. Conclusion We found beta-1 integrins to be a fungal receptor and Rac an activator of PI3K. These findings build a signaling model of anti-fungal killing that is different from the canonical tumor killing model that involves beta-2 integrins and Rac downstream of PI3K. Our model provides various potential therapeutic targets that could enhance fungal clearance mediated by the innate immune system.
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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) | 0.002 | 0.001 |
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".