In contrast to anti-tumor activity, YT cell and primary NK cell cytotoxicity for Cryptococcus neoformans bypasses LFA-1
Bibliographic record
Abstract
NK cell cytotoxicity requires two positive signals for killing of tumors. Activation receptors induce polarization of the microtubule organization center and degranulation, while leukocyte function-associated antigen (LFA)-1 is required for conjugate formation and actin polymerization and under some circumstances may be sufficient for NK cell cytotoxicity. Although the receptor for direct killing of fungi is not known, CD18, the beta2 chain of LFA-1, binds components of the capsule and cell wall of the opportunistic pathogen Cryptococcus neoformans, namely the polysaccharides glucoronoxylomannan and galactoxylomannan. Herein, we also demonstrate that LFA-1 was concentrated in regions of the NK cell surface interacting with C. neoformans. Consequently, there was compelling evidence to hypothesize that NK cells would also use LFA-1 to recognize and kill C. neoformans. Using a combination of NK cell lines that did or did not express LFA-1 or by using a CD18-specific functional blocking antibody, we confirm that NK cell anti-tumor activity is critically dependent upon the expression of LFA-1. Duplicating the events of tumor cytotoxicity, NK cells form conjugates with cryptococcal targets, rearrange the cell cytoskeleton to develop an NK immunologic synapse and release perforin-containing granules; however, each of these events occurred independently of LFA-1. Furthermore, NK cell-mediated killing of C. neoformans was detectable in both NK cells pre-treated with CD18-blocking antibodies and in NK cells lacking cell surface LFA-1 expression. These results demonstrate that in the absence of LFA-1 expression, NK cells are fully capable of recognizing a target (C. neoformans) and retain all of the events required for cytotoxicity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".