An activating receptor’s critical role in supporting NK cell proliferation for survival during viral infections (44.2)
Bibliographic record
Abstract
Abstract Activating receptors, including the Ly49H molecule recognizing MCMV, can stimulate NK cell cytotoxicity and proliferation. Ly49H induction of NK cell cytotoxicity contributes to defense against MCMV, but its in vivo importance in stimulating NK cell expansion remains unclear. To define Ly49H’s role in supporting cell proliferation during infection, experiments were carried out evaluating NK cell responses in Ly49h-/-, perforin 1 (Prf1)-/- and wild type (wt) B6 mice. NK cell numbers were similar in uninfected mice, and either deficiency alone resulted in high MCMV burdens. In the absence of Ly49h, however, NK cell numbers declined, with remaining populations having an immature phenotype. In the absence of Prf1, NK cells expanded and had higher rates of proliferation, eccentric phenotypes, and Ly49H expression on nearly all cells. Expansion was abolished, with negative consequences for survival, in mice deficient for both Ly49H and Prf1 (Ly49h-/-Prf1-/-), and NK cell depletions in Prf1-/- mice decreased survival during infection. The results prove that Ly49H can drive dramatic NK cell expansion during challenges with MCMV. Moreover, by dissecting functions for proliferation from cytotoxicity, they demonstrate a previously unappreciated critical role for activating receptors in keeping NK cells present during viral infection. Supported by NIH CA41268 and the Canadian Institutes of Health Research MOP-7781.
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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) | 0.004 | 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".