Independent Control of <i>Ly49g</i> Alleles: Implications for NK Cell Repertoire Selection and Tumor Cell Killing
Bibliographic record
Abstract
A novel murine NK cell-reactive mAb, AT8, was generated. AT8 recognizes Ly49G from 129/J, BALB/c, and related mouse strains, but does not bind to Ly49G(B6). Costaining with AT8 and a Ly49G(B6)-restricted Ab (Cwy-3) provides the first direct evidence that Ly49G protein is expressed from both alleles on a significant proportion of NK cells from four different types of F(1) hybrid mice. The observed level of biallelic Ly49G expression reproducibly followed the product rule in both freshly isolated and cultured NK cells. Surprisingly, the percentage of NK cells expressing both Ly49G alleles could be dramatically increased in vitro and in vivo through IL-2R- and IFN receptor-dependent signaling pathways, respectively. Unexpectedly, Ly49G(B6+) NK cells in an H-2(d), but not H-2(b), background were more likely to lyse D(d+) and Chinese hamster ovary tumor cells than Ly49G(BALB/129+) NK cells. Furthermore, Ly49G(B6+) NK cells also proliferated to a higher degree in response to poly(I:C) than NK cells expressing a non-Ly49G(B6) allele in an H-2(d), but not H-2(b), background. These results suggest that Ly49G(B6) has a lower affinity for H-2D(d) than Ly49G(BALB/129), and the genetic background calibrates the responsiveness of NK cells bearing self-specific Ly49. Other H-2D(d) receptors on the different Ly49G(+) NK cell subsets were unequally coexpressed, possibly explaining the disparate responses of Ly49G(B6+) NK cells in different hybrid mice. These data indicate that the stochastic mono- and biallelic expression of divergent Ly49G alleles increases the range of MHC affinities and the functional potential in the total NK cell population of heterozygous mice.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".