Molecular and Genetic Basis for Strain-Dependent NK1.1 Alloreactivity of Mouse NK Cells
Bibliographic record
Abstract
NK1.1 alloantigen expression can be used to define NK cells in certain mouse strains, such as B6 (NKR-P1C) and SJL (NKR-P1B). However, BALB/c NK cells do not react with the anti-NK1.1 mAb, PK136. To investigate the NK1.1(-) phenotype of BALB/c NK cells, we have undertaken NK1.1 epitope mapping and genomic analysis of the BALB/c Nkrp1 region. Bacterial artificial chromosome library analysis reveals that, unlike the Ly49 region, the Nkrp1-Ocil/Clr region displays limited genetic divergence between B6 and BALB/c mice. In fact, significant divergence is confined to the Nkrp1b and Nkrp1c genes. Strikingly, the B6 Nkrp1d gene appears to represent a divergent allele of the Nkrp1b gene in BALB/c mice and other strains. Importantly, BALB/c NK cells express abundant and functional Nkrp1 transcripts, and the BALB/c NKR-P1B receptor functionally binds Ocil/Clr-b ligand. However, the BALB/c NKR-P1B/C sequences differ from those of the known NK1.1 alloantigens, and epitope mapping demonstrates that directed mutation of a single amino acid in the NKR-P1B(BALB) protein confers NK1.1 reactivity. Thus, PK136 mAb recognizes, in part, a distal C-terminal epitope present in NKR-P1B(Sw/SJL) and NKR-P1C(B6), but absent in NKR-P1A/D/F(B6) and NKR-P1B/C(BALB). Allelic divergence of the Nkrp1b/c gene products and limited divergence of the BALB/c Nkrp1-Ocil/Clr region explain a longstanding confusion regarding the strain-specific NK1.1 alloantigen reactivity of mouse NK cells.
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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) | 0.002 | 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".