Evidence for high bi-allelic expression of activating Ly49 receptors
Bibliographic record
Abstract
Stochastic expression is a hallmark of the Ly49 family that encode the main MHC class-I-recognizing receptors of mouse natural killer (NK) cells. This highly polygenic and polymorphic family includes both activating and inhibitory receptor genes and is one of genome's fastest evolving loci. The inhibitory Ly49 genes are expressed in a stochastic mono-allelic manner, possibly under the control of an upstream bi-directional early promoter and show mono-allelic DNA methylation patterns. To date, no studies have directly addressed the transcriptional regulation of the activating Ly49 receptors. Our study shows differences in DNA methylation pattern between activating and inhibitory genes in C57BL/6 and F1 hybrid mouse strains. We also show a bias towards bi-allelic expression of the activating receptors based on allele-specific single-cell RT-PCR in F1 hybrid NK cells for Ly49d and Ly49H expression in Ly49h(+/-) mice. Furthermore, we have identified a region of high sequence identity with possible transcriptional regulatory capacity for the activating Ly49 genes. Our results also point to a likely difference between NK and T-cells in their ability to transcribe the activating Ly49 genes. These studies highlight the complex regulation of this rapidly evolving gene family of central importance in mouse NK cell function.
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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) | 0.003 | 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".