An Unbiased Linkage Approach Reveals That the p53 Pathway Is Coupled to NK Cell Maturation
Bibliographic record
Abstract
Abstract Natural killer cells constitute potent innate lymphoid cells that play a major role in both tumor immunosurveillance and viral clearance via their effector functions. A four-stage model of NK cell functional maturation has been established according to the expression of CD11b and CD27, separating mature NK (mNK) cells into distinct populations that exhibit specific phenotypic and functional properties. To identify genetic factors involved in the regulation of NK cell functional maturation, we performed a linkage analysis on F2 (B6.Rag1−/− × NOD.Rag1−/− intercross) mice. We identified six loci on chromosomes 2, 4, 7, 10, 11, and 18 that were linked to one or more mNK cell subsets. Subsequently, we performed an in silico analysis exploiting mNK cell subset microarray data, highlighting various genes and microRNAs as potential regulators of the functional maturation of NK cells. Together, the combination of our unbiased genetic linkage study and the in silico analysis positions genes known to affect NK cell biology along the specific stages of NK cell functional maturation. Moreover, this approach allowed us to uncover a novel candidate gene in the regulation of NK cell maturation, namely Trp53. Using mice deficient for Trp53, we confirm that this tumor suppressor regulates NK cell functional maturation. Additional candidate genes revealed in this study may eventually serve as targets for the modulation of NK cell functional maturation to potentiate both tumor immunosurveillance and viral clearance.
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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 | 0.001 | 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) | 0.001 | 0.000 |
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".