NKG2A educates uterine NK cells to optimise pregnancy outcomes in humans and mice
Bibliographic record
Abstract
Abstract The conserved CD94/NKG2A receptor binds HLA-E in humans and Qa-1 in mice. Besides inhibiting natural killer (NK) cell activation, NKG2A drives NK-cell education, a process influenced by HLA-B alleles that promotes NK cell function. In human populations some individuals are genetically programmed to favour NKG2A education and have more robust NK cell function. NKG2A is expressed by nearly all human and roughly half mouse uterine NK cells (uNK), but the importance of NK-cell education in physiology is unknown. Here we show that NKG2A was required for uNK cell-education in dams. Genetic ablation of NKG2A caused sub-optimal vascular responses in pregnancy, increased rate of smaller fetuses, which grew asymmetrically with abnormal brain development, and changes in placental gene expression consistent with stress. These are features of the human syndrome pre-eclampsia. In a genome-wide association study of 7,219 cases and 155,660 control pregnancies, we found that the maternal HLA-B allele that does not favour NKG2A education, was associated with a 7% greater relative risk of pre-eclampsia (P=0.005, OR= 1.07). These results establish the relevance of NK cell education in physiology and show that the maternal HLA-B –> HLA-E –> NKG2A pathway contributes to healthy pregnancy and may influence offspring health.
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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) | 0.001 | 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".