Maternal obesity alters uterine NK activity through a functional KIR2DL1/S1 imbalance
Bibliographic record
Abstract
In pregnancy, uterine natural killer cells (uNK) play essential roles in coordinating uterine angiogenesis, blood vessel remodeling and promoting maternal tolerance to fetal tissue. Deviances from a normal uterine microenvironment are thought to modify uNK function(s) by limiting their ability to establish a healthy pregnancy. While maternal obesity has become a major health concern due to associations with adverse effects on fetal and maternal health, our understanding into how obesity contributes to poor pregnancy disorders is unknown. Given the importance of uNK in pregnancy, this study examines the impact of obesity on uNK function in women in early pregnancy. We identify that uNK from obese women show a greater propensity for cellular activation, but this difference does not translate into increased effector killing potential. Instead, uNK from obese women express an altered repertoire of natural killer receptors, including an imbalance in inhibitory KIR2DL1 and activating KIR2DS1 receptors that favors HLA-C2-directed uNK activation. Notably, we show that obesity-related KIR2DS1 skewing potentiates TNFα production upon receptor crosslinking. Together, these findings suggest that maternal obesity modifies uNK activity by altering the response toward HLA-C2 antigen and KIR2DL1/2DS1-controlled TNFα release. Furthermore, this work identifies alterations in uNK function resulting from maternal obesity that may impact early developmental processes important in pregnancy 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) | 0.002 | 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".