Pregnancy Hyperglycemia in Prolactin Receptor Mutant, but Not Prolactin Mutant, Mice and Feeding-Responsive Regulation of Placental Lactogen Genes Implies Placental Control of Maternal Glucose Homeostasis1
Bibliographic record
Abstract
Pregnancy is often viewed as a conflict between the fetus and mother over metabolic resources. Insulin resistance occurs in mothers during pregnancy but does not normally lead to diabetes because of an increase in the number of the mother's pancreatic beta cells. In mice, this increase is dependent on prolactin (Prl) receptor signaling but the source of the ligand has been unclear. Pituitary-derived Prl is produced during the first half of pregnancy in mice but the placenta produces Prl-like hormones from implantation to term. Twenty-two separate mouse genes encode the placenta Prl-related hormones, making it challenging to assess their roles in knockout models. However, because at least four of them are thought to signal through the Prl receptor, we analyzed Prlr mutant mice and compared their phenotypes with those of Prl mutants. We found that whereas Prlr mutants develop hyperglycemia during gestation, Prl mutants do not. Serum metabolome analysis showed that Prlr mutants showed other changes consistent with diabetes. Despite the metabolic changes, fetal growth was normal in Prlr mutants. Of the four placenta-specific, Prl-related hormones that have been shown to interact with the Prlr, their gene expression localizes to different endocrine cell types. The Prl3d1 gene is expressed by trophoblast giant cells both in the labyrinth layer, sitting on the arterial side where maternal blood is highest in oxygen and nutrients, and in the junctional zone as maternal blood leaves the placenta. Expression increases during the night, though the increase in the labyrinth is circadian whereas it occurs only after feeding in the junctional zone. These data suggest that the placenta has a sophisticated endocrine system that regulates maternal glucose metabolism during pregnancy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".