Beta cell adaptation to pregnancy requires prolactin action on both beta and non-beta cells
Bibliographic record
Abstract
Abstract Pancreatic islets adapt to insulin resistance of pregnancy by up regulating β-cell mass and increasing insulin secretion. Previously, using a transgenic mouse with global, heterozygous deletion of prolactin receptor (Prlr+/−), we found Prlr signaling is important for this adaptation. However, since Prlr is expressed in tissues outside of islets as well as within islets and prolactin signaling affects β-cell development, to understand β-cell-specific effect of prolactin signaling in pregnancy, we generated a transgenic mouse with an inducible conditional deletion of Prlr from β-cells. Here, we found that β-cell-specific Prlr reduction in adult mice led to elevated blood glucose, lowed β-cell mass and blunted in vivo glucose-stimulated insulin secretion during pregnancy. When we compared gene expression profile of islets from transgenic mice with global (Prlr+/−) versus β-cell-specific Prlr reduction (βPrlR+/−), we found 95 differentially expressed gene, most of them down regulated in the Prlr+/−mice in comparison to the βPrlR+/−mice, and many of these genes regulate apoptosis, synaptic vesicle function and neuronal development. Importantly, we found that islets from pregnant Prlr+/−mice are more vulnerable to glucolipotoxicity-induced apoptosis than islets from pregnant βPrlR+/−mice. These observations suggest that down regulation of prolactin action during pregnancy in non-β-cells secondarily and negatively affect β-cell gene expression, and increased β-cell susceptibility to external insults.
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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 | 0.001 |
| 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".