Prolactin-induced AMPK stabilizes alveologenesis and lactogenesis through regulation of STAT5 signaling
Bibliographic record
Abstract
Abstract AMP-activated protein kinase (AMPK) is an evolutionarily conserved serine/threonine kinase that regulates energy homeostasis at cellular and organismal levels. It has been shown to affect several steps of breast cancer progression in a context-dependent manner. However, its role in normal mammary gland development and physiology remains ill-explored. Here, we show that AMPK expression and activity increased within murine mammary epithelia from puberty to pregnancy with highest levels during lactation, and then declined during involution. In ex vivo cultures of mammary epithelial cells (MECs) in organotypic scaffolds, treatment with lactogenic hormone prolactin (PRL) enhanced AMPK expression and activity. To understand the role of AMPK on mammary morphogenesis in vivo , we generated mice with conditional knockout of AMPKα isoforms α1 and α2 (AMPKα KO) in MECs. AMPKα KO mammary glands showed accelerated alveolar development with increased epithelial content of both luminal and myoepithelial lineages, suggestive of hyperproliferation. AMPKα KO mice also showed elevated beta-casein expression during pregnancy and lactation. These observations were phenocopied upon treatment of ex vivo cultivated wild-type MECs with a cognate AMPK inhibitor. AMPKα null MECs showed increased phosphorylated STAT5 which is known to drive alveologenesis downstream of prolactin signaling. Our study identifies a novel interplay between AMPK and PRL-STAT5 signaling that determines mammary alveologenesis and differentiation.
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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".