Nuclear targeting of stanniocalcin to mammary gland alveolar cells during pregnancy and lactation
Bibliographic record
Abstract
In most mammalian tissues, the stanniocalcin-1 gene (STC-1) produces a 50-kDa polypeptide hormone known as STC50. Within the ovaries, however, the STC-1 gene generates three higher-molecular-mass variants known as big STC. Big STC is targeted locally to corpus luteal cells to block progesterone release. During pregnancy and lactation, however, ovarian big STC production increases markedly, and the hormone is released into the serum. During lactation, this increase in hormone production is dependent on a suckling stimulus, suggesting that ovarian big STC may have regulatory effects on the lactating mammary gland. In this report, we have addressed this possibility. Our results revealed that virgin mammary tissue contained large numbers of membrane- and mitochondrial-associated STC receptors. However, as pregnancy progressed into lactation, there was a decline in receptor densities on both organelles and a corresponding rise in nuclear receptor density, most of which were on milk-producing, alveolar cells. This was accompanied by nuclear sequestration of the ligand. Sequestered STC resolved as one approximately 135-kDa band in the native state and therefore had the appearance of a big STC variant. However, chemical reduction collapsed this one band into six closely spaced, lower-molecular-mass species (28-41 kDa). Mammary gland STC production also underwent a dramatic shift during pregnancy and lactation. High levels of STC gene expression were observed in mammary tissue from virgin and pregnant rats. However, gene expression then fell to nearly undetectable levels during lactation, coinciding with the rise in nuclear targeting. These findings have thus shown that the mammary glands are indeed targeted by STC, even in the virgin state. They have further shown that there are marked changes in this targeting pathway during pregnancy and lactation, accompanied by a switch in ligand source (endogenous to exogenous). They also represent the first example of nuclear targeting by STC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".