Targeting of Big Stanniocalcin and Its Receptor to Lipid Storage Droplets of Ovarian Steroidogenic Cells
Bibliographic record
Abstract
Stanniocalcin (STC) is a large polypeptide hormone that is widely distributed in tissues such as kidney, adrenal, and ovary. In most tissues, STC exists as a 50-kDa homodimer (STC50). The ovaries produce a higher molecular weight variant (big STC) in androgen-producing theca cell and interstitial cell compartments. Luteal cells, which do not express the STC gene, nonetheless contain high levels of STC protein, suggesting they are targeted by and sequester big STC through a receptor-mediated process. Recently, an STC.alkaline phosphatase fusion protein was used to characterize mitochondrial targeting and sequestration of STC50 and its receptor in liver and kidney. The main objective of the present study was to characterize big STC and its receptor in mammalian ovary and determine whether the ovarian STC variant was similarly targeted to luteal cell mitochondria. By in situ ligand binding, we identified large numbers of STC receptors on corpus luteal cells. However, a more detailed analysis of sub-cellular fractions revealed that both STC and its receptor were not preferentially targeted to mitochondria but instead to cholesterol/lipid storage droplets, which was more indicative of a role in steroidogenesis. Functional studies revealed that additions of big STC had concentration-dependent inhibitory effects on both basal and stimulated progesterone output by primary cultured luteal cells. Furthermore, STC receptor levels were up-regulated in luteal cells in response to protein kinase A activation. Taken together, these findings indicate that theca cell-derived big STC is targeted to the cholesterol/lipid storage droplets of luteal cells to regulate steroidogenesis. This constitutes the first reported description of polypeptide hormone and receptor targeting to cholesterol/lipid droplets and the first biological role for the big STC variant.
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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) | 0.001 | 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".