High concentrations of progesterone inhibit the expression of genes related to steroid metabolism in MA-10 Leydig cells
Bibliographic record
Abstract
Leydig cells are the main testosterone-producing cells in males. During androgen synthesis, cholesterol enters the mitochondria via the STAR protein and is converted into pregnenolone by the CYP11A1 enzyme. This steroid is then exported from the mitochondria to be metabolized to progesterone by the HSD3B1 enzyme in the endoplasmic reticulum. In this study, we used 3'Tag-RNA-Seq to identify progesterone-regulated genes in MA-10 Leydig cells. Our results indicate that high concentrations of progesterone (30 μM) are involved in a negative feedback loop that inhibits cAMP/PKA-dependent activation of Star and Cyp11a1 expression and participate in cAMP/PKA-dependent down-regulation of genes related to the metabolism of steroid hormones. Linked to activation of the MAPK signaling pathway, endoplasmic reticulum stress and apoptosis, most of the genes encoding bZIP transcription factors are upregulated by progesterone in MA-10 Leydig cells. However, only DDIT3 protein levels are increased in response to progesterone in MA-10 Leydig cells. Like normal Leydig cells, MA-10 cells very weakly express the classical nuclear receptor for progesterone, suggesting that gene regulation by progesterone is rather mediated by one of the non-classical membrane receptors for progesterone However, current findings suggest that the inhibitory effect of progesterone on STAR protein increase in response to forskolin is not dependent on PGRMC1/2 or PAQR9. Furthermore, the increase in progesterone synthesis in response to activation of the cAMP/PKA pathway is rather inhibited by siRNA-mediated knockdown of PAQR9. Overall, this study shows that progesterone produced by Leydig cells participates in the regulation of steroidogenesis through autocrine action involving negative feedback upon activation of the cAMP/PKA pathway.
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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) | 0.003 | 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".