Hormone-Dependent Expression of a Steroidogenic Acute Regulatory Protein Natural Antisense Transcript in MA-10 Mouse Tumor Leydig Cells
Bibliographic record
Abstract
Cholesterol transport is essential for many physiological processes, including steroidogenesis. In steroidogenic cells hormone-induced cholesterol transport is controlled by a protein complex that includes steroidogenic acute regulatory protein (StAR). Star is expressed as 3.5-, 2.8-, and 1.6-kb transcripts that differ only in their 3'-untranslated regions. Because these transcripts share the same promoter, mRNA stability may be involved in their differential regulation and expression. Recently, the identification of natural antisense transcripts (NATs) has added another level of regulation to eukaryotic gene expression. Here we identified a new NAT that is complementary to the spliced Star mRNA sequence. Using 5' and 3' RACE, strand-specific RT-PCR, and ribonuclease protection assays, we demonstrated that Star NAT is expressed in MA-10 Leydig cells and steroidogenic murine tissues. Furthermore, we established that human chorionic gonadotropin stimulates Star NAT expression via cAMP. Our results show that sense-antisense Star RNAs may be coordinately regulated since they are co-expressed in MA-10 cells. Overexpression of Star NAT had a differential effect on the expression of the different Star sense transcripts following cAMP stimulation. Meanwhile, the levels of StAR protein and progesterone production were downregulated in the presence of Star NAT. Our data identify antisense transcription as an additional mechanism involved in the regulation of steroid biosynthesis.
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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 | 0.001 |
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".