Transcription of platelet-derived growth factor receptor in Leydig cells involves specificity protein 1 and 3
Bibliographic record
Abstract
Platelet-derived growth factor (PDGF) A is secreted by Sertoli cells and acts on Leydig precursor cells, which express the receptor PDGFRA, triggering their differentiation into steroidogenically active Leydig cells. There is, however, no information regarding the molecular mechanisms that govern Pdgfra expression in Leydig cells. In this study, we isolated and characterized a 2.2 kb fragment of the rat Pdgfra 5'-flanking sequence in the TM3 Leydig cell line, which endogenously expresses Pdgfra. A series of 5' progressive deletions of the Pdgfra promoter was generated and transfected in TM3 cells. Using this approach, two regions (-183/-154 and -154/-105), each conferring 46% of Pdgfra promoter activity, were identified. To better define the regulatory elements, trinucleotide mutations spanning the -154/-105 region were introduced by site-directed mutagenesis in the context of the -2.2 kb Pdgfra promoter. Mutations that altered the TCCGAGGGAAAC sequence at -138 bp significantly decreased Pdgfra promoter activity in TM3 cells. Several proteins from TM3 nuclear extracts were found to bind to this G(C/A) motif in electromobility shift assay. Two of the proteins were identified as the transcription factors SP1 and SP3. Using transient transfections of TM3 Leydig cells, SP1 and SP3 were found to activate the Pdgfra promoter by threefold. The SP1/SP3-dependent activation of the Pdgfra promoter was severely blunted when the G(C/A) motif was mutated. Our study provides new insights into the regulatory mechanisms of Pdgfra transcription in Leydig cells, which includes a role for the transcription factors SP1 and SP3.
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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 | 0.001 |
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".