Bone Morphogenetic Protein 2 Acts via Inhibitor of DNA Binding Proteins to Synergistically Regulate Follicle-Stimulating Hormone β Transcription with Activin A
Bibliographic record
Abstract
We recently reported that bone morphogenetic proteins (BMPs) 2 and 4 can stimulate FSHbeta-subunit (Fshb) transcription alone and in synergy with activins. We further showed that BMP2 signals via the BMP type IA receptor (or activin receptor-like kinase 3) to mediate its effects. However, the intracellular mechanisms through which BMP2 regulates Fshb are unknown. In the current study, we used cDNA microarray analyses (and validation by real-time quantitative RT-PCR) to identify BMP2 target genes in the murine gonadotrope cell line, LbetaT2. Short-interfering RNA-mediated knockdown, overexpression, and coimmunoprecipitation experiments were used to examine the potential functional roles of selected gene products. Quantitative RT-PCR analysis largely confirmed the results of the array analyses, and inhibitors of DNA binding 1, 2, and 3 (Id1, Id2, and Id3) were selected for functional analyses. Knockdown of endogenous Id2 or Id3, but not Id1, diminished the synergistic effects of BMP2 and activin A on Fshb transcription. Overexpression of Id1, Id2, or Id3 alone had no effect, but all three potentiated activin A or mothers against decapentaplegic homolog (SMAD)3 induction of Fshb transcription. Though the precise mechanism through which Ids produce their effects are not yet known, we observed physical interactions between Id1, Id2, or Id3 and SMAD3. Collectively, the data suggest that BMP2 synergistically regulates Fshb transcription with activins, at least in part, through the combined actions of Ids 2 or 3 and SMAD3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".