Disrupting <scp>BMP</scp> / <scp>TGF</scp> ‐β Signaling: Modulation of <i>AQP1</i> and <i>TGFB1</i> in Human Pulmonary Microvascular Endothelial Cells
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is a chronic disorder with high fatality rates, and its progression is highly associated with the genetic background. Alongside pathogenic variants in genes central to the BMP/TGF-β signaling pathway, recent evidence has linked aquaporin 1 (AQP1) gene variants to PAH. While BMP9 shows promise as a PAH therapy, emerging conflicting evidence challenges this prospect. Herein, we modulated the gene expression of AQP1 and TGFB1 and examined their effect, before and after BMP9 administration, on BMP9, BMP10, BMPR2, AQP1, TGFBR1, and TGFB1 in human pulmonary microvascular endothelial cells (HPMECs) in vitro. Our results demonstrated that silencing of the AQP1 gene resulted in decreased BMPR2 mRNA and protein, downregulated TGFB1 and TGFBR1 mRNA, while tending to reduce TGFBR1 protein levels. BMP9 exogenous administration affected only TGFB1 mRNA, restoring control levels. Silencing of the TGFB1 gene downregulated BMPR2 mRNA and protein levels and affected the expression of its ligands; BMP9 mRNA and protein increased, while BMP10 mRNA levels decreased. Exogenous BMP9 treatment of TGFB1-silenced cells decreased AQP1 mRNA and protein levels. Our results indicate that modulation of AQP1 and TGFB1 genes could possibly disrupt the complex signaling pathway, and that the effects of BMP9 may be cell- and context-dependent. Together, these findings could provide a novel perspective on the interactions of the BMP/TGF-β signaling 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 | 0.001 |
| 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".