PTH counteracts Hippo signaling via Src-dependent YAP stabilization to enhance bone marrow stromal cell differentiation
Bibliographic record
Abstract
Parathyroid hormone (PTH) regulates serum calcium and phosphate through its actions in bone and kidney and is used to increase bone in osteoporosis treatment. In bone, PTH targets osteoblasts and osteocytes to regulate bone remodeling but also bone marrow stromal cells (BMSCs), regulating their differentiation in the osteoblast or the adipocyte lineage. PTH exerts its action through the PTH/PTH-related peptide (PTHrP) receptor, a G protein-coupled receptor (GPCR), activating adenylyl cyclase and phospholipase C (PLC). Although the effects of cAMP and PKA are well characterized, little is known about the effects of PLC activation or on the crosstalk between PTH signaling and other pathways. Here, bulk RNA-Seq of PTH-treated murine BMSC line (W-20) revealed significant changes in the Hippo pathway. In addition to increasing its transcription, PTH stabilized YAP protein, a key target of Hippo, by decreasing YAP/LArge Tumor Suppressor kinase 1 (LATS1) interaction, YAPS127 phosphorylation, and YAP ubiquitination, leading to YAP nuclear translocation and expression of YAP target genes. Similar events occurred in osteocyte cell lines. This occurred via an increase in Src kinase activity: We identified YAPY428 as a key tyrosine residue phosphorylated by Src in response to PTH. Preventing YAPY428 phosphorylation led to YAP instability, blocking both osteogenic and adipogenic differentiation of W-20 cells. These results demonstrate active crosstalk between the PTH/PTHrP and the Hippo signaling pathways and reveal that PTH signaling utilizes the PLC/Ca2+/Src tyrosine kinase signaling cascade to influence YAP stability, antagonizing Hippo signaling and favoring stromal cell differentiation. Thus, PTH signaling counteracts the effects of Hippo signaling in BMSCs to favor their differentiation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".