Transforming Growth Factor-β1 (TGF-β1) Induces Human Osteoclast Apoptosis by Up-regulating Bim
Bibliographic record
Abstract
Transforming growth factor-beta1 (TGF-beta1) is the most abundant TGF-beta isoform detected in bone and is an important functional modulator of osteoclasts. TGF-beta1 can induce osteoclast apoptosis; however, the apoptotic pathways involved in this process are not known. We show here that human osteoclasts express both type-I and type-II TGF-beta receptors. In the absence of survival factors, TGF-beta1 (1 ng/ml) induced osteoclast apoptosis. The expression of activated caspase-9, but not that of caspase-8, was increased by TGF-beta1 stimulation, and the rate of TGF-beta1-induced apoptosis was significantly lower in the presence of a caspase-9 inhibitor. To study further the mechanisms involved in TGF-beta1-induced osteoclast apoptosis, we investigated TGF-beta1 signaling, which primarily involves the Smad pathway, but also other pathways that may interfere with intracellular modulators of apoptosis, such as mitogen-activated protein (MAP) kinases and Bcl2 family members. We show here that early events consisted of a trend toward increased expression of extracellular signal-regulated kinase (ERK), and then TGF-beta1 significantly induced the activation of p38 and Smad2 in a time-dependent manner. These signaling cascades may activate the intrinsic apoptosis pathway, which involves Bim, the expression of which was increased in the presence of TGF-beta1. Furthermore, the rate of TGF-beta1-induced osteoclast apoptosis was lower when Bim expression was suppressed, and inhibiting the Smad pathway abolished Bim up-regulation following TGF-beta stimulation. This could correspond to a regulatory mechanism involved in the inhibition of osteoclast activity by TGF-beta1.
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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) | 0.002 | 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".