Thromboxane Governs the Differentiation of Adipose-Derived Stromal Cells Toward Endothelial Cells In Vitro and In Vivo
Bibliographic record
Abstract
RATIONALE: Autologous adipose-derived stromal cells (ASCs) offer great promise as angiogenic cell therapy for ischemic diseases. Because of their limited self-renewal capacity and pluripotentiality, the therapeutic efficacy of ASCs is still relatively low. Thromboxane has been shown to play an important role in the maintenance of vascular homeostasis. However, little is known about the effects of thromboxane on ASC-mediated angiogenesis. OBJECTIVE: To explore the role of the thromboxane-prostanoid receptor (TP) in mediating the angiogenic capacity of ASCs in vivo. METHODS AND RESULTS: ASCs were prepared from mouse epididymal fat pads and induced to differentiate into endothelial cells (ECs) by vascular endothelial growth factor. Cyclooxygenase-2 expression, thromboxane production, and TP expression were upregulated in ASCs on vascular endothelial growth factor treatment. Genetic deletion or pharmacological inhibition of TP in mouse or human ASCs accelerated EC differentiation and increased tube formation in vitro, enhanced angiogenesis in in vivo Matrigel plugs and ischemic mouse hindlimbs. TP deficiency resulted in a significant cellular accumulation of β-catenin by suppression of calpain-mediated degradation in ASCs. Knockdown of β-catenin completely abrogated the enhanced EC differentiation of TP-deficient ASCs, whereas inhibition of calpain reversed the suppressed angiogenic capacity of TP re-expressed ASCs. Moreover, TP was coupled with Gαq to induce calpain-mediated suppression of β-catenin signaling through calcium influx in ASCs. CONCLUSION: Thromboxane restrained EC differentiation of ASCs through TP-mediated repression of the calpain-dependent β-catenin signaling pathway. These results indicate that TP inhibition could be a promising strategy for therapy utilizing ASCs in the treatment of ischemic diseases.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".