Pharmacological targeting of β-adrenergic receptor functions abrogates NF-κB signaling and MMP-9 secretion in medulloblastoma cells
Bibliographic record
Abstract
Targeting of the vascular endothelium compartment explains, in part, the therapeutic efficacy of the nonselective β-adrenergic antagonist propranolol against common endothelial tumors such as hemangiomas. In vitro, the antiangiogenic biological activity of propranolol was shown to inhibit human brain microvascular endothelial cell tubulogenesis. However, possible interference of propranolol with cell signaling associated with the tumoral compartment remains unexplored. We therefore assessed the potency of propranolol against a pediatric brain tumor- derived DAOY medulloblastoma cell model. Gene expression of β(1)-, β(2)-, and β(3)-adrenergic receptors was confirmed in DAOY cells by semiquantitative RT-PCR. We next found that propranolol dose-dependently inhibited induction of the key extracellular matrix-degrading and blood-brain barrier disrupting enzyme matrix metalloproteinase- 9 (MMP-9) by phorbol 12-myristate 13-acetate (PMA). Propranolol not only inhibited PMA- induced phosphorylation of the extracellular signal-regulated kinase (Erk), but also that of IkappaB (IκB), preventing the IκB phosphorylation which is a prerequisite for IκB degradation. Propranolol inhibition of IκB phosphorylation was shown to occur with optimal efficacy at 30 μM. Although propranolol, at up to 100 μM, did not affect cell viability, it potentiated PMA- mediated signaling that ultimately led to diminished phosphorylation of Akt. The anti-Erk and anti-Akt phosphorylation effects are both suggestive of antiproliferative and antisurvival signaling, respectively. Our data are therefore indicative of a pharmacological role for propranolol against β-adrenergic receptor signaling functions involving the nuclear factor-kappaB-mediated regulation of MMP-9.
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) | 0.000 | 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".