Interference of neuronal TrkB signaling by the cannabis-derived flavonoids cannflavins A and B
Bibliographic record
Abstract
Cannflavins A and B are flavonoids that accumulate in the Cannabis sativa plant. These specialized metabolites are uniquely prenylated and highly lipophilic, which may permit their interaction with membrane-bound enzymes and receptors. Although previous studies found that cannflavins can produce anti-inflammatory responses by inhibiting the biosynthesis of pro-inflammatory mediators, the full extent of their cellular influence remains to be understood. Here, we studied these flavonoids in relation to the Tropomyosin receptor kinase B (TrkB), a receptor tyrosine kinase that is activated by the growth factor brain-derived neurotrophic factor (BDNF). Using mouse primary cortical neurons, we first collected evidence that cannflavins prevent the accumulation of Activity-regulated cytoskeleton-associated (Arc) protein upon TrkB stimulation by exogenous BDNF in these cells. Consistent with this effect, we also observed a reduced activation of TrkB and downstream signaling effectors that mediate Arc mRNA transcription when BDNF was co-applied with the cannflavins. Of note, we also performed a high-throughput screen that demonstrated a lack of agonist action of cannflavins towards 320 different G protein-coupled receptors, a result that specifically limit the possibility of a TrkB transinactivation scenario via G protein signaling to explain our results with dissociated neurons. Finally, we used Neuro2a cells overexpressing TrkB to show that cannflavins can block the growth of neurites and increased survival rate produced by the higher abundance of the receptor in this model. Taken together, our study offers a new path to understand the reported effects of cannflavins and other closely related compounds in different cellular contexts.
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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".