Cannabinoid Receptor-induced Neurite Outgrowth Is Mediated by Rap1 Activation through Gαo/i-triggered Proteasomal Degradation of Rap1GAPII
Bibliographic record
Abstract
The G(alpha)o/i-coupled CB1 cannabionoid receptor induces neurite outgrowth in Neuro-2A cells. The mechanisms of signaling through G(alpha)o/i to induce neurite outgrowth were studied. The expression of G(alpha)o/i reduces the stability of its direct interactor protein, Rap1GAPII, by targeting it for ubiquitination and proteasomal degradation. This results in the activation of Rap1. G(alpha)o/i-induced activation of endogenous Rap1 in Neuro-2A cells is blocked by the proteasomal inhibitor lactacystin. G(alpha)o/i stimulates neurite outgrowth that is blocked by the expression of dominant negative Rap1. Expression of Rap1GAPII also blocks the G(alpha)o/i-induced neurite outgrowth and treatment with proteasomal inhibitors potentiates this inhibition. The endogenous G(alpha)o/i-coupled cannabinoid (CB1) receptor in Neuro-2A cells stimulates the degradation of Rap1GAPII; activation of Rap1 and treatment with pertussis toxin or lactacystin blocks these effects. The CB1 receptor-stimulated neurite outgrowth is blocked by treatment with pertussis toxin, small interfering RNA for Rap, lactacystin, and expression of Rap1GAPII. Thus, the G(alpha)o/i-coupled cannabinoid receptor, by regulating the proteasomal degradation of Rap1GAPII, activates Rap1 to induce neurite outgrowth.
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 | 0.001 |
| 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 | 0.001 | 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".