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The therapeutic efficacy of cannabinoid receptor type 1 ligands in Huntington's disease may depend on their functional selectivity (846.6)

2014· article· en· W1567996000 on OpenAlexaffabout
Robert B. Laprairie, Amina M. Bagher, Denis J. Dupré, Eileen M. Denovan‐Wright

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCannabinoid receptorEndocannabinoid systemCannabinoidCannabidiolAnandamideAgonistNeuroprotectionReceptorChemistryPharmacologyCannabinoid receptor type 2StriatumNeuroscienceInternal medicineBiologyMedicineBiochemistryDopaminePsychiatry

Abstract

fetched live from OpenAlex

Levels of the type 1 cannabinoid receptor (CB1) decline prior to Huntington’s disease (HD) symptom onset in the striatum and this decline is correlated with disease progression. Consequently, strategies that increase type CB1 expression and activity are being explored as a potential means of treating HD. However, CB1 agonists are biased ligands that differ in their functional selectivity and efficacy. Certain agonists, such as Δ9‐tetrahydrocannabinol (THC), are arrestin‐biased ligands, while other agonists, such as anandamide (AEA), are Gαi/o‐biased ligands. The objective of this study was to determine which CB1 agonists effectively enhanced Gαi/o‐ and Gαq‐dependent neuroprotective signalling and determine whether these agonists promoted survival in a cell culture model of HD striatal neurons. We hypothesized that endocannabinoids, such as AEA and 2‐arachidonylglycerol (2‐AG) would promote neuronal survival in HD cells whereas THC and THC‐like cannabinoids would exacerbate cell death. We used bioluminescence resonance energy transfer (BRET), ELISA, qRT‐PCR, and cytotoxicity assays to examine the efficacy of 6 cannabinoids: AEA, 2‐AG, WIN 55,212‐2, CP 55,940, cannabidiol (CBD), and THC on restoring neuronal function in the mouse STHdh cell culture model of HD striatal neurons that endogenously express CB1. We observed that 2‐AG, AEA, and WIN 55,212‐2 were Gαi/o‐ and αq‐biased ligands that increased CB1 levels and reduced GABA release. CBD was a Gαs‐biased ligand that increased neurotrophic factor levels. THC and CP 55,940 were arrestin2‐biased ligands that enhanced receptor internalization and downregulation. Therapies for HD that aim to enhance or maintain CB1 signaling should exploit the functional selectivity of AEA and 2‐AG. CBD‐derived therapies may also be useful in HD because CBD‐biased CB1 signaling increased the levels of neurotrophic factors and improved neuronal viability. In contrast to AEA, 2‐AG, and CBD, THC‐like compounds may exacerbate CB1 loss in HD. Grant Funding Source : Supported by Canadian Institutes of Health Research (CIHR) and Nova Scotia Health Research (NSHRF)

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.259
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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