Anandamide Modulates Thermal Avoidance in Caenorhabditis elegans through Vanilloid and Cannabinoid Receptor Interplay
Bibliographic record
Abstract
Abstract Understanding the endocannabinoid system in C. elegans may offer insights into basic biological processes and potential therapeutic targets for managing pain and inflammation in human. It is well established that anandamide modulates pain perception by binding to cannabinoid and vanilloid receptors, regulating neurotransmitter release and neuronal activity. One objective of this study was to demonstrate the suitability of C. elegans as a model organism for assessing the antinociceptive properties of bioactive compounds and learning about the role of endocannabinoid system in C. elegans. The evaluation of the compound anandamide (AEA) revealed antinociceptive activity by impeding C. elegans nocifensive response to noxious heat. Proteomic and bioinformatic investigations uncovered several pathways activated by AEA. Enrichment analysis unveiled significant involvement of ion homeostasis pathways, which are crucial for maintaining neuronal function and synaptic transmission, suggesting AEA's impact on neurotransmitter release and synaptic plasticity. Additionally, pathways related to translation, protein synthesis, and mTORC1 signaling were enriched, highlighting potential mechanisms underlying AEA's antinociceptive effects. Thermal proteome profiling identified NPR-32 and NPR-19 as primary targets of AEA, along with OCR-2, Cathepsin B, Progranulin, Transthyretin, and ribosomal proteins. These findings suggest a complex interplay between AEA and various cellular processes implicated in nociceptive pathways and inflammation modulation. Further investigation into these interactions could provide valuable insights into the therapeutic potential of AEA and its targets for the management of pain-related conditions.
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".