Rare Phytocannabinoids Exert Anti-Inflammatory Effects on Human Keratinocytes via the Endocannabinoid System and MAPK Signaling Pathway
Bibliographic record
Abstract
Increasing evidence supports the therapeutic potential of rare cannabis-derived phytocannabinoids (pCBs) in skin disorders such as atopic dermatitis, psoriasis, pruritus, and acne. However, the molecular mechanisms of the biological action of these pCBs remain poorly investigated. In this study, an experimental model of inflamed human keratinocytes (HaCaT cells) was set up by using lipopolysaccharide (LPS) in order to investigate the anti-inflammatory effects of the rare pCBs cannabigerol (CBG), cannabichromene (CBC), Δ9-tetrahydrocannabivarin (THCV) and cannabigerolic acid (CBGA). To this aim, pro-inflammatory interleukins (IL)-1β, IL-8, IL-12, IL-31, tumor necrosis factor (TNF-β) and anti-inflammatory IL-10 levels were measured through ELISA quantification. In addition, IL-12 and IL-31 levels were measured after treatment of HaCaT cells with THCV and CBGA in the presence of selected modulators of endocannabinoid (eCB) signaling. In the latter cells, the activation of 17 distinct proteins along the mitogen-activated protein kinase (MAPK) pathway was also investigated via Human Phosphorylation Array. Our results demonstrate that rare pCBs significantly blocked inflammation by reducing the release of all pro-inflammatory ILs tested, except for TNF-β. Moreover, the reduction of IL-31 expression by THCV and CBGA was significantly reverted by blocking the eCB-binding TRPV1 receptor and by inhibiting the eCB-hydrolase MAGL. Remarkably, THCV and CBGA modulated the expression of the phosphorylated forms (and hence of the activity) of the MAPK-related proteins GSK3β, MEK1, MKK6 and CREB also by engaging eCB hydrolases MAGL and FAAH. Taken together, the ability of rare pCBs to exert an anti-inflammatory effect in human keratinocytes through modifications of eCB and MAPK signaling opens new perspectives for the treatment of inflammation-related skin pathologies.
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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 | 0.000 |
| 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".