Therapeutic effect of an MRGPRX2/MRGPRB2 antagonist on LL-37-induced rosacea-like inflammation in mice
Bibliographic record
Abstract
INTRODUCTION: Rosacea is a chronic inflammatory skin disorder characterized by symptoms like itching, redness, and impaired skin barrier function. Mast cell activation plays a crucial role in its pathogenesis. Recent evidence shows higher expression of mast cell receptor MRGPRX2/MRGPRB2 in rosacea patients' skin tissues and its potential as a novel drug target. We evaluated the therapeutic effect of a novel small-molecule MRGPRX2/MRGPRB2 antagonist in a mouse model of rosacea and itch. METHODS: The therapeutic effects of GE1111 were evaluated in vivo on wildtype and MRGPRB2 knock-out mice with LL-37-induced rosacea. Serum MCP-1 level and histochemistry measured inflammation and mast cell degranulation in skin tissue. Functional in vitro cell culture assays were developed using MRGPRX2/MRGPRB2 agonist LL-37, mast cells, keratinocytes, and macrophage cell lines. RESULTS: LL-37-treated mice showed redness, increased serum MCP-1, and epidermal thickness of skin tissue, while these changes were absent in LL-37-treated MRGPRB2 knock-out mice. Treatment with GE1111 reduced rosacea symptoms, epidermal thickness, and serum MCP-1 levels. GE1111 protected tight junction protein expression and reduced mast cell degranulation and inflammatory cytokine gene and protein expression in skin lesions. GE1111 treatment reduced the number and duration of itch in the compound 48/80 induced itch model. In vitro evidence showed GE1111's mechanism by inhibiting inflammatory interaction of mast cells with keratinocytes and macrophages. CONCLUSION: GE1111 showed promising therapeutic effects in rosacea via targeting interactions between mast cells, keratinocytes, and macrophages and inhibiting inflammatory cytokines. These findings open possibilities for developing MRGPRX2/MRGPRB2 antagonists as novel treatments for rosacea.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".