The neuroendocrine peptide catestatin promotes clearance of cutaneous Staphylococcus aureus through mast cell Mrgpr activation
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of cutaneous infections, underscoring the need for alternative therapeutic strategies. Catestatin, a neuroendocrine antimicrobial peptide produced by neurons and epithelial cells, has been implicated in skin defense against pathogens such as MRSA, though its mechanisms remain unclear. Here, we show that catestatin expression is upregulated in MRSA-infected skin wounds and that topical catestatin application significantly reduces MRSA burden in infected murine cutaneous wounds. This effect is dependent on the mast cell-specific G protein-coupled receptor Mrgprb2, the murine ortholog of human MRGPRX2. Notably, catestatin treatment leads to Mrgprb2-dependent suppression of inflammatory cytokine production and leukocyte infiltration, alongside upregulation of the antimicrobial peptide Defb14. In human mast cells, catestatin induces MRGPRX2-dependent degranulation, histamine release, prostaglandin D₂ production, and cytokine expression. Pharmacological inhibition and western blot analysis reveal that catestatin activates multiple downstream G protein-dependent signaling pathways in an MRGPRX2-dependent manner. These findings demonstrate that catestatin promotes bacterial clearance by activating mast cells through Mrgprb2, thereby enhancing antimicrobial peptide production. Our study positions catestatin as a promising mast cell-targeting immunotherapeutic candidate for treating antibiotic-resistant skin infections.
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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 | 0.001 |
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".