The neuroendocrine peptide catestatin activates mast cell-expressed Mrgprb2 to promote bacterial clearance in skin infections 4047
Bibliographic record
Abstract
Abstract Description Introduction Mast cells serve as sentinel cells in the skin, influencing pathogen clearance, tissue remodeling, and vascular regulation. Our research has revealed a mast cell-expressed receptor, Mrgprb2, and its human homolog, MRGPRX2, which contribute to bacterial clearance and neurogenic skin inflammation. Recently, a variety of neuroimmune mechanisms have been revealed to mediate bacterial infections, but whether neuropeptide activation of mast cells via Mrgprb2/X2 is one of these mechanisms is unknown. We hypothesize that Mrgprb2/X2 are critical for promoting clearance of bacteria from skin infections through communication with immunomodulatory neuropeptides. Results In wildtype mice, treatment of methicillin-resistant Staphylococcus aureus (MRSA) skin infections with catestatin, a cutaneous neuroendocrine peptide, reduced bacterial load by 35% compared to vehicle control at 24 hours-post-infection. Conversely, in Mrgprb2-deficient mice, catestatin had no effect on bacterial load compared to control. Additionally, catestatin induces the release of multiple proinflammatory mediators via MRGPRX2/b2 by stimulating downstream G protein signalling pathways. Furthermore, catestatin facilitates immune cells’ migration via Mrgprb2. Conclusions Catestatin is an agonist for Mrgprb2/X2, facilitating MRSA clearance from skin infections and a new potential therapeutic target to treat pervasive pathogens. Funding Sources This research was funded by the J.P. Bickell Foundation, NSERC (RGPIN-2022-03453), and CFI. Topic Categories Innate Immune Responses and Host Defense: Cellular Mechanisms (INC)
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".