Mast cell function is impaired via MAPK inhibition by Pseudomonas aeruginosa quorum sensing molecules 4528
Bibliographic record
Abstract
Abstract Description Introduction Mast cells are found in tissues adjacent to the external environment like skin, lungs, and intestine, and play a crucial role in host defense. To evade immune responses, bacteria like Pseudomonas aeruginosa, which forms antibiotic-resistant biofilms through quorum sensing, may suppress mast cell function. While P. aeruginosa quorum sensing molecules (QSMs) are known to affect various immune cells, their impact on mast cells remains unclear. We recently identified that mast cells can detect QSMs through surface receptors. We hypothesize that P. aeruginosa QSMs disrupt mast cell immune functions by interfering with receptor-mediated signaling pathways. Aims: 1. Determine if P. aeruginosa QSMs affect FcεRI- and MRGPRX2-mediated mast cell mediator release. 2. To dissect the underlying signaling mechanism impacted by P. aeruginosa QSMs. Results P. aeruginosa QSMs inhibit human mast cell degranulation and histamine release through impairment of FcεRI- and MRGPRX2-induced intracellular calcium mobilization. Moreover, these QSMs attenuate the release of de novosynthesized proinflammatory mediators and prevent PI3K and ERK1/2 phosphorylation in both FceRI- and MRGPRX2-activated human mast cells. Conclusions P. aeruginosa QSMs are inhibitors of FcεRI and MRGPRX2 G protein-coupled receptor pathways of mast cell activation. This work advances our knowledge in crucial areas of host-pathogen interactions and opens new avenues for targeting antimicrobial-resistant pathogens. Funding Sources This research was supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC RGPIN-2022-03453), the Canada Foundation for Innovation, and the College of Biological Science Team Building Grant. Topic Categories Microbial, Parasitic, and Fungal Immunology (MPF)
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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".