Characterizing the regulatory role of diacylglycerol acyltransferase 2 (DGAT2) in IgE-activated mast cells 4362
Bibliographic record
Abstract
Abstract Description Mast cells are immune sentinels recognized for their role in allergic inflammation. Allergen-induced FcεRI activation initiates a biphasic response characterized by the rapid degranulation of preformed mediators and the delayed release of cytokines and chemokines. Triacylglycerols (TAG) serve as a source of energy required for cellular functions. Diacylglycerol O-acyltransferase 2 (DGAT2) is an enzyme responsible for the esterification of fatty acids to glycerol during TAG synthesis prior to storage or utilization. Currently, the metabolic requirements of mast cell activation are poorly understood. Therefore, we sought to characterize the importance of TAG as an energy source following allergen activation. IgE-sensitized murine mast cells were treated with the DGAT2 inhibitor JNJ DGAT2-A (JNJ) and stimulated with TNP-BSA and SCF. Following activation, degranulation was measured via β-hexosaminidase release, ELISAs were used to determine cytokine/chemokine release and O2 consumption was measured with a O2k-FluorRespiromoter. Here, DGAT2 inhibition reduced mast cell degranulation (p = 0.02). JNJ treatment significantly reduced the release of IL-6 (p = 0.0002), IL-13 (p = 0.002), TNF (p = 0.0002), CCL1 (p = 0.017) and CCL2 (p = 0.024), independent of any changes to O2 consumption. Together, these results highlight the potential regulatory role of DGAT2 activity during allergen-mediated mast cell activation, making it an intriguing therapeutic target to help combat the allergy epidemic. Funding Sources Canadian Foundation for Innovation (CFI), Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant and Alexandar Graham Bell Canadian Graduate Scholarship (CGS-D), Ontario Government, and Brock University Topic Categories Immediate Hypersensitivity, Asthma, and Allergic Responses (HYP)
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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) | 0.001 | 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".