Tafazzin Modulates Allergen-Induced Mast Cell Inflammatory Mediator Secretion
Bibliographic record
Abstract
Abstract Allergic inflammatory diseases are a steadily growing health concern. Mast cells, a driving force behind allergic pathologies, modulate metabolic pathways to carry out various functions following IgE-FcεRI–mediated activation. Tafazzin (TAZ) is a cardiolipin transacylase that functions to remodel, and thereby mature, cardiolipin, which is important for efficient energy production through oxidative phosphorylation. In this study, we aimed to evaluate the contribution of TAZ in IgE-mediated mast cell activation. Fetal liver-derived mast cells (FLMCs) were differentiated from mice with a doxycycline (dox)-inducible TAZ short hairpin RNA (shRNA) cassette (TAZ shRNA+/+) and littermate wild-types (WTs). TAZ knockdown in FLMCs following dox treatment was confirmed by Western blotting (99.1% by day 5), whereas flow cytometry confirmed FLMC phenotype (c-kit+ FcεRI+) and retention of receptor expression post-dox. Five-day dox-treated WT and TAZ shRNA+/+ FLMCs were activated via allergen-bound IgE cross-linking of FcεRI under stem cell factor potentiation. With dox, and in response to allergen, TAZ shRNA+/+ FLMCs displayed a 25% reduction in oxygen consumption and a significant 31% reduction in mast cell degranulation compared with dox-treated WT FLMCs. Secretion of TNF, CCL1, and CCL2 were significantly reduced, with CCL9 also impaired. Notably, gene expression was not impaired for any inflammatory mediator measured. Functionally, this suggests that TAZ is a contributor to mast cell degranulation and inflammatory mediator secretion. Given unimpacted induced gene expression for mediators measured, we propose that TAZ reduction impairs mast cell exocytosis mechanisms. We thus identify a potential new contributor to immunometabolism that enhances our understanding of mast cell signaling metabolic pathway interactions during allergic inflammation.
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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) | 0.002 | 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".