Peroxisomes Regulate Cellular Free Fatty Acids to Modulate Mast Cell TLR2, TLR4, and IgE-Mediated Activation
Bibliographic record
Abstract
Mast cells are specialized, tissue resident, immune effector cells able to respond to a wide range of stimuli. MCs are involved in the regulation of a variety of physiological functions, including vasodilation, angiogenesis and pathogen elimination. In addition, MCs recruit and regulate the functions of many immune cells such as dendritic cells, macrophages, T cells, B cells and eosinophils through their selective production of multiple cytokines and chemokines. MCs generate and release multi-potent molecules, such as histamine, proteases, prostanoids, leukotrienes, heparin, and many cytokines, chemokines, and growth factors through both degranulation dependent and independent pathways. Recent studies suggested that metabolic shifts dictate the activation and granule content secretion by MCs, however the metabolic signaling promoting these events is at its infancy. Lipid metabolism is recognized as a pivotal immunometabolic regulator during immune cell activation. Peroxisomes are organelles found across all eukaryotes, with a pivotal role in lipid metabolism and the detoxification of reactive oxygen species. Peroxisomes are one of the emerging axes in immunometabolism. Here we identified the peroxisome as an essential player in MCs activation. We determined that lack of functional peroxisomes in murine MCs causes a significant reduction of interleukin-6, Tumor necrosis factor and InterleukinL-13 following immunoglobulin IgE-mediated and Toll like receptor 2 and 4 activation compared to the Wild type (WT) BMMCs. We linked these defects in cytokine release to defects in free fatty acids homeostasis. In conclusion, our study identified the importance of peroxisomal fatty acids homeostasis in regulating mast cell-mediated immune functions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".