Abstract 2031 Modulation of the cell membrane lipid milieu by peroxisomes triggers inflammatory responses
Bibliographic record
Abstract
Phagocytosis, receptor-mediated signal transduction, and inflammatory responses require changes in lipid metabolism. Peroxisomes are crucial in fatty acid homeostasis and were recently found to regulate immune function. In Drosophila, we found that macrophages lacking peroxisomes had perturbed phospholipid profiles, which reduced host survival after infection. We used lipidomic, transcriptomic, and genetic screens to determine that peroxisomes contribute to the cell membrane glycerophospholipid composition necessary to activate macrophages. Loss of peroxisome function increased membrane phosphatidic acid (PA), and it unbalanced Diacylglycerol species (DAG) during infection, inhibiting Rho1, TNF pathway, and NFk-B-mediated responses. Peroxisome-glycerophospholipid-mediated signaling also controlled this immune signaling in mouse immune cells. While high levels of PA and DAG in cells without functional peroxisomes inhibited inflammatory phenotypes, many peroxisomes and low concentrations of cell membrane PA and DAG are features of immune cells from patients with the inflammatory disorders, Kawasaki Disease, Juvenile Idiopathic Arthritis, and Familial Mediterranean Fever. Our findings that peroxisomal lipid metabolism contributes to glycerophospholipid signaling to support immune cell function reveal new metabolic markers and potential therapeutic targets for immune diseases, metabolic disorders, and chronic inflammation. This work was funded by a Project Grant from the 2019-20 Establishment Grant (Research Nova Scotia), the CIHR Project grant PJT-169179 and NSERC Discovey Grant RGPIN-2019-04083 to Francesca Di Cara.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".