Macrophage polarization state affects lipid composition and the channeling of exogenous fatty acids into endogenous lipid pools
Bibliographic record
Abstract
Adipose-tissue-resident macrophages (ATMs) maintain metabolic homeostasis but also contribute to obesity-induced adipose tissue inflammation and metabolic dysfunction. Central to these contrasting effects of ATMs on metabolic homeostasis is the interaction of macrophages with fatty acids. Fatty acid levels are increased within adipose tissue in various pathological and physiological conditions, but appear to initiate inflammatory responses only upon interaction with particular macrophage subsets within obese adipose tissue. The molecular basis underlying these divergent outcomes is likely due to phenotypic differences between ATM subsets, although how macrophage polarization state influences the metabolism of exogenous fatty acids is relatively unknown. Herein, using stable isotope-labeled and nonlabeled fatty acids in combination with mass spectrometry lipidomics, we show marked differences in the utilization of exogenous fatty acids within inflammatory macrophages (M1 macrophages) and macrophages involved in tissue homeostasis (M2 macrophages). Specifically, the accumulation of exogenous fatty acids within triacylglycerols and cholesterol esters is significantly higher in M1 macrophages, while there is an increased enrichment of exogenous fatty acids within glycerophospholipids, ether lipids, and sphingolipids in M2 macrophages. Finally, we show that functionally distinct ATM populations in vivo have distinct lipid compositions. Collectively, this study identifies new aspects of the metabolic reprogramming that occur in distinct macrophage polarization states. The channeling of exogenous fatty acids into particular lipid synthetic pathways may contribute to the sensitivity/resistance of macrophage subsets to the inflammatory effects of increased environmental fatty acid levels.
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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".