Crosstalk between arachidonic acid metabolism and glycolysis drives integrated metabolic-inflammatory reprogramming in macrophages
Bibliographic record
Abstract
Arachidonic acid (AA)-derived lipid mediators play pivotal roles in inflammation and its resolution.While glycolysis is a key metabolic pathway determining macrophage polarization, the crosstalk between specific AA metabolites and glycolytic reprogramming remains poorly understood.In this study, we explore whether certain AA metabolites modulate macrophage function through covalent protein modification, with therapeutic implications for myocardial ischemia-reperfusion injury.Unlike conventional specialized pro-resolving mediators (SPMs) that primarily act via receptors, here we identify an endogenous electrophilic AA metabolite, 15-keto-prostaglandin F2α (15KPF), that covalently modifies pyruvate kinase M2 (PKM2) at Cys49.Such interaction enhanced PKM2 tetramerization, suppressed the PKM2/HIF-1α/STAT3 axis, redirected energy metabolism from glycolysis to mitochondrial respiration, and promoted pro-resolving M2 macrophage polarization.Mutated PKM2(C49S) failed to inhibit STAT3 signaling and blocked the effect of 15KPF on M1 to M2 phenotype switch.Moreover, 15KPF reduced infarct size and preserved myocardial integrity in in vivo model.Taken together, covalent 15-keto-PGF2α-PKM2 conjugation represents a self-regulatory mechanism linking AA metabolism to glycolysis to drive macrophage metabolic-inflammatory reprogramming.This pathway positions 15KPF as a promising therapeutic candidate for inflammatory and metabolic diseases, including ischemia-reperfusion injury, and distinguishes it from synthetic allosteric PKM2 activators such as TEPP-46.
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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".