Aging modifies the enzymatic activities involved in 2‐arachidonoylglycerol metabolism
Bibliographic record
Abstract
One of the principal monoacylglycerol (MAG) species in animal tissues is 2-arachidonoylglycerol (2-AG), and the diacylglycerol lipase (DAGL) pathway is the most important 2-AG biosynthetic pathway proposed to date. Lysophosphatidate phosphatase (LPAase) activity is part of another 2-AG-forming pathway in which monoacylglycerol lipase (MAGL) is the major degrading enzyme. The purpose of this study was to analyze the manner in which DAGL, LPAase, and MAGL enzymes are modified in the central nervous system (CNS) during aging. To this end, diacylglycerols (DAGs) and MAGs of different composition were used as substrates of DAGL and MAGL, respectively. All enzymatic activities were evaluated in membrane and soluble fractions as well as in synaptic terminals from the cerebral cortex (CC) of adult and aged rats. Results related to 2-AG metabolism show that aging: (a) decreases DAGL-α expression in the membrane fraction whereas in synaptosomes it increases DAGL-β and decreases MAGL expression; (b) decreases LPAase activity in both membrane and soluble fractions; (c) decreases DAGL and stimulates LPAase activities in CC synaptic terminals; (d) stimulates membrane-associated MAGL-coupled DAGL activity; and (e) stimulates MAGL activity in CC synaptosomes. Our results also reveal that during aging the net balance between the enzymatic activities involved in 2-AG synthesis and breakdown is low availability of 2-AG in CC membrane fractions and synaptic terminals. Taken together, our results lead us to conclude that these enzymes play crucial roles in the regulation of 2-AG tissue levels during aging.
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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) | 0.001 | 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".