Coordinate Regulation of Malonyl-CoA Decarboxylase,sn-Glycerol-3-phosphate Acyltransferase, and Acetyl-CoA Carboxylase by AMP-activated Protein Kinase in Rat Tissues in Response to Exercise
Bibliographic record
Abstract
UNLABELLED: Changes in the concentration of malonyl-CoA in many tissues have been related to alterations in the activity of acetyl-CoA carboxylase (ACC), the rate-limiting enzyme in its formation. In contrast, little is known about the physiological role of malonyl-CoA decarboxylase (MCD), an enzyme responsible for malonyl-CoA catabolism. In this study, we examined the effects of voluntary exercise on MCD activity in rat liver, skeletal muscle, and adipose tissue. In addition, the activity of sn-glycerol-3-phosphate acyltransferase (GPAT), which like MCD and ACC can be regulated by AMP-activated protein kinase (AMPK), was assayed. Thirty min after the completion of a treadmill run, MCD activity was increased approximately 2-fold, malonyl-CoA levels were reduced, and ACC and GPAT activities were diminished by 50% in muscle and liver. These events appeared to be mediated via activation of AMPK since: 1) AMPK activity was concurrently increased by exercise in both tissues; 2) similar findings were observed after the injection of 5-amino 4 imidazole carboxamide, an AMPK activator; 3) changes in the activity of GPAT and ACC paralleled that of MCD; and 4) the increase in MCD activity in muscle was reversed in vitro by incubating immunoprecipitated enzyme from the exercised muscle with protein phosphatase 2A, and it was reproduced by incubating immunopurified MCD from resting muscle with purified AMPK. An unexpected finding was that exercise caused similar changes in the activities of ACC, MCD, GPAT, and AMPK and the concentration of malonyl-CoA in adipose tissue. IN CONCLUSION: MCD, GPAT, and ACC are coordinately regulated by AMPK in liver and adipose tissue in response to exercise, and except for GPAT, also in muscle. The results suggest that AMPK activation plays a major role in regulating lipid metabolism in many cells following exercise. They also suggest that in each of them, it acts to increase fatty acid oxidation and decrease its esterification.
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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".