Allosteric, transcriptional and post-translational control of mitochondrial energy metabolism
Bibliographic record
Abstract
The heart is the organ with highest energy turnover rate (per unit weight) in our body. The heart relies on its flexible and powerful catabolic capacity to continuously generate large amounts of ATP utilizing many energy substrates including fatty acids, carbohydrates (glucose and lactate), ketones and amino acids. The normal health mainly utilizes fatty acids (40-60%) and glucose (20-40%) for ATP production while ketones and amino acids have a minor contribution (10-15% and 1-2%, respectively). Mitochondrial oxidative phosphorylation is the major contributor to cardiac energy production (95%) while cytosolic glycolysis has a marginal contribution (5%). The heart can dramatically and swiftly switch between energy-producing pathways and/or alter the share from each of the energy substrates based on cardiac workload, availability of each energy substrate and neuronal and hormonal activity. The heart is equipped with a highly sophisticated and powerful mitochondrial machinery which synchronizes cardiac energy production from different substrates and orchestrates the rate of ATP production to accommodate its contractility demands. This review discusses mitochondrial cardiac energy metabolism and how it is regulated. This includes a discussion on the allosteric control of cardiac energy metabolism by short-chain coenzyme A esters, including malonyl CoA and its effect on cardiac metabolic preference. We also discuss the transcriptional level of energy regulation and its role in the maturation of cardiac metabolism after birth and cardiac adaptability for different metabolic conditions and energy demands. The role post-translational modifications, namely phosphorylation, acetylation, malonylation, succinylation and glutarylation, play in regulating mitochondrial energy metabolism is also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".