Regulation of acetyl-CoA carboxylase
Bibliographic record
Abstract
Acetyl-CoA carboxylase (ACC) catalyses the formation of malonyl-CoA, an essential substrate for fatty acid synthesis in lipogenic tissues and a key regulatory molecule in muscle, brain and other tissues. ACC contributes importantly to the overall control of energy metabolism and has provided an important model to explore mechanisms of enzyme control and hormone action. Mammalian ACCs are multifunctional dimeric proteins (530-560 kDa) with the potential to further polymerize and engage in multiprotein complexes. The enzymatic properties of ACC are complex, especially considering the two active sites, essential catalytic biotin, the three-substrate reaction and effects of allosteric ligands. The expression of the two major isoforms and splice variants of mammalian ACC is tissue-specific and responsive to hormones and nutritional status. Key regulatory elements and cognate transcription factors are still being defined. ACC specific activity is also rapidly modulated, being increased in response to insulin and decreased following exposure of cells to catabolic hormones or environmental stress. The acute control of ACC activity is the product of integrated changes in substrate supply, allosteric ligands, the phosphorylation of multiple serine residues and interactions with other proteins. This review traces the path and implications of studies initiated with Dick Denton in Bristol in the late 1970s, through to current proteomic and other approaches that have been consistently challenging and immensely rewarding.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".