Mitochondrial TNAP controls thermogenesis by hydrolysis of phosphocreatine
Bibliographic record
Abstract
Adaptive thermogenesis has attracted much attention because of its ability to increase systemic energy expenditure and to counter obesity and diabetes1–3. Recent data have indicated that thermogenic fat cells use creatine to stimulate futile substrate cycling, dissipating chemical energy as heat4,5. This model was based on the super-stoichiometric relationship between the amount of creatine added to mitochondria and the quantity of oxygen consumed. Here we provide direct evidence for the molecular basis of this futile creatine cycling activity in mice. Thermogenic fat cells have robust phosphocreatine phosphatase activity, which is attributed to tissue-nonspecific alkaline phosphatase (TNAP). TNAP hydrolyses phosphocreatine to initiate a futile cycle of creatine dephosphorylation and phosphorylation. Unlike in other cells, TNAP in thermogenic fat cells is localized to the mitochondria, where futile creatine cycling occurs. TNAP expression is powerfully induced when mice are exposed to cold conditions, and its inhibition in isolated mitochondria leads to a loss of futile creatine cycling. In addition, genetic ablation of TNAP in adipocytes reduces whole-body energy expenditure and leads to rapid-onset obesity in mice, with no change in movement or feeding behaviour. These data illustrate the critical role of TNAP as a phosphocreatine phosphatase in the futile creatine cycle. Tissue nonspecific alkaline phosphatase (TNAP) within mitochondria hydrolyses phosphocreatine to initiate a futile cycle of creatine dephosphorylation and phosphorylation in thermogenic fat cells.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".