Loss of hepatic DEPTOR alters the metabolic transition to fasting
Bibliographic record
Abstract
OBJECTIVE: has never been tested. METHODS: We have generated a conditional transgenic mouse allowing the tissue-specific deletion of DEPTOR. This model was crossed with CMV-cre mice or Albumin-cre mice to generate either whole-body or liver-specific DEPTOR knockout (KO) mice. RESULTS: Whole-body DEPTOR KO mice are viable, fertile, normal in size, and do not display any gross physical and metabolic abnormalities. To circumvent possible compensatory mechanisms linked to the early and systemic loss of DEPTOR, we have deleted DEPTOR specifically in the liver, a tissue in which DEPTOR protein is expressed and affected in response to mTOR activation. Liver-specific DEPTOR null mice showed a reduction in circulating glucose upon fasting versus control mice. This effect was not associated with change in hepatic gluconeogenesis potential but was linked to a sustained reduction in circulating glucose during insulin tolerance tests. In addition to the reduction in glycemia, liver-specific DEPTOR KO mice had reduced hepatic glycogen content when fasted. We showed that loss of DEPTOR cell-autonomously increased oxidative metabolism in hepatocytes, an effect associated with increased cytochrome c expression but independent of changes in mitochondrial content or in the expression of genes controlling oxidative metabolism. We found that liver-specific DEPTOR KO mice showed sustained mTORC1 activation upon fasting, and that acute treatment with rapamycin was sufficient to normalize glycemia in these mice. CONCLUSION: We propose a model in which hepatic DEPTOR accelerates the inhibition of mTORC1 during the transition to fasting to adjust metabolism to the nutritional status.
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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) | 0.000 | 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) | 0.002 | 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".