DEPTOR loss impairs brown adipocyte development in vitro but has limited impacts in mice
Bibliographic record
Abstract
The mechanistic target of rapamycin (mTOR) is a serine/threonine kinase that regulates growth and metabolism. In mice, activation of mTOR controls cold adaptation by promoting the recruitment and the activation of brown adipose tissue (BAT). DEP-domain containing mTOR-interacting protein (DEPTOR) interacts with mTOR to modulate its activity. Whether DEPTOR levels are modulated by cold in BAT and whether this protein regulates brown adipocyte development and thermogenic activation has never been tested. DEPTOR levels were measured in mouse tissues upon cold exposure and in brown preadipocytes following the induction of adipogenesis. Lentiviruses expressing short-hairpin RNA were used to repress DEPTOR expression in brown preadipocytes in vitro. Conditional deletion of DEPTOR in brown preadipocytes and in mature brown fat cells was achieved by crossing DEPTOR floxed mice with either Myf5-Cre or Ucp1-CreERT2 mice. These animals were exposed to cold and extensively phenotyped. DEPTOR is highly expressed in BAT and its levels are induced by chronic cold exposure, a condition that triggers BAT expansion and activation. Supporting a role for DEPTOR in brown fat cell recruitment, we found that DEPTOR is induced during brown adipocyte development and that its depletion impairs adipogenesis in vitro. This adipogenic lesion was associated with defects in both Akt activation and the expression of key adipogenic regulators. Conditional deletion of DEPTOR in brown preadipocytes or mature brown fat cells did not impact BAT recruitment and thermogenesis in mice but slightly reduced the expression of adipogenic and lipogenic genes. DEPTOR is highly expressed in BAT and its levels are dynamically regulated during brown fat cell development and upon cold exposure. Although DEPTOR depletion severely represses brown fat adipogenesis in vitro, its deletion is dispensable for BAT development, recruitment, and thermogenic activation in mice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".