Control of proteolysis by norepinephrine and insulin in brown adipocytes: role of ATP, phosphatidylinositol 3-kinase, and p70 S6K
Bibliographic record
Abstract
The objective of this study was to evaluate some of the mechanisms by which norepinephrine (NE) and insulin may influence protein degradation in mouse brown adipocytes differentiated in cultures. The effects of NE and insulin, alone or in combination, on three factors known to influence proteolysis (maintenance of cell ATP and 1-phosphatidylinositol 3-kinase (PI 3-kinase) and p70 ribosomal S6-kinase (p70 S6K) activities) were examined. It was proposed that NE affects proteolysis indirectly by decreasing cell ATP from activation of uncoupling protein-1 (UCP1)-dependent mitochondrial respiration. This was tested by comparing the effects of NE and fatty acids (which directly activate UCP1) on proteolysis in brown adipocytes, as well as in pre-adipocytes and 3T3-L1 adipocytes, which do not express UCP1. An inhibitory effect of insulin on proteolysis is observed in both pre-adipocytes and differentiated cells, whereas NE and exogenously added fatty acids inhibit proteolysis only in brown adipocytes. There is a linear relationship between reductions in cell ATP and proteolysis in response to increasing concentrations of NE or fatty acids. PI 3-kinase activity is required for proteolysis, because two selective inhibitors (wortmannin and LY294002) reduce proteolysis in both pre-adipocytes and differentiated cells. This effect is not additive to that of NE, which suggests they affect the same proteolytic pathway. In contrast to NE, insulin increases PI 3-kinase activity and phosphorylation of p70 S6K. Rapamycin, which prevented insulin-dependent increase in phosphorylation of p70 S6K, increases proteolysis in brown adipocytes and antagonizes the inhibitory effect of insulin on proteolysis, but not the inhibitory effect of NE. Thus, insulin inhibits proteolysis via rapamycin-sensitive activation of p70 S6K, whereas the effect of NE appears largely to be a function of decreasing cell ATP content.
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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) | 0.000 | 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 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".