Adipose Triglyceride Lipase Deficiency Causes Tissue-specific Changes in Insulin Signaling
Bibliographic record
Abstract
Triacylglycerol accumulation in insulin target tissues is associated with insulin resistance. Paradoxically, mice with global targeted deletion of adipose triglyceride lipase (ATGL), the rate-limiting enzyme in triacylglycerol hydrolysis, display improved glucose tolerance and insulin sensitivity despite triacylglycerol accumulation in multiple tissues. To determine the molecular mechanisms for this phenotype, ATGL-deficient (ATGL(-/-)) and wild-type mice were injected with saline or insulin (10 units/kg, intraperitoneally), and then phosphorylation and activities of key insulin-signaling proteins were determined in insulin target tissues (liver, adipose tissue, and muscle). Insulin signaling and/or glucose transport was also evaluated in isolated adipocytes and skeletal muscle ex vivo. In ATGL(-/-) mice, insulin-stimulated phosphatidylinositol 3-kinase and Akt activities as well as phosphorylation of critical residues of IRS1 (Tyr(P)-612) and Akt (Ser(P)-473) were increased in skeletal muscle in vivo. Insulin-stimulated phosphatidylinositol 3-kinase activity and total insulin receptor and insulin receptor substrate 1, but not other parameters, were also increased in white adipose tissue in vivo. In contrast, in vivo measures of insulin signaling were decreased in brown adipose tissue and liver. Interestingly, the enhanced components of insulin signaling identified in skeletal muscle and white adipose tissue in vivo and their expected downstream effects on glucose transport were not present ex vivo. ATGL deficiency altered intramyocellular lipids as well as serum factors known to influence insulin sensitivity. Thus, skeletal muscle, rather than other tissues, primarily contributes to enhanced insulin sensitivity in ATGL(-/-) mice in vivo despite triacylglycerol accumulation, and both local and systemic factors contribute to tissue-specific effects of global ATGL deficiency on insulin action.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".