The role of adipose triglyceride lipase in lipid and glucose homeostasis: lessons from transgenic mice
Bibliographic record
Abstract
The ability of mammals to store and draw on fat reserves has been a driving force throughout evolution in an environment with intermittent nutrient availability. The discovery of adipose triglyceride lipase (ATGL) as a triglyceride lipase provided a heightened understanding of the mechanisms governing mobilization of fat reserves from adipose tissue. ATGL catalyses the initial step in adipose triglyceride lipolysis, working in concert with other enzymes to mobilize triglyceride for energy production. In addition to the role of ATGL in adipose tissue triglyceride mobilization, ATGL plays crucial roles in regulating lipid homeostasis in other tissues. These roles have been characterized primarily using transgenic mice with tissue-specific ATGL ablation. For example, the global ATGL knockout induces a severe cardiac defect that results in premature mortality that is mimicked by inducible cardiomyocyte-specific ATGL knockout. Global- and adipose-specific ATGL ablation induces a whole-body shift from lipid metabolism to glucose metabolism to satisfy metabolic demand primarily facilitated by an increase in glucose uptake by skeletal muscle. Generation of liver-specific ATGL knockouts has implicated hepatic lipolysis as a critical component of normal liver function. Analysis of β-cell ATGL knockouts implicates the necessity of pancreatic ATGL in insulin secretion. The objective of this review is to discuss the contributions of ATGL to systemic lipid- and glucose-homeostasis discovered through the study of transgenic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.002 |
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".