Contribution of Adipose Triglyceride Lipase and Hormone-sensitive Lipase to Lipolysis in hMADS Adipocytes
Bibliographic record
Abstract
Lipolysis is the catabolic pathway by which triglycerides are hydrolyzed into fatty acids. Adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL) have the capacity to hydrolyze in vitro the first ester bond of triglycerides, but their respective contributions to whole cell lipolysis in human adipocytes is unclear. Here, we have investigated the roles of HSL, ATGL, and its coactivator CGI-58 in basal and forskolin-stimulated lipolysis in a human white adipocyte model, the hMADS cells. The hMADS adipocytes express the various components of fatty acid metabolism and show lipolytic capacity similar to primary cultured adipocytes. We show that lipolysis and fatty acid esterification are tightly coupled except in conditions of stimulated lipolysis. Immunocytochemistry experiments revealed that acute forskolin treatment promotes HSL translocation from the cytosol to small lipid droplets and redistribution of ATGL from the cytosol and large lipid droplets to small lipid droplets, resulting in enriched colocalization of the two lipases. HSL or ATGL overexpression resulted in increased triglyceride-specific hydrolase capacity, but only ATGL overexpression increased whole cell lipolysis. HSL silencing had no effect on basal lipolysis and only partially reduced forskolin-stimulated lipolysis. Conversely, silencing of ATGL or CGI-58 significantly reduced basal lipolysis and essentially abolished forskolin-stimulated lipolysis. Altogether, these results suggest that ATGL/CGI-58 acts independently of HSL and precedes its action in the sequential hydrolysis of triglycerides in human hMADS adipocytes.
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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 | 0.001 |
| 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".