Human hormone-sensitive lipase (HSL): expression in white fat corrects the white adipose phenotype of HSL-deficient mice
Bibliographic record
Abstract
In white adipose tissue (WAT), hormone-sensitive lipase (HSL) can mediate lipolysis, a central pathway in obesity and diabetes. Gene-targeted HSL-deficient (HSL-/-) mice with no detectable HSL peptide or activity (measured as cholesteryl esterase) have WAT abnormalities, including low mass, marked heterogeneity of cell diameter, increased diacylglycerol content, and low beta-adrenergic stimulation of adipocyte lipolysis. Three transgenic mouse strains preferentially expressing human HSL in WAT were bred to a HSL-/- background. One, HSL-/- N, expresses normal human HSL (41.3 +/- 9.1% of normal activity); two express a serine-to-alanine mutant (S554A) initially hypothesized to be constitutively active: HSL-/- ML, 50.3 +/- 12.3% of normal, and HSL-/- MH, 69.8 +/- 15.8% of normal. In WAT, HSL-/- N mice resembled HSL+/+ controls in WAT mass, histology, diacylglyceride content, and lipolytic response to beta-adrenergic agents. In contrast, HSL-/- ML and HSL-/- MH mice resembled nontransgenic HSL-/- mice, except that diacylglycerol content and perirenal and inguinal WAT masses approached normal in HSL-/- MH mice. Therefore, 1) WAT expression of normal human HSL markedly improves HSL-/- WAT biochemically, physiologically, and morphologically; 2) similar levels of S554A HSL have a low physiological effect despite being active in vitro; and 3) diacylglycerol accumulation is not essential for the development of the characteristic WAT pathology of HSL-/- 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".