Fasting upregulates adipose triglyceride lipase and hormone-sensitive lipase levels and phosphorylation in mouse kidney
Bibliographic record
Abstract
Circulating non-esterified fatty acids (NEFA) rise during fasting and are taken up by the kidneys, either directly from the plasma or during re-uptake of albumin from glomerular filtrate, and are stored as triacylglycerol (TAG). Subsequent utilization of stored fatty acids requires their hydrolytic release from cellular lipid droplets, but relatively little is known about renal lipolysis. We found that total [(3)H]triolein hydrolase activity of kidney lysates was significantly increased by 15% in the fasted state. Adipose triglyceride lipase (Atgl) and hormone-sensitive lipase (Hsl) mRNA expression was time-dependently increased by fasting, along with other fatty acid metabolism genes (Pparα, Cd36, and Aox). ATGL and HSL protein levels were also significantly induced (by 239 ± 7% and 322 ± 8%, respectively). Concomitant with changes in total protein levels, there was an increase in ATGL phosphorylation at the AMPK-regulated serine 406 site in the 14-3-3 binding motif, and an increase in HSL phosphorylation at serines 565 and 660 that are regulated by AMPK and PKA, respectively. Using immunofluorescence, we further demonstrate nearly ubiquitous expression of ATGL in the renal cortex with a concentration on the apical/lumenal surface of some cortical tubules. Our findings suggest a role for ATGL and HSL in kidney lipolysis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".