An Epistatic Interaction between Pnpla2 and Lipe Reveals New Pathways of Adipose Tissue Lipolysis
Bibliographic record
Abstract
White adipose tissue (WAT) lipolysis contributes to energy balance during fasting. Lipolysis can proceed by the sequential hydrolysis of triglycerides (TGs) by adipose triglyceride lipase (ATGL), then of diacylglycerols (DGs) by hormone-sensitive lipase (HSL). We showed that the combined genetic deficiency of ATGL and HSL in mouse adipose tissue produces a striking different phenotype from that of isolated ATGL deficiency, inconsistent with the linear model of lipolysis. We hypothesized that the mechanism might be functional redundancy between ATGL and HSL. To test this, the TG hydrolase activity of HSL was measured in WAT. HSL showed TG hydrolase activity. Then, to test ATGL for activity towards DGs, radiolabeled DGs were incubated with HSL-deficient lipid droplet fractions. The content of TG increased, suggesting DG-to-TG synthesis rather than DG hydrolysis. TG synthesis was abolished by a specific ATGL inhibitor, suggesting that ATGL functions as a transacylase when HSL is deficient, transferring an acyl group from one DG to another, forming a TG plus a monoglyceride (MG) that could be hydrolyzed by monoglyceride lipase. These results reveal a previously unknown physiological redundancy between ATGL and HSL, a mechanism for the epistatic interaction between Pnpla2 and Lipe. It provides an alternative lipolytic pathway, potentially important in patients with deficient 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".