Thematic Review Series: Glycerolipids. Multiple roles for lipins/phosphatidate phosphatase enzymes in lipid metabolism
Bibliographic record
Abstract
Phosphatidate phosphatase-1 (PAP1) enzymes have a key role in glycerolipid synthesis through the conversion of phosphatidate to diacylglycerol, the immediate precursor of triacylglycerol, phosphatidylcholine, and phosphatidylethanolamine. PAP1 activity in mammals is determined by the lipin family of proteins, lipin-1, lipin-2, and lipin-3, which each have distinct tissue expression patterns and appear to have unique physiological functions. In addition to its role in glycerolipid synthesis, lipin-1 also operates as a transcriptional coactivator, working in collaboration with known nuclear receptors and coactivators to modulate lipid metabolism gene expression. The requirement for different lipin activities in vivo is highlighted by the occurrence of lipodystrophy, insulin resistance, and neuropathy in a lipin-1-deficient mutant mouse strain. In humans, variations in lipin-1 expression levels and gene polymorphisms are associated with insulin sensitivity, metabolic rate, hypertension, and risk for the metabolic syndrome. Furthermore, critical mutations in lipin-2 result in the development of an inflammatory disorder in human patients. A key goal of future studies will be to further elucidate the specific roles and modes of regulation of each of the three lipin proteins in key metabolic processes, including triglyceride and phospholipid synthesis, fatty acid metabolism, and insulin sensitivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.036 |
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".