Piecing together the puzzle of perilipin proteins and skeletal muscle lipolysis
Bibliographic record
Abstract
The regulation of skeletal muscle lipolysis and fat oxidation is a complex process involving multiple proteins and enzymes. Emerging work indicates that skeletal muscle PLIN proteins likely play a role in the hydrolysis of triglycerides stored in lipid droplets and the passage of fatty acids to the mitochondria for oxidation. In adipocytes, PLIN1 regulates lipolysis by interacting with comparative gene identification-58 (CGI-58), an activator of adipose triglyceride lipase (ATGL). Upon lipolytic stimulation, PLIN1 is phosphorylated, releasing CGI-58 to activate ATGL and initiate triglyceride breakdown. The absence of PLIN1 in skeletal muscle leads us to believe that other PLIN family members undertake this role. The focus of this review is on the PLIN family proteins expressed in skeletal muscle: PLIN2, PLIN3, and PLIN5. To date, most studies involving these PLIN proteins have used nonmuscle tissues and cell cultures to determine their potential roles. Results from work in these models support a role for PLIN proteins in sequestering lipases during basal conditions and in potentially working together for lipase translocation and activity during lipolysis. In skeletal muscle, PLIN2 tends to mirror the lipid content and may play a role in lipid droplet growth and stability through lipase interactions on the lipid droplet surface, whereas the skeletal muscle roles of both PLIN3 and PLIN5 seem to be more complex because they are found not only on the lipid droplet, but also at the mitochondria. Clearly, further work is needed to fully understand the intricate mechanisms by which PLIN proteins contribute to skeletal muscle lipid metabolism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".