Skeletal muscle PLIN proteins, ATGL and CGI-58, interactions at rest and following stimulated contraction
Bibliographic record
Abstract
Evidence indicates that skeletal muscle lipid droplet-associated proteins (PLINs) regulate lipolysis through protein-protein interactions on the lipid droplet surface. In adipocytes, PLIN1 is thought to regulate lipolysis by directly 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 fully 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. Our purpose was to examine interactions between PLIN2, PLIN3, and PLIN5, with ATGL and its coactivator CGI-58 at rest and following contraction. Isolated rat solei were incubated for 30 min at rest or during 30 min of intermittent tetanic stimulation [150-ms volleys at 60 Hz with a train rate of 20 tetani/min (25°C)] to maximally stimulate intramuscular lipid breakdown. Results show that the interaction between ATGL and CGI-58 increased 128% following contraction (P = 0.041). Further, ATGL interacts with PLIN2, PLIN3, and PLIN5 at rest and following contraction. The PLIN2-ATGL interaction decreased significantly by 21% following stimulation (P = 0.013). Both PLIN3 and PLIN5 coprecipitated with CGI-58 at rest and following contraction, while there was no detectable interaction between PLIN2 and CGI-58 in either condition. Therefore, our findings indicate that in skeletal muscle, during contraction-induced muscle lipolysis, ATGL and CGI-58 strongly associate and that the PLIN proteins work together to regulate lipolysis, in part, by preventing ATGL and CGI-58 interactions at rest.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".