Abstract 20520: A New Lysosome-Mediated Degradation Mechanism for Hepatic Lipid Droplets Turnover
Bibliographic record
Abstract
Dietary supplementation with fish oil, rich in n-3 fatty acids such as EPA (20:5n-3), has been shown in many studies to exert a hypotriglyceridemic effect by reducing both plasma triglycerides (in the form of VLDL) and intrahepatic triglycerides (presented in cytoplasm as lipid droplets). To date, no cellular or molecular mechanisms have been defined to explain the hypotriglyceridemic action of n-3 fatty acids. Intrahepatocellular triglycerides are an important contributor to plasma fatty acids and lipoproteins level. We have discovered a direct interaction between lysosome and lipid droplets, in a Kiss-and-run fashion, as the principal mechanism for the lipid droplets turnover upon EPA treatment. Inactivating lysosome function or disrupting lysosome movement resulted in accumulation of lipid droplets under EPA treatment conditions, whereas inactivating the known lipolysis enzymes ATGL or HSL was unable to attenuate droplets turnover. Lipophagy has been reported as a selective autophagic mechanism for degradation of lipid droplets under starvation conditions. However, knockdown of autophagy proteins did not prevent droplets turnover induced by EPA. On the other hand, silencing lysosome-associated GTPase Rab7 or the Rab7 effectors, such as FYCO1 and RILP, entirely blocked EPA-induced droplets turnover. Overexpression of the lysosome-associated Arf-like GTPase Arl8b markedly accelerated the EPA-induced droplets turnover. The current study thus unveiled a new lysosome-mediated lipid degradation mechanism that is responsible for the hypotriglyceridemia action of n-3 fatty acids. The therapeutic potential for the treatment of non-alcoholic hepatosteatosis will be discussed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".