Phosphorus Overload Promotes Hepatic Lipolysis by Suppressing GSK3β-Dependent Phosphorylation of PPARα at Ser84 and Thr265 in a Freshwater Teleost
Bibliographic record
Abstract
Excessive phosphorus (Pi) contributes to eutrophication in an aquatic environment, which threatens human and fish health. However, the mechanisms by which Pi overload influences aquatic animals remain largely unexplored. In the present study, Pi supplementation increased the Pi content, inhibited lipid accumulation and lipogenesis, and stimulated lipolysis in the liver. Pi supplementation increased the phosphorylation of glycogen synthase kinase-3 β (GSK3β) at serine 9 (S9) but inhibited the phosphorylation of GSK3α at tyrosine 279 (Y279), GSK3β at tyrosine 216 (Y216), and peroxisome proliferator-activated receptor α (PPARα) at serine 84 (S84) and threonine 265 (T265). Pi supplementation also upregulated PPARα protein expression and stimulated its transcriptional activity, thereby inducing lipolysis. Pi suppressed GSK3β activity and prevented GSK3β, but not GSK3α, from interacting with PPARα, which in turn alleviated PPARα phosphorylation. GSK3β-induced phosphorylation of PPARα was dependent on GSK3β S9 dephosphorylation rather than Y216 phosphorylation. Mechanistically, underphosphorylation of PPARα mediated Pi-induced lipid degradation through transcriptionally activating adipose triglyceride lipase ( atgl ) and very long-chain-specific acyl-CoA dehydrogenase ( acadvl ). Collectively, our findings uncovered a new mechanism by which Pi facilitates lipolysis via the GSK3β–PPARα pathway and highlighted the importance of S84 and T265 phosphorylation in PPARα action.
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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 | 0.001 |
| 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 | 0.001 |
| 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".