Activation of HSPA5 contributes to pazopanib-induced hepatotoxicity through <scp>l</scp>-ornithine metabolism pathway and endoplasmic reticulum stress
Bibliographic record
Abstract
OBJECTIVES: The clinical application of Pazopanib (Paz) is often accompanied by hepatotoxicity. However, the mechanisms of hepatic toxicity induced by pazopanib are not entirely clarified. METHODS: Male C57BL/6J mice were treated with pazopanib every day for 2, 4, or 8 weeks. Transcriptomics and metabolomics analyses of liver tissues were performed. In vitro experiments were carried out to estimate cell viability, apoptosis, and autophagy in L02 cells after Paz treatment. We also examined apoptosis and autophagy-related genes under 4-PBA, l-ornithine, nor-NOHA treatments, and HSPA5 knockdown. KEY FINDINGS: Repeated Paz treatment for 8 weeks resulted in more severe hypofunction of the liver in mice. Moreover, Paz treatment inhibited L02 cells cell viability in a dose-dependent manner. We also discovered activation of endoplasmic reticulum stress, apoptosis, and autophagy in Paz-treated L02 cells, as evidenced by the boosted expression of HSPA5, p-IRE1α, ATF4, ATF6, p-eIF2α, LC3, Beclin-1, and a decrease of phosphorylated PI3K, AKT, and mTOR levels. Moreover, 4-PBA, l-ornithine, and HSPA5 knockdown inhibited apoptosis and autophagy, while nor-NOHA weakened the effects of HSPA5 knockdown on apoptosis in Paz-treated L02 cells. CONCLUSIONS: In summary, our study revealed that Paz-induced liver toxicity is related to HSPA5 expression and l-ornithine metabolism pathway in mice.
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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".