The Bipyridyl Herbicide Paraquat Induces Proteasome Dysfunction in Human Neuroblastoma SH-SY5Y Cells
Bibliographic record
Abstract
Paraquat (PQ) is suspected to be an environmental risk factor for Parkinson's disease (PD). A strong correlation between exposure to paraquat and the occurrence of PD was reported in Canada, Taiwan, and the United States. This correlation is supported by in vivo work showing that paraquat produces dopaminergic pathogenesis. In particular, paraquat forms abnormal protein aggregates in dopaminergic neurons of mice. However, it is not clear how paraquat produces this pathology. Given that proteasome dysfunction induces aberrant protein aggregation, it was hypothesized that paraquat induces proteasome dysfunction. To explore this possibility, proteasome activity and some factors possibly contributing to proteasome dysfunction were investigated in dopaminergic SY5Y cells treated with paraquat. Furthermore, levels of alpha-synuclein and ubiquitin-conjugated proteins were measured to test whether paraquat induces protein accumulation in SY5Y cells. Results showed that at a concentration of paraquat that reduced viability by about 60% at 48 h (0.5 mM) loss of proteasome activity occurred. In addition, the cells showed decreased ATP levels and reduced mitochondrial complex V activity. These changes were significant 24 h after treatment with paraquat. Furthermore, paraquat-treated cells showed decreased protein levels of proteasome 19S subunits, but not 20S alpha or beta subunits, suggesting that the effects observed were not the result of general cytotoxicity. Paraquat also increased levels of alpha-synuclein and ubiquitinated proteins, suggesting that paraquat-induced proteasome dysfunction leads to aberrant protein accumulation. Taken together, these findings support the hypothesis that paraquat impairs proteasome function in SY5Y cells.
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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) | 0.001 | 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 | 0.001 |
| 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".