Methylmercury can induce Parkinson’s-like neurotoxicity similar to 1-methyl-4- phenylpyridinium: a genomic and proteomic analysis on MN9D dopaminergic neuron cells
Bibliographic record
Abstract
Exposure to environmental chemicals has been implicated as a possible risk factor for the development of neurodegenerative diseases. Our previous study showed that methylmercury (MeHg) exposure can disrupt synthesis, uptake and metabolism of dopamine similar to 1-methyl-4-phenylpyridinium (MPP(+)). The objective of this study was to investigate the effects of MeHg exposure on gene and protein profiles in a dopaminergic MN9D cell line. MN9D cells were treated with MeHg (1-5 μM) and MPP(+) (10-40 μM) for 48 hr. Real-time PCR Parkinson's disease (PD) arrays and high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS) were performed for the analysis. PD PCR array results showed that 19% genes were significantly changed in the 2.5 μM MeHg treated cells, and 39% genes were changed in the 5 μM MeHg treated cells. In comparison, MPP(+) treatment (40 µM) resulted in significant changes in 25% genes. A total of 15 common genes were altered by both MeHg and MPP(+), and dopaminergic signaling transduction was the most affected pathway. Proteomic analysis identified a total of 2496 proteins, of which 188, 233 and 395 proteins were differentially changed by 1 μM and 2.5 μM MeHg, and MPP(+) respectively. A total of 61 common proteins were changed by both MeHg and MPP(+) treatment. The changed proteins were mainly involved in energetic generation-related metabolism pathway (propanoate metabolism, pyruvate metabolism and fatty acid metabolism), oxidative phosphorylation, proteasome, PD and other neurodegenerative disorders. A total of 7 genes/proteins including Ube2l3 (Ubiquitin-conjugating enzyme E2 L3) and Th (Tyrosine 3-monooxygenase) were changed in both genomic and proteomic analysis. These results suggest that MeHg and MPP(+) share many similar signaling pathways leading to the pathogenesis of PD and other neurodegenerative diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".