Manganese-induced cellular disturbance in the baker’s yeast, Saccharomyces cerevisiae with putative implications in neuronal dysfunction
Bibliographic record
Abstract
Abstract Manganese (Mn) is an essential element, but in humans, chronic and/or acute exposure to this metal can lead to neurotoxicity and neurodegenerative disorders including Parkinsonism and Parkinson’s Disease by unclear mechanisms. To better understand the effects that exposure to Mn2+ exert on eukaryotic cell biology, we exposed a non-essential deletion library of the yeast Saccharomyces cerevisiae to a sub-inhibitory concentration of Mn2+ followed by targeted functional analyses of the positive hits. This screen produced a set of 43 sensitive deletion mutants that were enriched for genes associated with protein biosynthesis. Our follow-up investigations demonstrated that Mn reduced total rRNA levels in a dose-dependent manner and decreased expression of a β-galactosidase reporter gene. This was subsequently supported by analysis of ribosome profiles that suggested Mn-induced toxicity was associated with a reduction in formation of active ribosomes on the mRNAs. Altogether, these findings contribute to the current understanding of the mechanism of Mn-triggered cytotoxicity. Lastly, using the Comparative Toxicogenomic Database, we revealed that Mn shared certain similarities in toxicological mechanisms with neurodegenerative disorders including amyotrophic lateral sclerosis, Alzheimer’s, Parkinson’s and Huntington’s diseases.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".