Intracellular amyloid formation in muscle cells of Aβ-transgenic Caenorhabditis elegans: determinants and physiological role in copper detoxification
Bibliographic record
Abstract
BACKGROUND: The amyloid beta-peptide is a ubiquitous peptide, which is prone to aggregate forming soluble toxic oligomers and insoluble less-toxic aggregates. The intrinsic and external/environmental factors that determine Abeta aggregation in vivo are poorly understood, as well as the cellular meaning of this process itself. Genetic data as well as cell biological and biochemical evidence strongly support the hypothesis that Abeta is a major player in the onset and development of Alzheimer's disease. In addition, it is also known that Abeta is involved in Inclusion Body Myositis, a common myopathy of the elderly in which the peptide accumulates intracellularly. RESULTS: In the present work, we found that intracellular Abeta aggregation in muscle cells of Caenorhabditis elegans overexpressing Abeta peptide is affected by two single amino acid substitutions, E22G (Arctic) and V18A (NIC). Both variations show decrease intracellular amyloidogenesis compared to wild type Abeta. We show that intracellular amyloid aggregation of wild type Abeta is accelerated by Cu2+ and diminished by copper chelators. Moreover, we demonstrate through toxicity and behavioral assays that Abeta-transgenic worms display a higher tolerance to Cu2+ toxic effects and that this resistance may be linked to the formation of amyloid aggregates. CONCLUSION: Our data show that intracellular Abeta amyloid aggregates may trap excess of free Cu2+ buffering its cytotoxic effects and that accelerated intracellular Abeta aggregation may be part of a cell protective mechanism.
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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) | 0.001 | 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".