The Mechanism of Membrane Disruption by Cytotoxic Amyloid Oligomers Formed by Prion Protein(106–126) Is Dependent on Bilayer Composition
Bibliographic record
Abstract
The formation of fibrillar aggregates has long been associated with neurodegenerative disorders such as Alzheimer and Parkinson diseases. Although fibrils are still considered important to the pathology of these disorders, it is now widely understood that smaller amyloid oligomers are the toxic entities along the misfolding pathway. One characteristic shared by the majority of amyloid oligomers is the ability to disrupt membranes, a commonality proposed to be responsible for their toxicity, although the mechanisms linking this to cell death are poorly understood. Here, we describe the physical basis for the cytotoxicity of oligomers formed by the prion protein (PrP)-derived amyloid peptide PrP(106-126). We show that oligomers of this peptide kill several mammalian cells lines, as well as mouse cerebellar organotypic cultures, and we also show that they exhibit antimicrobial activity. Physical perturbation of model membranes mimicking bacterial or mammalian cells was investigated using atomic force microscopy, polarized total internal reflection fluorescence microscopy, and NMR spectroscopy. Disruption of anionic membranes proceeds through a carpet or detergent model as proposed for other antimicrobial peptides. By contrast, when added to zwitterionic membranes containing cholesterol-rich ordered domains, PrP(106-126) oligomers induce a loss of domain separation and decreased membrane disorder. Loss of raft-like domains may lead to activation of apoptotic pathways, resulting in cell death. This work sheds new light on the physical mechanisms of amyloid cytotoxicity and is the first to clearly show membrane type-specific modes of action for a cytotoxic peptide.
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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".