Autoinsertion of soluble oligomers of Alzheimer's Abeta(1-42) peptide into cholesterol-containing membranes is accompanied by relocation of the sterol towards the bilayer surface.
Bibliographic record
Abstract
BACKGROUND: Soluble Alzheimer's Abeta oligomers autoinsert into neuronal cell membranes, contributing to the pathology of Alzheimer's Disease (AD), and elevated serum cholesterol is a risk factor for AD, but the reason is unknown. We investigated potential connections between these two observations at the membrane level by testing the hypothesis that Abeta(1-42) relocates membrane cholesterol. RESULTS: Oligomers of Abeta(1-42), but not the monomeric peptide, inserted into cholesterol-containing phosphatidylcholine monolayers with an anomalously low molecular insertion area, suggesting concurrent lipid rearrangement. Membrane neutron diffraction, including isomorphous replacement of specific lipid hydrogens with highly-scattering deuterium, showed that Abeta(1-42) insertion was accompanied by outward displacement of membrane cholesterol, towards the polar surfaces of the bilayer. Changes in the generalised polarisation of laurdan confirmed that the structural changes were associated with a functional alteration in membrane lipid order. CONCLUSION: Cholesterol is known to regulate membrane lipid order, and this can affect a wide range of membrane mechanisms, including intercellular signalling. Previously unrecognised Abeta-dependent rearrangement of the membrane sterol could have an important role in AD.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".