Neutralization of Aβ‐amyloid induced toxicity by oligomer 'trapping'
Bibliographic record
Abstract
Objectives The transmembrane sequence (TMS) of APP (Aβ residues 29 to 49) is a key determinant of Aβ42 oligomerization and toxicity, with G33 being the critical residue (Harmeier et al., 2009). A systematic analysis of Aβ‐lipid interactions revealed that residues 29 to 49of the neurotoxic Aβ42 bind more strongly to sphingomyelin (SM), GM1, phosphatidylcholine (PC), and a mixture of neuronal lipid membranes (BTLE) than its non‐toxic counterparts Aβ40 and Aβ42 G33I. As the contact site likely represents a drug target site, potential molecules interfering with Aβ‐lipid interactions should enable the design of amyloid selective therapeutics. Methods Surface plasmon resonance (SPR), Size‐exclusion chromatography (SEC), organotypic slice cultures, MALDI‐MS, electron microscopy, and Drosophila melanogaster as an in vivo model for AD. Results To date, we have found that an eight amino acid peptide (a so‐called “Aβ42‐oligomer interacting peptide” ‐ AIP) with alternating hydrophobic and hydrophilic amino acids neutralizes Aβ42‐induced neurotoxicity by binding to preferentially highly toxic Aβ tetra‐/hexamers. Our data indicates (i) that co‐administration of AIP with Aβ42 prevents the loss of synaptic spine density and rescues long‐term potentiation (LTP) in organotypic slice cultures, and (ii) that the AIP exhibits protective effects in our in vivo model for AD. Conclusions Membrane regions with a high percentage of SM, such as “lipid rafts” could be of particular importance for the interaction with Aβ42 and may play a crucial role in mediating amyloid in vivo toxicity. The development of compounds that interfere with toxic Aβ‐lipid interactions may have a preventive and therapeutic potential.
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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".