Characterization of Intermediate Steps in Amyloid Beta (Aβ) Production under Near-native Conditions
Bibliographic record
Abstract
Processing of the amyloid precursor protein (APP) by γ-secretase results in generation of Aβ peptides of different lengths ranging from 51 to 30 residues. Accumulation of Aβ and in particular Aβ42 is enhanced by familial Alzheimer disease (FAD) causing mutations in APP and is believed to play a pivotal role. The molecular mechanism underlying normal Aβ production, the impact of FAD mutations on this process and how anti-amyloidogenic γ-secretase modulators (GSMs) cause a selective decrease in Aβ40 and Aβ42 and an increase in shorter Aβ peptides, however, is poorly understood. By using a combined immuno- and LC-MS-based assay we identify several major intermediates, i.e. 3- and 4-peptides that line up head to head across the entire APP transmembrane sequence from Aβ51 to Aβ31/Aβ30 and from Aβ49 to Aβ30/31. FAD APP mutations displayed a relative increase in 3- and 4-peptides from Aβ48 to Aβ38 compared with Aβ49 to Aβ37. These findings correlate with an increase in the Aβ42/40 ratio. GSMs caused a decrease in Aβ40 and Aβ42 and an increase in Aβ37 and Aβ38 paralleled by an increase of the intermediates Aβ40-38 and Aβ42-39. Collectively, these data provide a thorough characterization of all intermediate steps in Aβ production in native cell membranes and provide key mechanistic insights to genetic and pharmacological modulation of Aβ generation.
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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) | 0.001 | 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".