Impact of Amyloid Precursor Protein Hydrophilic Transmembrane Residues on Amyloid-Beta Generation
Bibliographic record
Abstract
Amyloid-β (Aβ) peptides are likely the molecular cause of neurodegeneration observed in Alzheimer's disease. In the brain, Aβ42 and Aβ40 are toxic and the most important proteolytic fragments generated through sequential processing of the amyloid precursor protein (APP) by β- and γ-secretases. Impeding the generation of Aβ42 and Aβ40 is thus considered as a promising strategy to prevent Alzheimer's disease. We therefore wanted to determine key parameters of the APP transmembrane sequence enabling production of these Aβ species. Here we show that the hydrophilicity of amino acid residues G33, T43, and T48 critically determines the generation of Aβ42 and Aβ40 peptides (amino acid numbering according to Aβ nomenclature starting with aspartic acid 1). First, we performed a comprehensive mutational analysis of glycine residue G33 positioned within the N-terminal half of the APP transmembrane sequence by exchanging it against the 19 other amino acids. We found that hydrophilicity of the residue at position 33 positively correlated with Aβ42 and Aβ40 generation. Second, we analyzed two threonine residues at positions T43 and T48 in the C-terminal half of the APP-transmembrane sequence. Replacement of single threonine residues by hydrophobic valines inversely affected Aβ42 and Aβ40 generation. We observed that threonine mutants affected the initial γ-secretase cut, which is associated with levels of Aβ42 or Aβ40. Overall, hydrophilic residues of the APP transmembrane sequence decide on the exact initial γ-cut and the amounts of Aβ42 and Aβ40.
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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".