Random Mutagenesis of Presenilin-1 Identifies Novel Mutants Exclusively Generating Long Amyloid β-Peptides
Bibliographic record
Abstract
Familial Alzheimer disease-causing mutations in the presenilins increase production of longer pathogenic amyloid beta-peptides (A beta(42/43)) by altering gamma-secretase activity. The mechanism underlying this effect remains unknown, although it has been proposed that heteromeric macromolecular complexes containing presenilins mediate gamma-secretase cleavage of the amyloid beta-precursor protein. Using a random mutagenesis screen of presenilin-1 (PS1) for PS1 endoproteolysis-impairing mutations, we identified five unique mutants, including R278I-PS1 and L435H-PS1, that exclusively generated a high level of A beta43, but did not support physiological PS1 endoproteolysis or A beta40 generation. These mutants did not measurably alter the molecular size or subcellular localization of PS1 complexes. Pharmacological studies indicated that the up-regulation of activity for A beta43 generation by these mutations was not further enhanced by the difluoroketone inhibitor DFK167 and was refractory to inhibition by sulindac sulfide. These results suggest that PS1 mutations can lead to a wide spectrum of changes in the activity and specificity of gamma-secretase and that the effects of PS1 mutations and gamma-secretase inhibitors on the specificity are mediated through a common mechanism.
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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) | 0.001 | 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".