Carboxyl-terminal Fragments of Alzheimer β-Amyloid Precursor Protein Accumulate in Restricted and Unpredicted Intracellular Compartments in Presenilin 1-deficient Cells
Bibliographic record
Abstract
Absence of functional presenilin 1 (PS1) protein leads to loss of gamma-secretase cleavage of the amyloid precursor protein (betaAPP), resulting in a dramatic reduction in amyloid beta peptide (Abeta) production and accumulation of alpha- or beta-secretase-cleaved COOH-terminal fragments of betaAPP (alpha- or beta-CTFs). The major COOH-terminal fragment (CTF) in brain was identified as betaAPP-CTF-(11-98), which is consistent with the observation that cultured neurons generate primarily Abeta-(11-40). In PS1(-/-) murine neurons and fibroblasts expressing the loss-of-function PS1(D385A) mutant, CTFs accumulated in the endoplasmic reticulum, Golgi, and lysosomes, but not late endosomes. There were some subtle differences in the subcellular distribution of CTFs in PS1(-/-) neurons as compared with PS1(D385A) mutant fibroblasts. However, there was no obvious redistribution of full-length betaAPP or of markers of other organelles in either mutant. Blockade of endoplasmic reticulum-to-Golgi trafficking indicated that in PS1(-/-) neurons (as in normal cells) trafficking of betaAPP to the Golgi compartment is necessary before alpha- and beta-secretase cleavages occur. Thus, although we cannot exclude a specific role for PS1 in trafficking of CTFs, these data argue against a major role in general protein trafficking. These results are more compatible with a role for PS1 either as the actual gamma-secretase catalytic activity or in other functions indirectly related to gamma-secretase catalysis (e.g. an activator of gamma-secretase, a substrate adaptor for gamma-secretase, or delivery of gamma-secretase to betaAPP-containing compartments).
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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 | 0.001 |
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".