Quantitative comparison of presenilin protein expression reveals greater activity of <scp>PS2‐γ‐secretase</scp>
Bibliographic record
Abstract
γ-secretase processing of amyloid precursor protein (APP) has long been of interest in the pathological progression of Alzheimer's disease (AD) due to its role in the generation of amyloid-β. The catalytic component of the enzyme is the presenilins of which there are two homologues, Presenilin-1 (PS1) and Presenilin-2 (PS2). The field has focussed on the PS1 form of this enzyme, as it is typically considered the more active at APP processing. However, much of this work has been completed without appropriate consideration of the specific levels of protein expression of PS1 and PS2. We propose that expression is an important factor in PS1- and PS2-γ-secretase activity, and that when this is considered, PS1 does not have greater activity than PS2. We developed and validated tools for quantitative assessment of PS1 and PS2 protein expression levels to enable the direct comparison of PS in exogenous and endogenous expression systems, in HEK-293 PS1 and/or PS2 knockout cells. We show that exogenous expression of Myc-PS1-NTF is 5.5-times higher than Myc-PS2-NTF. Quantitating endogenous PS protein levels, using a novel PS1/2 fusion standard we developed, showed similar results. When the marked difference in PS1 and PS2 protein levels is considered, we show that compared to PS1-γ-secretase, PS2-γ-secretase has equal or more activity on APP and Notch1. This study has implications for understanding the PS1- and PS2-specific contributions to substrate processing, and their potential influence in AD pathogenesis.
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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 | 0.001 | 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".