Modulation of Aβ generation by small ubiquitin-like modifiers does not require conjugation to target proteins
Bibliographic record
Abstract
The sequential processing of the APP (amyloid precursor protein) by the beta- and gamma-secretase and generation of the Abeta (amyloid-beta) peptide is a primary pathological factor in AD (Alzheimer's disease). Regulation of the processing or turnover of these proteins represents potential targets for the development of AD therapies. Sumoylation is a process by which SUMOs (small ubiquitin-like modifiers) are covalently conjugated to target proteins, resulting in a number of functional consequences. These include regulation of protein-protein interactions, intracellular trafficking and protein stability, which all have the potential to impact on several aspects of the amyloidogenic pathway. The present study examines the effects of overexpression and knockdown of the major SUMO isoforms (SUMO1, 2 and 3) on APP processing and the production of Abeta peptides. SUMO3 overexpression significantly increased Abeta40 and Abeta42 secretion, which was accompanied by an increase in full-length APP and its C-terminal fragments. These effects of SUMO3 were independent of its covalent attachment or chain formation, as mutants lacking the motifs responsible for SUMO chain formation or SUMO conjugation led to similar changes in Abeta. SUMO3 overexpression also up-regulated the expression of the transmembrane protease BACE (beta-amyloid-cleaving enzyme), but failed to affect levels of several other unrelated proteins. Suppression of SUMO1 or combined SUMO2+3 by RNA interference did not affect APP levels or Abeta production. These findings confirm a specific effect of SUMO3 overexpression on APP processing and the production of Abeta peptides but also suggest that endogenous sumoylation is not essential and likely plays an indirect role in modulating the amyloid processing pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".