Potentially Pathogenic SORL1 Mutations Observed in Autosomal-Dominant Cases of Alzheimer’s Disease Do Not Modulate APP Physiopathological Processing
Bibliographic record
Abstract
The SORL1 gene encodes LR11/SorLA, a protein that binds β-amyloid precursor protein (APP) and drives its intracellular trafficking. SORL1 mutations, occurring frequently in a subset of familial cases of Alzheimer’s disease (AD), have been documented, but their pathogenic potential is not yet clear and questions remain concerning their putative influence on the physiopathological processing of APP. We have assessed the influence of two SORL1 mutations that were described as likely disease-causing and that were associated with either benign (SorLA924) or severe (SorLA511) AD phenotypes. We examined the influence of wild-type and mutants SorLA in transiently transfected HEK293 cells expressing either wild-type or Swedish mutated APP on APP expression, secreted Aβ and sAPPα levels, intracellular Aβ 40 and Aβ42 peptides, APP-CTFs (C99 and C83) expressions, α-, β- and γ-secretases expressions and activities as well as Aβ and CTFs-degrading enzymes. These paradigms were studied in control conditions or after pharmacological proteasomal modulation. We also established stably transfected CHO cells expressing wild-type SorLA and established the colocalization of APP and either wild-type or mutant SorLA. SorLA mutations partially disrupt co-localization of wild-type sorLA with APP. Overall, although we mostly confirmed previous data concerning the influence of wild-type SorLA on APP processing, we were unable to evidence significant alterations triggered by our set of SorLA mutants, whatever the cells or pharmacological conditions examined. Our study , however, does not rule out the possibility that other AD-linked SORL1 mutations could indeed affect APP processing, and that pathogenic mutations examined in the present study could interfere with other cellular pathways/triggers in AD.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".