Overexpression of the Insulin-Like Growth Factor II Receptor Increases β-Amyloid Production and Affects Cell Viability
Bibliographic record
Abstract
Amyloid β (Aβ) peptides originating from amyloid precursor protein (APP) in the endosomal-lysosomal compartments play a critical role in the development of Alzheimer's disease (AD), the most common type of senile dementia affecting the elderly. Since insulin-like growth factor II (IGF-II) receptors facilitate the delivery of nascent lysosomal enzymes from the trans-Golgi network to endosomes, we evaluated their role in APP metabolism and cell viability using mouse fibroblast MS cells deficient in the murine IGF-II receptor and corresponding MS9II cells overexpressing the human IGF-II receptors. Our results show that IGF-II receptor overexpression increases the protein levels of APP. This is accompanied by an increase of β-site APP-cleaving enzyme 1 levels and an increase of β- and γ-secretase enzyme activities, leading to enhanced Aβ production. At the cellular level, IGF-II receptor overexpression causes localization of APP in perinuclear tubular structures, an increase of lipid raft components, and increased lipid raft partitioning of APP. Finally, MS9II cells are more susceptible to staurosporine-induced cytotoxicity, which can be attenuated by β-secretase inhibitor. Together, these results highlight the potential contribution of IGF-II receptor to AD pathology not only by regulating expression/processing of APP but also by its role in cellular vulnerability.
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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 | 0.000 |
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".