Mutant human APP exacerbates pathology in a mouse model of NPC and its reversal by a β-cyclodextrin
Bibliographic record
Abstract
Niemann-Pick type C (NPC) disease, an autosomal recessive disorder caused primarily by loss-of-function mutations in NPC1 gene, is characterized neuropathologically by intracellular cholesterol accumulation, gliosis and neuronal loss in selected brain regions. Recent studies have shown that NPC disease exhibits intriguing parallels with Alzheimer's disease (AD), including the presence of tau-positive neurofibrillary tangles (NFTs) and β-amyloid (Aβ)-related peptides in vulnerable brain regions. Since enhanced cholesterol level, which acts as a risk factor for AD, can increase Aβ production by regulating amyloid precursor protein (APP) metabolism, it is possible that APP overexpression can influence cholesterol-regulated NPC pathology. We have addressed this issue in a novel bigenic mice (ANPC) generated by crossing heterozygous Npc1-deficient mice with mutant human APP transgenic mice. These mice exhibited decreased lifespan, early object memory and motor impairments, and exacerbated glial pathology compared with other littermates. Neurodegeneration observed in the cerebellum of ANPC mice was found to be accelerated along with a selective increase in the phosphorylation/cleavage of tau protein. Additionally, enhanced levels/activity of cytosolic cathepsin D together with cytochrome c and Bcl-2-associated X protein suggest a role for the lysosomal enzyme in the caspase-induced degeneration of neurons in ANPC mice. The reversal of cholesterol accretion by 2-hydroxypropyl-β-cyclodextrin (2-HPC) treatment increased longevity and attenuated behavioral/pathological abnormalities in ANPC mice. Collectively, our results reveal that overexpression of APP in Npc1-deficient mice can negatively influence longevity and a wide spectrum of behavioral/neuropathological abnormalities, thus raising the possibility that APP and NPC1 may interact functionally to regulate the development of AD and NPC pathologies.
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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 | 0.000 | 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) | 0.000 | 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".