Expression of endogenous mouse APP modulates β-amyloid deposition in hAPP-transgenic mice
Bibliographic record
Abstract
Amyloid-β (Aβ) deposition is one of the hallmarks of the amyloid hypothesis in Alzheimer's disease (AD). Mouse models using APP-transgene overexpression to generate amyloid plaques have shown to model only certain parts of the disease. The extent to which the data from mice can be transferred to man remains controversial. Several studies have shown convincing treatment results in reducing Aβ and enhancing cognition in mice but failed totally in human. One model-dependent factor has so far been almost completely neglected: the endogenous expression of mouse APP and its effects on the transgenic models and the readout for therapeutic approaches.Here, we report that hAPP-transgenic models of amyloidosis devoid of endogenous mouse APP expression (mAPP-knockout / mAPPko) show increased amounts and higher speed of Aβ deposition than controls with mAPP. The number of senile plaques and the level of aggregated hAβ were elevated in mAPPko mice, while the deposition in cortical blood vessels was delayed, indicating an alteration in the general aggregation propensity of hAβ together with endogenous mAβ. Furthermore, the cellular response to Aβ deposition was modulated: mAPPko mice developed a pronounced and age-dependent astrogliosis, while microglial association to amyloid plaques was diminished. The expression of human and murine aggregation-prone proteins with differing amino acid sequences within the same mouse model might not only alter the extent of deposition but also modulate the route of pathogenesis, and thus, decisively influence the study outcome, especially in translational research.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".