Modeling the β-secretase cleavage site and humanizing amyloid-beta precursor protein in rat and mouse to study Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Three amino acid differences between rodent and human APP affect medically important features, including β-secretase cleavage of APP and Aβ peptide aggregation (De Strooper et al., EMBO J 14:4932-38, 1995; Ueno et al., Biochemistry 53:7523-30, 2014; Bush, 2003, Trends Neurosci 26:207-14). Most rodent models for Alzheimer's disease (AD) are, therefore, based on the human APP sequence, expressed from artificial mini-genes randomly inserted in the rodent genome. While these models mimic rather well various biochemical aspects of the disease, such as Aβ-aggregation, they are also prone to overexpression artifacts and to complex phenotypical alterations, due to genes affected in or close to the insertion site(s) of the mini-genes (Sasaguri et al., EMBO J 36:2473-87, 2017; Goodwin et al., Genome Res 29:494-505, 2019). Knock-in strategies which introduce clinical mutants in a humanized endogenous rodent APP sequence (Saito et al., Nat Neurosci 17:661-3, 2014) represent useful improvements, but need to be compared with appropriate humanized wildtype (WT) mice. METHODS: , express APP from the endogenous promotor. We also introduced the early-onset familial Alzheimer's disease (FAD) mutation M139T into the endogenous Rat Psen1 gene. RESULTS: We show that introducing these three amino acid substitutions into the rodent sequence lowers the affinity of the APP substrate for BACE1 cleavage. The effect on β-secretase processing was confirmed as both humanized rodent models produce three times more (human) Aβ compared to the original WT strain. These models represent suitable controls, or starting points, for studying the effect of transgenes or knock-in mutations on APP processing (Saito et al., Nat Neurosci 17:661-3, 2014). We introduced the early-onset familial Alzheimer's disease (FAD) mutation M139T into the endogenous Rat Psen1 gene and provide an initial characterization of Aβ processing in this novel rat AD model. CONCLUSION: The different humanized APP models (rat and mouse) expressing human Aβ and PSEN1 M139T are valuable controls to study APP processing in vivo allowing the use of a human Aβ ELISA which is more sensitive than the equivalent system for rodents. These animals will be made available to the research community.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".