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Record W3093175098 · doi:10.1186/s13024-020-00399-z

Modeling the β-secretase cleavage site and humanizing amyloid-beta precursor protein in rat and mouse to study Alzheimer’s disease

2020· article· en· W3093175098 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMolecular Neurodegeneration · 2020
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersMedical Research CouncilVlaams Instituut voor BiotechnologieFonds Wetenschappelijk OnderzoekVlaamse regeringEuropean CommissionUK Dementia Research InstituteKU LeuvenAlzheimer SocietyAlzheimer's SocietyFondation pour la Recherche sur AlzheimerAlzheimer’s Research UKAlzheimer's Association
KeywordsAmyloid precursor protein secretaseAmyloid precursor proteinNeurologyDiseaseAmyloid betaCleavage (geology)NeuroscienceAlpha secretaseAlzheimer's diseaseAmyloid βP3 peptideAmyloid (mycology)MedicineBACE1-ASChemistryPathologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.295
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it