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Record W3044156510 · doi:10.1101/2020.07.22.215616

Protective mutation A673T as a potential gene therapy for most forms of APP Familial Alzheimer’s Disease

2020· preprint· en· W3044156510 on OpenAlexafffund
Antoine Guyon, Joël Rousseau, Gabriel Lamothe, Jacques P. Tremblay

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersCentre Hospitalier Universitaire de Québec
KeywordsAmyloid precursor proteinMutationGeneBACE1-ASAmyloid precursor protein secretaseTransfectionBiologyGeneticsMolecular biologyAlzheimer's diseaseP3 peptideDiseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract The accumulation of plaque in the brain leads to the onset and development of Alzheimer’s disease. The Amyloid precursor protein (APP) is usually cut by α-secretase, however an abnormal cleavage profile by β-secretase (BACE1) leads to the accumulation of Aβ peptides, which forms these plaques. Numerous APP gene mutations favor plaque accumulation, causing Familial Alzheimer Disease (FAD). However, a variant of the APP gene (A673T) in Icelanders reduces BACE1 cleavage by 40 %. A library of plasmids containing APP genes with 29 FAD mutations with or without the additional A673T mutation was generated and transfected in neuroblastomas to assess the effect of this mutation on Aβ peptide production. In most cases the production of Aβ peptides was decreased by the co-dominant A673T mutation. The reduction of Aβ peptide concentrations for the London mutation (V717I) even reached the same level as A673T carriers. These results suggest that the insertion of A673T in the APP gene of genetically susceptible FAD patients may prevent the onset of, slow down, or stop the progression of the disease.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.286
Teacher spread0.258 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicAlzheimer's disease research and treatments→French-language works237,207→