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Record W3111151965 · doi:10.1002/alz.038980

Optimization of <i>in vitro</i> gene delivery to neurons containing an APP gene with a mutation responsible for familial Alzheimer’s disease for the development of a base editing therapy

2020· article· en· W3111151965 on OpenAlexaff
Jacques P. Tremblay, Antoine Guyon, Rousseau P. Joël

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAmyloid precursor proteinGeneGenome editingMutationCRISPRViral vectorMolecular biologyBiologyGenetic enhancementAmyloid precursor protein secretaseCas9Hippocampal formationGeneticsAlzheimer's diseaseDiseaseRecombinant DNAMedicineNeurosciencePathology

Abstract

fetched live from OpenAlex

Abstract Background The Amyloïd Precursor Protein (APP) is preferentially cut by the α‐secretase enzyme in healthy individuals. However, an abnormal cleavage by β‐secretase leads to the accumulation of β‐amyloid peptides. Numerous APP genetic mutations cause Familial Alzheimer Disease (FAD). However, a rare APP gene variant (A673T) found in Icelanders drastically reduces cleavage by the β‐secretase. We hypothesized that the insertion of this A673T mutation in a patient’s genome using the CRISPR/Cas9 Base‐Editing Technology (Komor et al. Nature 2016) could be an effective method to slow down the progression of familial and sporadic forms of Alzheimer’s disease. The objective of this study is to eventually compare the efficiency by which a lentiviral or a dual‐AAV‐based vector could deliver a base‐editing complex (SpCas9VQR‐Target‐AID) into neurons in vitro. Method Neurons were obtained using two methods. The first method involved differentiating fibroblasts from a FAD patient or a mouse model with a London (V717I) mutation into neurons (Shrigley et al. JoVE 2018) (Figure 1), while the second method involved isolating hippocampal neurons from prenatal NL/F/G mice (Seibenhener et al. JoVE 2012). The dual AAV complex was designed to deliver two parts of the base editor gene separated by inteins (Villiger et al. Nat Med 2018). Hippocampal or induced neurons were transduced with lentivirus or AAV1. Cas9 detection was performed by immunofluorescence and Western Blot. The β‐amyloid peptides in supernatant were characterized and quantified by Meso Scale Discovery’s Aβ kit. Result We have been able to detect the Cas9 protein in neurons by western blot and immunofluorescence (Figure 2). In subsequent experiments, we will determine which delivery method will reduce more the β‐amyloid peptide. Conclusion Our approach aims to confirm the protective effect of the A673T mutation against the development of familial and sporadic forms of Alzheimer’s disease as well as develop an efficient delivery method for the base editing complex which will lead to an in vivo application. Long‐term perspective: We aim to develop a completely new therapeutic approach for AD and FAD using base editing or the new PRIME editing technique (Anzalone et al. Nature 2019). This may eventually lead to a Phase I clinical trial.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.312
Teacher spread0.250 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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