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Record W3136047862 · doi:10.15252/emmm.202013228

Targeted genome editing in vivo corrects a Dmd duplication restoring wild‐type dystrophin expression

2021· article· en· W3136047862 on OpenAlexafffund
Eleonora Maino, Daria Wojtal, Sonia L. Evagelou, Aiman Farheen, Tatianna Wai Ying Wong, Kyle Lindsay, Ori Scott, Samar Z. Rizvi, Elzbieta Hyatt, M. Rok, Shagana Visuvanathan, Amanda Chiodo, Michelle Schneeweiss, Evgueni A. Ivakine, Ronald D. Cohn

Bibliographic record

VenueEMBO Molecular Medicine · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick ChildrenDuchenne Research FundHoward Hughes Medical Institute
KeywordsSick childMedical geneticsGeneticsGenomeMedicineLibrary scienceBiologyPediatricsComputer scienceGene

Abstract

fetched live from OpenAlex

Tandem duplication mutations are increasingly found to be the direct cause of many rare heritable diseases, accounting for up to 10% of cases. Unfortunately, animal models recapitulating such mutations are scarce, limiting our ability to study them and develop genome editing therapies. Here, we describe the generation of a novel duplication mouse model, harboring a multi-exonic tandem duplication in the Dmd gene which recapitulates a human mutation. Duplication correction of this mouse was achieved by implementing a single-guide RNA (sgRNA) CRISPR/Cas9 approach. This strategy precisely removed a duplication mutation in vivo, restored full-length dystrophin expression, and was accompanied by improvements in both histopathological and clinical phenotypes. We conclude that CRISPR/Cas9 represents a powerful tool to accurately model and treat tandem duplication mutations. Our findings will open new avenues of research for exploring the study and therapeutics of duplication disorders.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.742

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 head0.007
GPT teacher head0.278
Teacher spread0.271 · 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 teacher head, 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

Citations32
Published2021
Admission routes2
Has abstractyes

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