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Record W3037823855 · doi:10.1073/pnas.1909649117

Inhibition of <i>DUX4</i> expression with antisense LNA gapmers as a therapy for facioscapulohumeral muscular dystrophy

2020· article· en· W3037823855 on OpenAlexafffund
Kenji Rowel Q. Lim, Rika Maruyama, Yusuke Echigoya, Quynh Nguyen, Aiping Zhang, Hunain Khawaja, Sreetama Sen Chandra, Takako I. Jones, Peter L. Jones, Yi-Wen Chen, Toshifumi Yokota

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMuscular Dystrophy CanadaUniversity of Alberta
FundersCanadian Institutes of Health ResearchCanada Foundation for InnovationUniversity of AlbertaMuscular Dystrophy CanadaFriends of FSH ResearchGovernment of CanadaWomen and Children's Health Research InstituteNational Institute of Arthritis and Musculoskeletal and Skin DiseasesFSH SocietyFSHD Global Research Foundation
KeywordsFacioscapulohumeral muscular dystrophyMuscular dystrophyBiologyLocked nucleic acidOligonucleotideMessenger RNAGeneticsMolecular biologyCell biologyComputational biologyGene

Abstract

fetched live from OpenAlex

Significance Facioscapulohumeral dystrophy (FSHD) is an inherited disabling muscular disorder caused by misexpression of DUX4 in skeletal muscles. FSHD has variable onset; its infantile form has a more severe disease course. There is no cure for FSHD. Here, we show the potential of antisense oligonucleotides called locked nucleic acid (LNA) gapmers for treating FSHD. We designed LNA gapmers to knock down DUX4 messenger RNA and found that its expression was effectively reduced in patient-derived cells and an FSHD mouse model. Functional benefits and minimal off-targeting were observed in vitro. Our study facilitates progress toward finding new candidates for treating FSHD. The screening protocol used here for antisense oligonucleotides targeting DUX4 can also be adapted by other efforts developing similar treatments for FSHD.

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 score0.004
Threshold uncertainty score0.192

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 studies0.0000.001
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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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

Citations54
Published2020
Admission routes2
Has abstractyes

Explore more

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