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Record W2908470163 · doi:10.1016/j.isci.2018.12.019

Pluripotent Stem Cell-Based Drug Screening Reveals Cardiac Glycosides as Modulators of Myotonic Dystrophy Type 1

2018· article· en· W2908470163 on OpenAlexfundno aff
Yves Maury, Pauline Poydenot, Benjamin Brinon, Léa Lesueur, Jacqueline Gide, Sylvain Roquevière, Julien Côme, Hélène Polvèche, Didier Auboeuf, Jérôme Alexandre Denis, Geneviève Piétu, Denis Furling, Marc Lechuga, Sandrine Baghdoyan, Marc Peschanski, Cécile Martinat

Bibliographic record

VenueiScience · 2018
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersAFM-TéléthonAgence Nationale de la RechercheAdvanced Foods and Materials CanadaInstitut National de la Santé et de la Recherche MédicaleAssociation Française contre les Myopathies
KeywordsInduced pluripotent stem cellMyotonic dystrophyBiologyRNA splicingStem cellAlternative splicingDrug discoveryMyopathyCell biologyGeneticsRNABioinformaticsGeneMessenger RNAEmbryonic stem cell

Abstract

fetched live from OpenAlex

There is currently no treatment for myotonic dystrophy type 1 (DM1), the most frequent myopathy of genetic origin. This progressive neuromuscular disease is caused by nuclear-retained RNAs containing expanded CUG repeats. These toxic RNAs alter the activities of RNA splicing factors, resulting in alternative splicing misregulation. By combining human mutated pluripotent stem cells and phenotypic drug screening, we revealed that cardiac glycosides act as modulators for both upstream nuclear aggregations of DMPK mRNAs and several downstream alternative mRNA splicing defects. However, these occurred at different drug concentration ranges. Similar biological effects were recorded in a DM1 mouse model. At the mechanistic level, we demonstrated that this effect was calcium dependent and was synergic with inhibition of the ERK pathway. These results further underscore the value of stem-cell-based assays for drug discovery in monogenic diseases.

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 score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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