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Record W1973717479 · doi:10.1038/mtna.2013.41

A Potential New Therapeutic Approach for Friedreich Ataxia: Induction of Frataxin Expression With TALE Proteins

2013· article· en· W1973717479 on OpenAlexaff
Pierre Chapdelaine, Zoé Coulombe, Amina Chikh, Catherine Gérard, Jacques P. Tremblay

Bibliographic record

VenueMolecular Therapy — Nucleic Acids · 2013
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsFrataxinHaploinsufficiencyAtaxiaBiologyTranscription (linguistics)MyoDMolecular biologyGene expressionCancer researchGeneCell biologyGeneticsIron-binding proteinsPhenotype

Abstract

fetched live from OpenAlex

TALEs targeting a promoter sequence and fused with a transcription activation domain (TAD) may be used to specifically induce the expression of a gene as a potential treatment for haploinsufficiency. This potential therapeutic approach was applied to increase the expression of frataxin in fibroblasts of Friedreich ataxia (FRDA) patients. FRDA fibroblast cells were nucleofected with a pCR3.1 expression vector coding for TALEFrat#8 fused with VP64. A twofold increase of the frataxin mRNA (detected by quantitative reverse transcription-PCR (qRT-PCR)) associated with a similar increase of the mature form of the frataxin protein was observed. The frataxin mRNA and protein were also increased by this TALE in the fibroblasts of the YG8R mouse model. The addition of 5-aza-2'-deoxycytidine (5-Aza-dC) or of valproic acid (VPA) to the TALE treatment did not produce significant improvement. Other TADs (i.e., p65, TFAP2α, SRF, SP1, and MyoD) fused with the TALEFrat#8 gene did not produce a significant increase in the frataxin protein. Thus the TALEFrat#8-VP64 recombinant protein targeting the frataxin promoter could eventually be used to increase the frataxin expression and alleviate the FRDA symptoms.Molecular Therapy-Nucleic Acids (2013) 2, e119; doi:10.1038/mtna.2013.41; published online 3 September 2013.

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 categoriesMeta-epidemiology (narrow)
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 score0.234
Threshold uncertainty score1.000

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 head0.021
GPT teacher head0.240
Teacher spread0.218 · 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.

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

Citations27
Published2013
Admission routes1
Has abstractyes

Explore more

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