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Record W2883699765 · doi:10.1016/j.bmcl.2018.07.033

Activating frataxin expression by single-stranded siRNAs targeting the GAA repeat expansion

2018· article· en· W2883699765 on OpenAlexaff
Xiulong Shen, Audrius Kilikevičius, Daniel O’Reilly, Thazha P. Prakash, Masad J. Damha, Frank Rigo, David R. Corey

Bibliographic record

VenueBioorganic & Medicinal Chemistry Letters · 2018
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Science CouncilIonis PharmaceuticalsWelch Foundation
KeywordsFrataxinSmall interfering RNAGene silencingTrinucleotide repeat expansionRNA interferenceChemistryOligonucleotideRNANucleic acidRNA silencingGene expressionGene knockdownCell biologyAtaxiaDecoyMolecular biologyGeneBiologyBiochemistryIron-binding proteinsNeuroscience

Abstract

fetched live from OpenAlex

Friedreich's ataxia (FRDA) is an incurable neurodegenerative disorder caused by reduced expression of the mitochondrial protein frataxin (FXN). The genetic cause of the disease is an expanded GAA repeat within the FXN gene. Agents that increase expression of FXN protein are a potential approach to therapy. We previously described anti-trinucleotide GAA duplex RNAs (dsRNAs) and antisense oligonucleotides (ASOs) that activate FXN protein expression in multiple patient derived cell lines. Here we test two distinct series of compounds for their ability to increase FXN expression. ASOs with butane linkers showed low potency, which is consistent with the low T m values and suggesting that flexible conformation impairs activity. By contrast, single-stranded siRNAs (ss-siRNAs) that combine the strengths of dsRNA and ASO approaches had nanomolar potencies. ss-siRNAs provide an additional option for developing nucleic acid therapeutics to treat FRDA.

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 score0.003
Threshold uncertainty score0.011

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.241
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 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

Citations21
Published2018
Admission routes1
Has abstractyes

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