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Record W3045265801 · doi:10.1073/pnas.2005748117

Metformin inhibits RAN translation through PKR pathway and mitigates disease in <i>C9orf72</i> ALS/FTD mice

2020· article· en· W3045265801 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsMcGill University
FundersNational Institute of Neurological Disorders and StrokeALS AssociationMuscular Dystrophy AssociationOffice of Extramural Research, National Institutes of HealthMyotonic Dystrophy FoundationU.S. Department of Defense
KeywordsProtein kinase RRanProtein kinase AMetforminBiologyTranslation (biology)GeneRNA interferenceRNAMedicineCell biologyMessenger RNAKinaseCancer researchGeneticsEndocrinologyMitogen-activated protein kinase kinaseDiabetes mellitus

Abstract

fetched live from OpenAlex

Significance Repeat-associated non-AUG (RAN) proteins accumulate in patient brains and contribute to a growing number of neurodegenerative diseases. There is an urgent need to understand why expression of these proteins does not require canonical or near-cognate AUG start codons and to develop ways to block RAN protein production. We show several types of repeat-expansion RNAs activate the double-stranded RNA-dependent protein kinase (PKR) pathway and that blocking PKR reduces RAN protein levels in cells. PKR is activated in C9orf72 ALS/FTD human and mouse brains and PKR inhibition using AAV-PKR-K296R or the FDA-approved drug metformin decreases RAN protein levels and improves disease in ALS/FTD mice. Targeting PKR using gene therapy or metformin are promising therapeutic approaches for C9orf72 ALS/FTD and other expansion 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.

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 score0.039
Threshold uncertainty score0.215

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 head0.036
GPT teacher head0.283
Teacher spread0.247 · 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