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Record W2745415858 · doi:10.1042/bcj20170469

A novel image-based high-throughput screening assay discovers therapeutic candidates for adult polyglucosan body disease

2017· article· en· W2745415858 on OpenAlexaff
Leonardo J. Solmesky, Netaly Khazanov, Hanoch Senderowitz, Peixiang Wang, Berge A. Minassian, Igor Monteze Ferreira, Wyatt W. Yue, Alexander Lossos, Miguel Weil, Or Kakhlon

Bibliographic record

VenueBiochemical Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicGlycogen Storage Diseases and Myoclonus
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeTel Aviv UniversityWellcome Trust
KeywordsGlycogenGlycogen storage diseaseGlycogen synthaseActivator (genetics)IntracellularGlycogen branching enzymeBiologyBiochemistryMolecular biologyInternal medicineChemistryMedicineGene

Abstract

fetched live from OpenAlex

Glycogen storage disorders (GSDs) are caused by excessive accumulation of glycogen. Some GSDs [adult polyglucosan (PG) body disease (APBD), and Tarui and Lafora diseases] are caused by intracellular accumulation of insoluble inclusions, called PG bodies (PBs), which are chiefly composed of malconstructed glycogen. We developed an APBD patient skin fibroblast cell-based assay for PB identification, where the bodies are identified as amylase-resistant periodic acid-Schiff's-stained structures, and quantified. We screened the DIVERSet CL 10 084 compound library using this assay in high-throughput format and discovered 11 dose-dependent and 8 non-dose-dependent PB-reducing hits. Approximately 70% of the hits appear to act through reducing glycogen synthase (GS) activity, which can elongate glycogen chains and presumably promote PB generation. Some of these GS inhibiting hits were also computationally predicted to be similar to drugs interacting with the GS activator protein phosphatase 1. Our work paves the way to discovering medications for the treatment of PB-involving GSD, which are extremely severe or fatal disorders.

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)0.001
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 score0.338
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.303
Teacher spread0.285 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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