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Record W4379053944 · doi:10.1016/j.celrep.2023.112578

Mutant IDH regulates glycogen metabolism from early cartilage development to malignant chondrosarcoma formation

2023· article· en· W4379053944 on OpenAlexafffund
Sinthu Pathmanapan, Raymond Poon, Tomasa Barrientos De Renshaw, Puviindran Nadesan, Makoto Nakagawa, Gireesh Seesankar, Adrian Kwan Ho Loe, Hongyuan H. Zhang, Joan J. Guinovart, Jordi Durán, Christopher B. Newgard, Jay S. Wunder, Benjamin A. Alman

Bibliographic record

VenueCell Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsIsocitrate dehydrogenaseChondrosarcomaIDH1MutantIDH2BiologySomatic cellGlycogenMalignant transformationCancer researchGeneEnzymeGeneticsBiochemistryPathologyMedicine

Abstract

fetched live from OpenAlex

Chondrosarcomas are the most common malignancy of cartilage and are associated with somatic mutations in isocitrate dehydrogenase 1 (IDH1) and IDH2 genes. Somatic IDH mutations are also found in its benign precursor lesion, enchondromas, suggesting that IDH mutations are early events in malignant transformation. Human mutant IDH chondrosarcomas and mutant Idh mice that develop enchondromas investigated in our studies display glycogen deposition exclusively in mutant cells from IDH mutant chondrosarcomas and Idh1 mutant murine growth plates. Pharmacologic blockade of glycogen utilization induces changes in tumor cell behavior, downstream energetic pathways, and tumor burden in vitro and in vivo. Mutant IDH1 interacts with hypoxia-inducible factor 1α (HIF1α) to regulate expression of key enzymes in glycogen metabolism. Here, we show a critical role for glycogen in enchondromas and chondrosarcomas, which is likely mediated through an interaction with mutant IDH1 and HIF1α.

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 score0.169
Threshold uncertainty score0.955

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)0.0000.001

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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