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Record W2619358118 · doi:10.1016/j.celrep.2017.05.012

2-HG Inhibits Necroptosis by Stimulating DNMT1-Dependent Hypermethylation of the RIP3 Promoter

2017· article· en· W2619358118 on OpenAlexaff
Zhentao Yang, Bin Jiang, Yan Wang, Hengxiao Ni, Jia Zhang, Jin‐Mei Xia, Minggang Shi, Li‐Man Hung, Jingsong Ruan, Tak W. Mak, Qinxi Li, Jiahuai Han

Bibliographic record

VenueCell Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
FundersGuangzhou Institutes of Biomedicine and Health, Chinese Academy of SciencesFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsNecroptosisDNMT1DNA methylationChemistryCancer researchCell biologyBiologyApoptosisBiochemistryProgrammed cell deathGene expressionGene

Abstract

fetched live from OpenAlex

2-hydroxyglutarate-(2-HG)-mediated inhibition of TET2 activity influences DNA hypermethylation in cells harboring mutations of isocitrate dehydrogenases 1 and 2 (IDH1/2). Here, we show that 2-HG also regulates DNA methylation mediated by DNA methyltransferase 1 (DNMT1). DNMT1-dependent hypermethylation of the RIP3 promoter occurred in both IDH1 R132Q knockin mutant mouse embryonic fibroblast (MEFs) and 2-HG-treated wild-type (WT) MEFs. We found that 2-HG bound to DNMT1 and stimulated its association with the RIP3 promoter, inducing hypermethylation that reduces RIP3 protein and consequently impaired RIP3-dependent necroptosis. In human glioma samples, RIP3 protein levels correlated negatively with IDH1 R132H levels. Furthermore, ectopic expression of RIP3 in transformed IDH1-mutated MEFs inhibited the growth of tumors derived from these cells following transplantation into nude mice. Thus, our research sheds light on a mechanism of 2-HG-induced DNA hypermethylation and suggests that impaired necroptosis contributes to the tumorigenesis driven by IDH1/2 mutations.

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 score0.018
Threshold uncertainty score0.437

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations76
Published2017
Admission routes1
Has abstractyes

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