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Record W2039147478 · doi:10.1210/en.2010-1162

Hypermethylation of Hepatic Glucokinase and L-Type Pyruvate Kinase Promoters in High-Fat Diet–Induced Obese Rats

2011· article· en· W2039147478 on OpenAlexfundno aff
Minghong Jiang, Yuhao Zhang, Meng Liu, Michael S. Lan, Fei Jing, Weiwei Fan, Xin Gao, Daru Lu

Bibliographic record

VenueEndocrinology · 2011
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersState Key Laboratory of Genetic EngineeringInstitute of GeneticsFudan University
KeywordsGlucokinaseInternal medicineEndocrinologyPyruvate kinasePromoterDNA methylationChemistryBiologyMedicineMetabolismGeneBiochemistryGlycolysisInsulinGene expression

Abstract

fetched live from OpenAlex

Obesity-dependent insulin resistance and type 2 diabetes mellitus are closely associated with decreased glucose utilization and down-regulation of hepatic glycolytic enzymes expression. Previously, we showed that DNA hypermethylation is involved in age-dependent susceptibility to hepatic insulin resistance and diabetes. However, what we cannot distinguish is whether the age-related obesity contributes to DNA hypermethylation in those natural aging rats. In the present study, we hypothesize that DNA methylation plays a crucial role in the regulation of glycolytic enzymes in the high-fat diet-induced obesity. Here, we report that DNA hypermethylation is correlated with a decline in hepatic glucokinase (Gck) and L-type pyruvate kinase (LPK) expression in high-fat diet-induced obese rats as compared with the control diet group. Down-regulation of Gck and LPK expression are reversed by the 5-aza-2'-deoxycytidine in the cell model of steatosis. These novel observations indicate that DNA methylation is involved in the development of metabolic diseases, such as obesity, insulin resistance, type 2 diabetes mellitus, and nonalcoholic steatohepatitis, suggesting that the hypermethylation level of Gck and LPK promoters may be a useful parameter for the evaluation of obesity-induced insulin resistance and fatty liver.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.447

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 head0.037
GPT teacher head0.262
Teacher spread0.225 · 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

Citations75
Published2011
Admission routes1
Has abstractyes

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