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Record W2548552719 · doi:10.18632/oncotarget.13121

Tet methylcytosine dioxygenase 2 inhibits atherosclerosis via upregulation of autophagy in ApoE−/− mice

2016· article· en· W2548552719 on OpenAlexaffabout
Juan Peng, Qin Yang, Afang Li, Rongqing Li, Wang Zuo, Lushan Liu, Zhong Ren, Xi‐Long Zheng, Xiao‐Qing Tang, Guohua Li, Zhi‐Han Tang, Zhi‐Sheng Jiang, Dangheng Wei

Bibliographic record

VenueOncotarget · 2016
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersConstruct Program of the Key Discipline in Hunan ProvinceScience and Technology Innovative Research Team in Higher Educational Institutions of Hunan ProvinceUniversity of South ChinaChongqing UniversityNational Natural Science Foundation of China
KeywordsAutophagyDownregulation and upregulationInflammationApolipoprotein EMedicineImmunologyInternal medicineCancer researchBiologyGeneGeneticsDiseaseApoptosis

Abstract

fetched live from OpenAlex

// Juan Peng 1,* , Qin Yang 1,* , A-Fang Li 1,* , Rong-Qing Li 1 , Zuo Wang 1 , Lu-Shan Liu 1 , Zhong Ren 1 , Xi-Long Zheng 2 , Xiao-Qing Tang 3 , Guo-Hua Li 1 , Zhi-Han Tang 1 , Zhi-Sheng Jiang 1 and Dang-Heng Wei 1 1. Institute of Cardiovascular Disease, Key Lab for Arteriosclerology of Hunan Province, University of South China, Hengyang, Hunan, China 2. Department of Biochemistry and Molecular Biology, The Libin Cardiovascular Institute of Alberta, The University of Calgary, Health Sciences Center, Calgary, Alberta, Canada 3. Department of Physiology & Institute of Neuroscience, Medical School, University of South China, Hengyang, PRC, China * Co-first author Correspondence to: Zhi-Han Tang, email: // Zhi-Sheng Jiang, email: // Dang-Heng Wei, email: // Keywords : TET2, autophagy, endothelial cells, inflammation, atherosclerosis, Pathology Section Received : June 08, 2016 Accepted : October 21, 2016 Published : November 04, 2016 Abstract Tet methylcytosine dioxygenase 2 (TET2) mediates the conversion of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). The loss of TET2 is associated with advanced atherosclerotic lesions. Our previous study showed that TET2 improves endothelial cell function by enhancing endothelial cell autophagy. Accordingly, this study determined the role of TET2 in atherosclerosis and potential mechanisms. In ApoE −/− mice fed high-fat diet, TET2 overexpression markedly decreased atherosclerotic lesions with uniformly increased level of 5hmC and decreased level of 5mC in genomic DNA. TET2 overexpression also promoted autophagy and downregulated inflammation factors, such as vascular cell adhesion molecule 1, intercellular adhesion molecule 1, monocyte chemotactic protein 1, and interleukin-1. Consistently, TET2 knockdown with small hairpin RNA (shRNA) in ApoE −/− mice decreased 5hmC and increased 5mC levels in atherosclerotic lesions. Meanwhile, autophagy was inhibited and atherosclerotic lesions progressed with an unstable lesion phenotype characterized by large lipid core, macrophage accumulation, and upregulated inflammation factor expression. Experiments with the cultured endothelial cells revealed that oxidized low-density lipoprotein (ox-LDL) inhibited endothelial cell autophagy. TET2 shRNA strengthened impaired autophagy and autophagic flux in the ox-LDL-treated endothelial cells. TET2 overexpression reversed these effects by decreasing the methylation level of the Beclin 1 promoter, which contributed to the downregulation of inflammation factors. Overall, we identified that TET2 was downregulated during the pathogenesis of atherosclerosis. The downregulation of TET2 promotes the methylation of the Beclin 1 promoter, leading to endothelial cell autophagy, impaired autophagic flux, and inflammatory factor upregulation. Upregulation of TET2 may be a novel therapeutic strategy for treating atherosclerosis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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

Citations88
Published2016
Admission routes2
Has abstractyes

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