Association between UBE2E2 variant rs7612463 and type 2 diabetes mellitus in a Chinese Han Population

Kazakova, Elena V.; Zhou, Zhongyu; Chen, Meijun; Wang, Tongtong; Tong, Huixin; Zhuang, Tianwei; Sun, Lulu; Qiao, Hong

doi:10.18388/abp.2014_936

Cited by 10 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ubiquitin-conjugating enzyme E2E2 (UBE2E2) plays an important role in the synthesis and secretion of insulin. Mutations in UBE2E2 increase risk for type 2 diabetes [ 36 ]. 60S acidic ribosomal protein P0 is downregulated, suggesting a decrease in protein synthesis.…”

Section: Resultsmentioning

confidence: 99%

Effect of IAPP on the proteome of cultured Rin-5F cells

et al. 2018

View full text Add to dashboard Cite

BackgroundIslet amyloid polypeptide (IAPP) or amylin deposits can be found in the islets of type 2 diabetes patients. The peptide is suggested to be involved in the etiology of the disease through formation of amyloid deposits and destruction of β islet cells, though the underlying molecular events leading from IAPP deposition to β cell death are still largely unknown.ResultsWe used OFFGEL™ proteomics to study how IAPP exposure affects the proteome of rat pancreatic insulinoma Rin-5F cells. The OFFGEL™ methodology is highly effective at generating quantitative data on hundreds of proteins affected by IAPP, with its accuracy confirmed by In Cell Western and Quantitative Real Time PCR results. Combining data on individual proteins identifies pathways and protein complexes affected by IAPP. IAPP disrupts protein synthesis and degradation, and induces oxidative stress. It causes decreases in protein transport and localization. IAPP disrupts the regulation of ubiquitin-dependent protein degradation and increases catabolic processes. IAPP causes decreases in protein transport and localization, and affects the cytoskeleton, DNA repair and oxidative stress.ConclusionsResults are consistent with a model where IAPP aggregates overwhelm the ability of a cell to degrade proteins via the ubiquitin system. Ultimately this leads to apoptosis. IAPP aggregates may be also toxic to the cell by causing oxidative stress, leading to DNA damage or by decreasing protein transport. The reversal of any of these effects, perhaps by targeting proteins which alter in response to IAPP, may be beneficial for type II diabetes.Electronic supplementary materialThe online version of this article (10.1186/s12858-018-0099-3) contains supplementary material, which is available to authorized users.

show abstract

Section: Resultsmentioning

confidence: 99%

Effect of IAPP on the proteome of cultured Rin-5F cells

et al. 2018

View full text Add to dashboard Cite

show abstract

“…On chromosome 27, a QTL with strong evidence for MY (Table 3 ) corresponded to a single gene that aligned with mouse, rat and sheep sequences: UBE2E2 (Table 6 ). This gene is a good candidate for MY because it encodes a protein that plays an important role in the synthesis and secretion of insulin and may influence fat and protein content in milk [ 40 , 41 ].…”

Section: Resultsmentioning

confidence: 99%

Insights into the genetic variation of maternal behavior and suckling performance of continental beef cows

et al. 2016

View full text Add to dashboard Cite

BackgroundIn beef cattle, maternal care is critical for calf survival and growth. Our objective was to evaluate the major sources of additive genetic variation in maternal behavior and suckling performance in two genetically close beef breeds.MethodsMaternal performance was assessed based on maternal behavior (MB), milk yield (MY) and udder swelling score (US) of 1236 Blonde d’Aquitaine cows and 1048 Limousin cows. MB was scored just after calving to describe the intensity of the dam’s protective behavior towards her calf. Most of the cows were genotyped using the low-density chip EuroG10K BeadChip, and imputed to the high-density 770K panel within breed. Genetic parameters for each trait were estimated for each breed under a multi-trait best linear unbiased prediction animal model. Genomic analysis was performed for each breed using the high-density genotypes and a Bayesian variable selection method.ResultsHeritabilities were low for MB (0.11–0.13), intermediate for MY (0.33–0.45) and high for US (0.47–0.64). Genetic correlations between the traits ranged from 0.31 to 0.58 and 0.72 to 0.99 for the Blonde d’Aquitaine and Limousin breeds, respectively. Two quantitative trait loci (QTL) were detected for MB in Blonde d’Aquitaine with NPY1R and ADRA2A as candidate causative genes. Thirty to 56 QTL were detected for MY and US in both breeds and 12 candidate genes were identified as having a role in the genetic variation of suckling performance. Since very few pleiotropic QTL were detected, there was little biological explanation for the moderate (0.57) to very high (0.99) genetic correlations estimated between MY and US in the Blonde d’Aquitaine and Limousin cows, respectively. In Blonde d’Aquitaine, the correlation was largely due to the pleiotropic QTL detected in the region upstream of the CG gene, while in Limousin, this region was only identified for US, thus attesting the difference in genetic architecture between the breeds.ConclusionsOur findings question the assumption that two populations that have close genetic links share many QTL. Nevertheless, we identified four candidate genes that may explain a substantial amount of the genetic variation in suckling performance of these two breeds.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0223-z) contains supplementary material, which is available to authorized users.

show abstract

“…Cheung et al suggested that MYC (MYC Proto-Oncogene) can serve as an important mediator of impaired insulin secretion and β-cell apoptosis 38 . Some researches have indicated that the SNPs in UBE2E2 (Ubiquitin Conjugating Enzyme E2) were associated with the development of type 2 diabetes mellitus 39,40 . Besides, MIB2 (Mindbomb E3 Ubiquitin Protein Ligase 2), ANAPC1 (Anaphase Promoting Complex Subunit 1) and TCEB1 (ELOC, Elongin C) are also involved in the process of ubiquitination which can affect the development of insulin resistance and metabolic syndrome [41][42][43] .…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Biomarkers for Metabolic Syndrome Based on Machine Learning Algorithms and Integrated Bioinformatics Analysis

Liu¹,

Chen²,

Kong³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Metabolic syndrome is a common and complicated metabolic disorder and defined as a clustering of metabolic risk factors such as insulin resistance or diabetes, obesity, hypertension, and hyperlipidemia. However, its early diagnosis is limited because the lack of definitive clinical diagnostic biomarkers. In present study, we aim to select several candidate gene as a blood-based clinically applicable transcriptomics signature for metabolic syndrome. Method: We collected so far the largest MetS-associated peripheral blood high-throughput transcriptomics data and put forward a novel feature selection strategy by combining weighted gene co-expression network analysis, protein-protein interaction network analysis, LASSO regression and random forest approaches. Then, based on selected hub gene signature, we performed logistic regression analysis and subsequently established a web nomogram calculator for metabolic syndrome risk to detect the diagnostic value of this hub gene signature. Finally, Receiver Operating Characteristic curve analysis, calibration curve analysis, Hosmer-Lemeshow good of fit test and decision curve analysis showed the classification and calibration performance as well as potential clinical benefit of this hub gene signature. Results: Through weighted gene co-expression network analysis, protein-protein interaction network analysis, we identified 2 gene modules and 51 hub genes associated with metabolic syndrome. Then, we subsequently performed further feature selection via LASSO regression and random forest method. Finally, a 9-hub-gene signature with high diagnostic value and a web nomogram calculator for metabolic syndrome risk (https://xjtulgz.shinyapps.io/DynNomapp/) were developed. This 9-hub-gene signature showed excellent classification and calibration performance (AUC= 0.968 in training set, AUC= 0.883 in internal validation set, AUC= 0.861 in external validation set) as well as ideal potential clinical benefit. Conclusions: The blood-based 9-hub-gene signature identified in present study and the web nomogram calculator for metabolic syndrome risk are possible to accurately achieve the noninvasive screening or diagnosis of MetS considering the excellent classification ability, calibration and potential clinical benefits.

show abstract

Association between UBE2E2 variant rs7612463 and type 2 diabetes mellitus in a Chinese Han Population

Cited by 10 publications

References 22 publications

Effect of IAPP on the proteome of cultured Rin-5F cells

Effect of IAPP on the proteome of cultured Rin-5F cells

Insights into the genetic variation of maternal behavior and suckling performance of continental beef cows

Identification of Novel Biomarkers for Metabolic Syndrome Based on Machine Learning Algorithms and Integrated Bioinformatics Analysis

Contact Info

Product

Resources

About