A refined DNA methylation detection method using MspJI coupled quantitative PCR

Petell, Christopher J.; Loiseau, Gilbert; Gandy, Ryan; Pradhan, Sriharsa; Gowher, Humaira

doi:10.1016/j.ab.2017.06.006

Cited by 12 publications

(7 citation statements)

References 55 publications

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“…Given the changes in nuclear localization of AHCY in RyR2 KO and IRBIT KO cells, we examined the possibility that insulin genes in the knockout cells were differentially methylated. PCR amplification of genomic DNA regions, with or without digestion by a methylation-dependent endonuclease using primers that flank potential methylation sites (Fig 7A&B), provide a measure of the relative amount of DNA methylation in the amplified region [23]. Comparing PCR amplification at promoter regions upstream of the translation start site of the INS1 (Fig 7C) and INS2 (Fig 7D) genes revealed low methylation that was not different between RyR2 KO , IRBIT KO , or control cells.…”

Section: Resultsmentioning

confidence: 99%

RyR2/IRBIT regulates insulin gene transcription, insulin content, and secretion in the insulinoma cell line INS-1

Harvey

LaVigne²,

Dar³

et al. 2021

Preprint

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The role of ER Ca2+ release via ryanodine receptors (RyR) in pancreatic β-cell function is not well defined. Deletion of RyR2 from the rat insulinoma INS-1 (RyR2KO) enhanced the Ca2+ integral (AUC) stimulated by 7.5 mM glucose, and rendered it sensitive to block by the IP3 receptor inhibitor xestospongin C, coincident with reduced levels of the protein IP3Receptor Binding protein released with Inositol 1,4,5 Trisphosphate (IRBIT; aka AHCYL1). Deletion of IRBIT from INS-1 cells (IRBITKO) increased the Ca2+ AUC in response to 7.5 mM glucose and induced xestospongin sensitivity. Insulin content and basal (2.5 mM glucose) and 7.5 mM glucose-stimulated insulin secretion were reduced in RyR2KO cells and more modestly reduced in IRBITKO cells compared to controls. INS2 mRNA levels were reduced in both RyR2KO and IRBITKO cells, but INS1 mRNA levels were specifically decreased in RyR2KO cells. Nuclear localization of S-adenosylhomocysteinase (AHCY) was increased in RyR2KO and IRBITKO cells. DNA methylation of the INS1 and INS2 gene promotor regions was very low, and not different among RyR2KO, IRBITKO, and controls. In contrast, exon 2 of the INS1 and INS2 genes was more extensively methylated in RyR2KO and IRBITKO cells than in controls. Proteomics analysis using LC-MS/MS revealed that deletion of RyR2 or IRBIT resulted in differential regulation of 314 and 137 proteins, respectively, with 41 in common. These results suggest that RyR2 regulates IRBIT levels and activity in INS-1 cells, and together maintain insulin content and secretion, and regulate the proteome, perhaps via DNA methylation.One sentence SummaryDeletion of RyR2 from INS-1 cells had the unanticipated effect of reducing IRBIT proteins levels, and both RyR2 and IRBIT contribute to maintenance of glucose- stimulated insulin secretion.

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Section: Resultsmentioning

confidence: 99%

RyR2/IRBIT regulates insulin gene transcription, insulin content, and secretion in the insulinoma cell line INS-1

Harvey

LaVigne²,

Dar³

et al. 2021

Preprint

Self Cite

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“…The sequence features were extracted by k -gram and multivariate mutual information (MMI) [ 29 ], considering the nucleotide sequences around the candidate methylation sites. The physicochemical features were extracted by discrete wavelet transform (DWT) [ 34 ] and pseudo amino acid composition (PseAAC) [ 35 ], which are associated with the physical structure of nucleotide permutations [ 30 , 36 ]. Then, we trained the sparse Bayesian learning model to predict the DNA methylation sites.…”

Section: Methodsmentioning

confidence: 99%

“…Pseudo Amino Acid Composition (PseAAC) [ 35 ] is a very common method used to analyze protein sequences [ 31 ], and has been widely used in many research fields [ 36 , 46 , 47 ]. We adapt PseAAC by using physicochemical properties and correlation between each tuple to fit the DNA methylation prediction.…”

Section: Methodsmentioning

confidence: 99%

A Novel Computational Method for Detecting DNA Methylation Sites with DNA Sequence Information and Physicochemical Properties

Pan

Jiang

Tang

et al. 2018

IJMS

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DNA methylation is an important biochemical process, and it has a close connection with many types of cancer. Research about DNA methylation can help us to understand the regulation mechanism and epigenetic reprogramming. Therefore, it becomes very important to recognize the methylation sites in the DNA sequence. In the past several decades, many computational methods—especially machine learning methods—have been developed since the high-throughout sequencing technology became widely used in research and industry. In order to accurately identify whether or not a nucleotide residue is methylated under the specific DNA sequence context, we propose a novel method that overcomes the shortcomings of previous methods for predicting methylation sites. We use k-gram, multivariate mutual information, discrete wavelet transform, and pseudo amino acid composition to extract features, and train a sparse Bayesian learning model to do DNA methylation prediction. Five criteria—area under the receiver operating characteristic curve (AUC), Matthew’s correlation coefficient (MCC), accuracy (ACC), sensitivity (SN), and specificity—are used to evaluate the prediction results of our method. On the benchmark dataset, we could reach 0.8632 on AUC, 0.8017 on ACC, 0.5558 on MCC, and 0.7268 on SN. Additionally, the best results on two scBS-seq profiled mouse embryonic stem cells datasets were 0.8896 and 0.9511 by AUC, respectively. When compared with other outstanding methods, our method surpassed them on the accuracy of prediction. The improvement of AUC by our method compared to other methods was at least 0.0399. For the convenience of other researchers, our code has been uploaded to a file hosting service, and can be downloaded from: .

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“…The DNA samples are treated with Hpa II and Msp I restriction enzymes. Both the Hpa II and Msp I enzymes can specifically detect the 5 ′ -CCGG-3 sequences [59,60]. The Hpa II and Msp I are unable to cut the nonmethylated C in the 5 ′ -CCGG-3, while the Msp I can cut the methylated C in the 5 ′ -CmCGG-3 ′ .…”

Section: Techniques Based On Biological Identificationmentioning

confidence: 99%

Current Advances in DNA Methylation Analysis Methods

Khodadadi

Fahmideh

Khodadadi

et al. 2021

BioMed Research International

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DNA methylation is one of the epigenetic changes, which plays a major role in regulating gene expression and, thus, many biological processes and diseases. There are several methods for determining the methylation of DNA samples. However, selecting the most appropriate method for answering biological questions appears to be a challenging task. The primary methods in DNA methylation focused on identifying the state of methylation of the examined genes and determining the total amount of 5-methyl cytosine. The study of DNA methylation at a large scale of genomic levels became possible following the use of microarray hybridization technology. The new generation of sequencing platforms now allows the preparation of genomic maps of DNA methylation at the single-open level. This review includes the majority of methods available to date, introducing the most widely used methods, the bisulfite treatment, biological identification, and chemical cutting along with their advantages and disadvantages. The techniques are then scrutinized according to their robustness, high throughput capabilities, and cost.

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A refined DNA methylation detection method using MspJI coupled quantitative PCR

Cited by 12 publications

References 55 publications

RyR2/IRBIT regulates insulin gene transcription, insulin content, and secretion in the insulinoma cell line INS-1

RyR2/IRBIT regulates insulin gene transcription, insulin content, and secretion in the insulinoma cell line INS-1

A Novel Computational Method for Detecting DNA Methylation Sites with DNA Sequence Information and Physicochemical Properties

Current Advances in DNA Methylation Analysis Methods

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