Dataset Information

DNA methylation profiling of genomic DNA isolated from urine in diabetic chronic kidney disease: A pilot study.

ABSTRACT: To characterise the genomic DNA (gDNA) yield from urine and quality of derived methylation data generated from the widely used Illuminia Infinium MethylationEPIC (HM850K) platform and compare this with buffy coat samples.DNA methylation is the most widely studied epigenetic mark and variations in DNA methylation profile have been implicated in diabetes which affects approximately 415 million people worldwide.QIAamp Viral RNA Mini Kit and QIAamp DNA micro kit were used to extract DNA from frozen and fresh urine samples as well as increasing volumes of fresh urine. Matched buffy coats to the frozen urine were also obtained and DNA was extracted from the buffy coats using the QIAamp DNA Mini Kit. Genomic DNA of greater concentration than 20?g/ml were used for methylation analysis using the HM850K array.Irrespective of extraction technique or the use of fresh versus frozen urine samples, limited genomic DNA was obtained using a starting sample volume of 5ml (0-0.86?g/mL). In order to optimize the yield, we increased starting volumes to 50ml fresh urine, which yielded only 0-9.66?g/mL A different kit, QIAamp DNA Micro Kit, was trialled in six fresh urine samples and ten frozen urine samples with inadequate DNA yields from 0-17.7?g/mL and 0-1.6?g/mL respectively. Sufficient genomic DNA was obtained from only 4 of the initial 41 frozen urine samples (10%) for DNA methylation profiling. In comparison, all four buffy coat samples (100%) provided sufficient genomic DNA.High quality data can be obtained provided a sufficient yield of genomic DNA is isolated. Despite optimizing various extraction methodologies, the modest amount of genomic DNA derived from urine, may limit the generalisability of this approach for the identification of DNA methylation biomarkers of chronic diabetic kidney disease.

SUBMITTER: Lecamwasam A

PROVIDER: S-EPMC5819761 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DNA methylation profiling of genomic DNA isolated from urine in diabetic chronic kidney disease: A pilot study.

Lecamwasam Ashani A Sexton-Oates Alexandra A Carmody Jake J Ekinci Elif I EI Dwyer Karen M KM Saffery Richard R

PloS one 20180220 2

<h4>Aim</h4>To characterise the genomic DNA (gDNA) yield from urine and quality of derived methylation data generated from the widely used Illuminia Infinium MethylationEPIC (HM850K) platform and compare this with buffy coat samples.<h4>Background</h4>DNA methylation is the most widely studied epigenetic mark and variations in DNA methylation profile have been implicated in diabetes which affects approximately 415 million people worldwide.<h4>Methods</h4>QIAamp Viral RNA Mini Kit and QIAamp DNA ...[more]

PMID: 29462136

Similar Datasets

Project description:A subset of individuals with type 1 diabetes will develop diabetic kidney disease (DKD). DKD is heritable and large-scale genome-wide association studies have begun to identify genetic factors that influence DKD. Complementary to genetic factors, we know that a person's epigenetic profile is also altered with DKD. This study reports analysis of DNA methylation, a major epigenetic feature, evaluating methylome-wide loci for association with DKD. Unique features (n = 485,577; 482,421 CpG probes) were evaluated in blood-derived DNA from carefully phenotyped White European individuals diagnosed with type 1 diabetes with (cases) or without (controls) DKD (n = 677 samples). Explicitly, 150 cases were compared to 100 controls using the 450K array, with subsequent analysis using data previously generated for a further 96 cases and 96 controls on the 27K array, and de novo methylation data generated for replication in 139 cases and 96 controls. Following stringent quality control, raw data were quantile normalized and beta values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls; resultant P-values for array-based data were adjusted for multiple testing. Genes with significantly increased (hypermethylated) and/or decreased (hypomethylated) levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways. Twenty-two loci demonstrated statistically significant fold changes associated with DKD and additional support for these associated loci was sought using independent samples derived from patients recruited with similar inclusion criteria. Markers associated with CCNL1 and ZNF187 genes are supported as differentially regulated loci (P < 10-8), with evidence also presented for AFF3, which has been identified from a meta-analysis and subsequent replication of genome-wide association studies. Further supporting evidence for differential gene expression in CCNL1 and ZNF187 is presented from kidney biopsy and blood-derived RNA in people with and without kidney disease from NephroSeq. Evidence confirming that methylation sites influence the development of DKD may aid risk prediction tools and stimulate research to identify epigenomic therapies which might be clinically useful for this disease.

Dataset Information

DNA methylation profiling of genomic DNA isolated from urine in diabetic chronic kidney disease: A pilot study.

Publications

DNA methylation profiling of genomic DNA isolated from urine in diabetic chronic kidney disease: A pilot study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets