Er to further test which factors affect the DNA methylation levelEr to further test which

Er to further test which factors affect the DNA methylation level
Er to further test which factors affect the DNA methylation level in our dataset, we performed a principal component analysis of the DNA methylation data and correlated the top principal component with sex and all covariates of interest, including age, BMI, purity of the islets, days in culture and HbAa1c as described in [84]. Here, only sex and BMI showed significant correlations with the first principal component (P = 6.8 ?10-3 and P = 0.03, respectively). Since -values are easier to interpret biologically, M-valueswere reconverted into -values and were then used when describing the data and when generating figures. Non-CpG probes are presented with probe ID starting with `ch’ (Additional files 2, 3, 4, 5, 15, 16, 17, and 18). Moreover, since some probes on Illumina’s DNA methylation chip may cross-react to multiple locations in the genome, we used the published data by Chen et al. [23] to evaluate the number of possible cross-reactive probes among our significant methylation data (Additional files 6 and 7).mRNA expression analysis of human pancreatic isletsmRNA expression was analyzed using Affymetrix GeneChip?Human Gene 1.0 ST whole transcript based array (Affymetrix, Santa Clara, CA, USA) according to the manufacturer’s recommendations. We computed Robust Multichip Average (RMA) expression measure using the oligo package from Bioconductor [89]. To identify differences in gene expression between males and females, the data were analyzed using a linear regression model with the limma package in Bioconductor [87,88] with age, BMI, purity of the islets, days in culture, and HbA1c as covariates. We also performed a principal component analysis of the mRNA expression data and correlated the top principal component with sex and all covariates of interest, including age, BMI, purity of the islets, days in culture and HbAa1c as described in [84]. Here, purity of the islets showed significant correlations with the first principal component (P = 0.020), while sex and HbA1c showed significant correlations with the second principle component (P = 0.036 and P = 0.034).Locked nucleic acid-based microarray of human microRNAsRNA from hand-picked human islets was extracted from four male and seven female donors using a miRNeasy kit (Qiagen). RNA quantity and quality were evaluated using spectrophotometry by Nanodrop and electropherogram profiles by LDN193189 biological activity Experion (BioRad, Hercules, CA, USA), respectively. Total RNA (500 ng) was directly labeled with miRCURY Hy3 fluorescent dye, which was subsequently hybridized to miRCURY LNA microRNA array v.11.0 in a Maui hybridization chamber according to the manufacturer’s recommendations (Exiqon, Vedbaek, Denmark). Images were acquired using Agilent array scanner and spot intensities were quantified in Genepix Pro 4.1. Array signals were normalized using the global Lowess regression algorithm as implemented in CARMAweb 1.4 [90]. For this study, only array signals from microRNAs within genomic loci showing differential methylation patterns were considered in the analysis.Validation of DNA methylation array resultsTechnical validation of the Infinium HumanMethylation450 BeadChip methylation data was performed byHall PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/26866270 et al. Genome Biology 2014, 15:522 http://genomebiology.com/2014/15/12/Page 18 ofPyroSequencing (Qiagen) of three selected CpG sites (cg27483305, cg05688478, and cg13808071). Pre-designed PyroSequencing assays (PCR primers and sequencing primer) were used for the selected CpG sites (Qiagen) (Additional file.