Project description:Purpose: The goal of this study was to compare the genome-wide promoter methylation alterations in macrophages and endothelial cells during hindlimb ischemia among normal, hyperlipidemic and type-2 diabetic mice. Methods: Unilateral hindlimb ischemia was induced by ligating femoral artery proximal to the bifurcation of superficial and deep femoral artery in mice deficient of LDL receptor and expressing only apolipoprotein B100 (LDLR-/-ApoB100/100, C57BL/6J background) (The Jackson Laboratory, Bar Harbor,USA) and mice with Î²-cell specific over-expression of insulin-like growth factor-2 in atherosclerotic background (IGF-II/LDLR-/-ApoB100/100, C57BL/6J background) with type 2 diabetic features on high-fat diet (TD 88173, Harlan Teklad: 42% of calories from fat and 0.15% from cholesterol, no sodium cholate) 8 weeks prior to surgery and continued throughout the study 1. C57BL/6J (WT) mice fed with regular chow-diet (R36, Lactamin) served as controls. All animals were aged between 20 to 24 weeks at the time of hindlimb operations. For sorting macrophages from ischemic muscles, ischemic gastrocnemius muscles were minced and enzymatically dissociated using a cocktail containing 450 U/mL Collagenase I, 125 U/mL Collagenase XI, 60 U/mL DNAseI, and 60 U/mL hyaluronidase (Sigma Aldrich) for 1 h at 37Â°C. The cells were then counted and divided into CD31+ve and CD31-vefractions using CD31 magnetic bead enrichment (Miltenyi Biotec). For macrophage sorting CD31-ve fraction was incubated for 15 minutes with rat anti-mouse CD16/32 mAb (Fc Block, BD-pharmingen) and stained with FITC conjugated rat anti-mouse F4/80 antibody (Serotec) for 20 minutes at 4ËC. For endothelial sorting CD31+ fraction was incubated for 15 minutes with rat anti-mouse CD16/32 mAb (Fc Block, BD-pharmingen) and stained with APC conjugated rat anti-mouse CD31 antibody (BD-pharmingen) and FITC conjugated rat anti-mouse CD45 ((BD-pharmingen) for 20 minutes at 4ËC. FACS sorting was performed on FACS AriaIII (BD Biosciences). Genomic DNA was isolated from FACS sorted macrophages and endothelial cells using AllPrep DNA/RNA/Protein Mini Kit (Qiagen Finland, Helsinki, Finland) according to manufacturer's instructions. Results: The sample similarity as assessed by Pearsonâs correlation matrix and Hierarchial clustering showed high correalation among macrophages, as well as endothelial cells. There was a clear clustering of macrophages and endothelial cells as evidence by their CpG methylation clustering, furthermore macrophages from HL and T2DM mice showed clear clustering compared to control macrophages. Differential methylation analysis of RRBS methylation data from macrophages and endothelial cells was performed using Methylkit. Using a threshold of adjusted p value (Q) <0.05 and percentage methylation difference of >5%, we identified 198 and 272 genes whose promoters were hypomethylated in HL and T2DM macrophages. Similarly, there were 102 and 136 gene promoters were hypermethylated in HL and T2DM macrophages, respectively compare to control macrophages. Thus, proximal promoter methylation suggested that HL and T2DM have convergent influences on the proximal promoter methylation of numerous macrophage specific genes. In order to find out whether these genes with differential methylated promoters were differentially expressed at mRNA expression level in purified macrophages, we further compared our data with the GEO datasets as above. Of the 198 genes with promoter hypomethylation in HL macrophages 72 genes were suggested to be upregulated in M1- MÏs; whereas, of the 102 genes with promoter hypermethylation, 51 genes were suggested to be upregulated in M2- MÏs. Similarly, out of 272 genes with differentially methylated promoters in T2DM macrophages 88 genes were suggested to be upregulated in M1-MÏs; whereas, out of 136 genes with promoter hypermethylation 60 genes were suggested to be upregulated in M2- MÏs. Thus a significant promoter hypomethylation of M1-MÏ and hypermethylation of M2-MÏ genes suggested the predominance of proinflammatory M1-MÏs in ischemic muscles of HL and T2DM compared to M2-MÏs in control mice. Conclusions: We found significant promoter hypomethylation of genes typical for proinflammatory M1-MÏs and hypermethylation of anti-inflammatory, proangiogenic M2-MÏ associated genes in HL and T2DM ischemic muscles. Epigenetic alterations skewing MÏ phenotype towards proinflammatory as opposed to anti-inflammatory, proangiogenic and tissue repair phenotype may contribute to impaired adaptive vascular growth in these pathological conditions. Macrophages and endothelial whole genome DNA methylation was performed in triplicates (Each sample was pooled from 3-4 mice) by RRBS Sequencing approach using Illumina HiSeq 2500. qRTâPCR validation was performed using TaqMan assays.

Project description:The samples were collected from the participants of the Finnish Diabetes Prediction and Prevention (DIPP) Study, born between 1995 and 2006. DIPP is a prospective follow-up cohort of children with a moderate or high risk of type 1 diabetes, based on the HLA-DR-DQ genotype. Islet cell autoantibodies (ICA, GADA, IAA, IA2A and ZnT8A) were measured 1 - 4 times per year until age 15 or year 2018. The aim was to study associations between perinatal DNA methylation marks and later progression to type 1 diabetes. Case individuals who became persistently positive for at least two biochemical autoantibodies (GADA, IAA, IA2A or ZnT8A) and/or were diagnosed with type 1 diabetes during the follow-up were compared to the control individuals who remained autoantibody-negative throughout the follow-up. These data were also used in the development of data analysis methodology in bisulfite sequencing studies. To protect the privacy of the study participants, the sequence read data are not publicly available. However, the processed data can be downloaded here. These include two count matrices: \\"methylated_reads\\" and \\"total_reads\\". The matrix \\"methylated_reads\\" contains methylated read counts at each high-coverage CpG site (altogether approximately 2.5 million rows) at each of the 173 samples (173 columns), and the matrix \\"total_reads\\" contains the corresponding total read counts (coverage). Please notice that the methylated read counts are read counts, not percentages. Methylation proportions can be calculated as methylated_reads/total_reads. The row names are the genomic locations of these CpG sites in hg19 (GRCh37) coordinates (1,2). For privacy reasons, all potential SNPs were excluded from these publicly available count matrices. Specifically, we removed all common (minor allele frequency > 1 %) human SNPs, as listed in dbSNP (3). We also removed all SNPs that were detected in one or more samples even with \\"low\\" evidence by BS-SNPer, which is a software for detecting SNPs from bisulfite sequencing data (4). Altogether 204443 out of 2752981 rows were removed from the original coverage-filtered count matrices that were analyzed in the present study. Description of the sample attributes: Individual: The individual-specific identifiers, such as “Subject1”. Since each sample is from a different individual, these correspond to the sample identifiers (Subject1 == Sample 1 etc.) Experimental Group: The variable of interest (called \\"class\\" in the associated publications) with three possible values: 1) case, 2) control and 3) NA (neither case nor control). 1) Case: became persistently positive for at least two biochemical autoantibodies (GADA, IAA, IA2A or ZnT8A) and/or diagnosed with type 1 diabetes during the follow-up. 2) Control: remained autoantibody-negative throughout the follow-up. 3) NA: The remaining 51 individuals with a missing value (“NA”) did not qualify as cases or controls, since they were either persistently positive for only 1 biochemical autoantibody or transiently positive for one or more autoantibodies. We excluded these 51 individuals from the case-control-comparison but included them in the comparison between the sexes. Library preparation batch: The sequencing libraries were prepared in 7 batches. The names of the batches do not have any special meaning. That is, \\"1A\\" is not necessarily more similar to \\"1B\\" than it is to \\"3B\\". We treated this as a categorical technical variable with 7 categories. PC1 and PC2: Projections of the sample-specific methylation proportion vectors on the first two orthonormal principal components. The principal component analysis (PCA) was performed on the original coverage-filtered methylation proportion matrix (methylated/total reads), where missing values at each CpG site were imputed by the median over samples with non-missing values. The original methylation proportion matrix included 2752981 rows, whereas these publicly available matrices include 2548538 rows (all potential SNPs excluded). Hence, PCA on the publicly available data would result in slightly different values for PC1 and PC2. We included these as covariates in the differential methylation analysis to represent technical variation (in addition to the library preparation batches). References 1. Church DM, Schneider VA, Graves T, Auger K, Cunningham F, Bouk N, et al. Modernizing reference genome assemblies. PLoS Biol. 2011 Jul;9(7):e1001091. 2. Genome Reference Consortium. NCBI downloads: https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz, accessed Feb 10th, 2019 3. NCBI. dbSNP: https://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz, accessed April 29th, 2021 4. Gao S, Zou D, Mao L, Liu H, Song P, Chen Y, et al. BS-SNPer: SNP calling in bisulfite-seq data. Bioinformatics. 2015 Dec 15;31(24):4006–8.

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DNA methylation profiling to investigate how Tet1 controls maintenance DNA methylation via the regulation of Dppa3 in ESCs

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