Project description:Objective: To define the role of epigenetics, particularly DNA methylation in adaptive vascular growth in hyperlipidemic and type 2 diabetic mouse models of hind limb ischemia Methods: Unilateral hindlimb ischemia was induced by ligating femoral artery proximal to the bifurcation of superficial and deep femoral artery. DNA was isolated from ischemic muscles collected at day 7 after ischemia induction using DNeasy Tissue kit (Qiagen). 5 Âµg of genomic DNA was sheared into small fragments with a mean size of 150 bp by using a Covarisâ¢ S2 sonicator System. Quality of the fragmentation was analyzed with Bioanalyzer. Fragmented DNA samples were used for preparation of DNA fragment libraries and sequenced on the SOLiD4 sequencing instrument on one flow cell for 50 bp reads. The sequencing reads were mapped to mus musculus genome build mm9. Data was analyzed by using Bioscope. The samples were normalized with the MEDIPS package in R/B Bioconductor. The analysis of CpG methylation was done primarily in the proximal promoter regions encompassing a region of â1kb upstream of the transcription start site (TSS) and +500 bp downstream of the TSS. Comparisons were performed between hyperlipidemic versus controls and diabetic versus controls to detect differentially methylated regions. R package Limma was used for performing the statistical testing between the groups. Results: When visualizing the whole normalized data the samples did not cluster according to the sample groups. Especially two samples from hyperlipidemic and diabetic ischemic muscles differed clearly from the rest of the samples. To detect the differentially methylated genes, stringent thresholds for p-values and fold change values were chosen to list a reasonable number of genes. Upon filtering, significant differences in the methylation patterns of the sample groups were observed. More importantly, when clustering only the filtered genes, the samples clustered clearly according to the sample groups giving evidence of condition-dependent behavior. Using a threshold of methylation fold change of >1.2 and p value <0.05, we identified 397 and 446 genes to be hypomethylated in hyperlipidemic and diabetic ischemic muscles respectively compared to controls. There were 46 genes commonly shared, but still having a unique pattern of hypomethylation in 371 and 394 genes in hyperlipidemic and diabetic ischemic muscles respectively compared to controls. Similarly, there were 371 and 394 genes hypermethylated in hyperlipidemic and diabetic ischemic respectively compared to controls. Interestingly, we found 264 genes to be commonly hypermethylated, whereas 107 and 130 genes were uniquely hypermethylated in hyperlipidemic and diabetic ischemic muscles respectively. Thus, proximal promoter methylation suggested a shared, yet distinct pattern of DNA methylation in ischemic muscles of hyperlipidemic and type 2 diabetic mice compared to controls. Out of 397 genes that were hypomethylated in hyperlipidemic ischemic muscle, 68 genes were shown to be upregulated in âproinflammatory M1 macrophagesâ as shown by recent studies. Similarly, out of the 371 hypermethylated genes 93 genes were shown to be upregulated in âanti-inflammatory and proangiogenic M2 macrophagesâ as described recently. Out of 446 hypomethylated genes in diabetic ischemic muscle, 65 genes were shown to be upregulated in âproinflammatory M1 macrophagesâ as shown recently. Similarly, out of 394 hypermethylated genes 105 genes were specifically upregulated in âanti-inflammatory and proangiogenic M2 macrophagesâ as shown recently. qRT-PCR analysis suggested an inverse relationship between proximal promoter hypermethylation and mRNA expression in a subset of M2 macrophage specific genes in hyperlipidemic and type 2 diabetic ischemic muscles compared to control ischemic muscles. Conclusions: Our results suggest a role of epigenetics particularly proximal promoter DNA methylation in macrophage polarization and their contribution to angiogenesis and tissue repair in hyperlipidemic and type 2 diabetic mouse models of hind limb ischemia. Epigenetics at the level of DNA methylation may act as a deciding factor in promoting a pro or anti-inflammatory phenotype of macrophages critical in cardiovascular diseases. Ischemic skeletal muscle DNA methylation sequencing of triplicate samples from C57BL/6J (WT) mice, hyperlipidemic mice (LDLR-/-ApoB100/100 , C57BL/6J background) and type 2 diabetic mice (IGF-II/LDLR-/-ApoB100/100 , C57BL/6J background) using SoliD4 sequencing platform

Project description:Objective: To define the role of epigenetics, particularly DNA methylation in adaptive vascular growth in hyperlipidemic and type 2 diabetic mouse models of hind limb ischemia Methods: Unilateral hindlimb ischemia was induced by ligating femoral artery proximal to the bifurcation of superficial and deep femoral artery. DNA was isolated from ischemic muscles collected at day 7 after ischemia induction using DNeasy Tissue kit (Qiagen). 5 µg of genomic DNA was sheared into small fragments with a mean size of 150 bp by using a Covaris™ S2 sonicator System. Quality of the fragmentation was analyzed with Bioanalyzer. Fragmented DNA samples were used for preparation of DNA fragment libraries and sequenced on the SOLiD4 sequencing instrument on one flow cell for 50 bp reads. The sequencing reads were mapped to mus musculus genome build mm9. Data was analyzed by using Bioscope. The samples were normalized with the MEDIPS package in R/B Bioconductor. The analysis of CpG methylation was done primarily in the proximal promoter regions encompassing a region of –1kb upstream of the transcription start site (TSS) and +500 bp downstream of the TSS. Comparisons were performed between hyperlipidemic versus controls and diabetic versus controls to detect differentially methylated regions. R package Limma was used for performing the statistical testing between the groups. Results: When visualizing the whole normalized data the samples did not cluster according to the sample groups. Especially two samples from hyperlipidemic and diabetic ischemic muscles differed clearly from the rest of the samples. To detect the differentially methylated genes, stringent thresholds for p-values and fold change values were chosen to list a reasonable number of genes. Upon filtering, significant differences in the methylation patterns of the sample groups were observed. More importantly, when clustering only the filtered genes, the samples clustered clearly according to the sample groups giving evidence of condition-dependent behavior. Using a threshold of methylation fold change of >1.2 and p value <0.05, we identified 397 and 446 genes to be hypomethylated in hyperlipidemic and diabetic ischemic muscles respectively compared to controls. There were 46 genes commonly shared, but still having a unique pattern of hypomethylation in 371 and 394 genes in hyperlipidemic and diabetic ischemic muscles respectively compared to controls. Similarly, there were 371 and 394 genes hypermethylated in hyperlipidemic and diabetic ischemic respectively compared to controls. Interestingly, we found 264 genes to be commonly hypermethylated, whereas 107 and 130 genes were uniquely hypermethylated in hyperlipidemic and diabetic ischemic muscles respectively. Thus, proximal promoter methylation suggested a shared, yet distinct pattern of DNA methylation in ischemic muscles of hyperlipidemic and type 2 diabetic mice compared to controls. Out of 397 genes that were hypomethylated in hyperlipidemic ischemic muscle, 68 genes were shown to be upregulated in ‘proinflammatory M1 macrophages’ as shown by recent studies. Similarly, out of the 371 hypermethylated genes 93 genes were shown to be upregulated in ‘anti-inflammatory and proangiogenic M2 macrophages’ as described recently. Out of 446 hypomethylated genes in diabetic ischemic muscle, 65 genes were shown to be upregulated in ‘proinflammatory M1 macrophages’ as shown recently. Similarly, out of 394 hypermethylated genes 105 genes were specifically upregulated in ‘anti-inflammatory and proangiogenic M2 macrophages’ as shown recently. qRT-PCR analysis suggested an inverse relationship between proximal promoter hypermethylation and mRNA expression in a subset of M2 macrophage specific genes in hyperlipidemic and type 2 diabetic ischemic muscles compared to control ischemic muscles. Conclusions: Our results suggest a role of epigenetics particularly proximal promoter DNA methylation in macrophage polarization and their contribution to angiogenesis and tissue repair in hyperlipidemic and type 2 diabetic mouse models of hind limb ischemia. Epigenetics at the level of DNA methylation may act as a deciding factor in promoting a pro or anti-inflammatory phenotype of macrophages critical in cardiovascular diseases.

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: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 β-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 37°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 4˚C. 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 4˚C. 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 Pearson’s 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- Mϕs; whereas, of the 102 genes with promoter hypermethylation, 51 genes were suggested to be upregulated in M2- Mϕs. Similarly, out of 272 genes with differentially methylated promoters in T2DM macrophages 88 genes were suggested to be upregulated in M1-Mϕs; whereas, out of 136 genes with promoter hypermethylation 60 genes were suggested to be upregulated in M2- Mϕs. Thus a significant promoter hypomethylation of M1-Mϕ and hypermethylation of M2-Mϕ genes suggested the predominance of proinflammatory M1-Mϕs in ischemic muscles of HL and T2DM compared to M2-Mϕs in control mice. Conclusions: We found significant promoter hypomethylation of genes typical for proinflammatory M1-Mϕs and hypermethylation of anti-inflammatory, proangiogenic M2-Mϕ associated genes in HL and T2DM ischemic muscles. Epigenetic alterations skewing Mϕ phenotype towards proinflammatory as opposed to anti-inflammatory, proangiogenic and tissue repair phenotype may contribute to impaired adaptive vascular growth in these pathological conditions.

			Action	DRS
	SRR1794040.fastq.gz	Fastqsanger.gz
	SRR1794050.fastq.gz	Fastqsanger.gz
	SRR1794060.fastq.gz	Fastqsanger.gz
	SRR1794041.fastq.gz	Fastqsanger.gz
	SRR1794051.fastq.gz	Fastqsanger.gz

Dataset Information

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets