Genome-Wide DNA Methylation Profiling in Cultured Eutopic and Ectopic Endometrial Stromal Cells

<div><p>The objective of this study was to characterize the genome-wide DNA methylation profiles of isolated endometrial stromal cells obtained from eutopic endometria with (euESCa) and without endometriosis (euESCb) and ovarian endometrial cysts (choESC). Three samples were analyzed in each group. The infinium methylation array identified more hypermethylated and hypomethylated CpGs in choESC than in euESCa, and only a few genes were methylated differently in euESCa and euESCb. A functional analysis revealed that signal transduction, developmental processes, immunity, etc. were different in choESC and euESCa. A clustering analysis and a principal component analysis performed based on the methylation levels segregated choESC from euESC, while euESCa and euESCb were identical. A transcriptome analysis was then conducted and the results were compared with those of the DNA methylation analysis. Interestingly, the hierarchical clustering and principal component analyses showed that choESC were segregated from euESCa and euESCb in the DNA methylation analysis, while no segregation was recognized in the transcriptome analysis. The mRNA expression levels of the epigenetic modification enzymes, including DNA methyltransferases, obtained from the specimens were not significantly different between the groups. Some of the differentially methylated and/or expressed genes (NR5A1, STAR, STRA6 and HSD17B2), which are related with steroidogenesis, were validated by independent methods in a larger number of samples. Our findings indicate that different DNA methylation profiles exist in ectopic ESC, highlighting the benefits of genome wide DNA methylation analyses over transcriptome analyses in clarifying the development and characterization of endometriosis.</p></div

Volksrecht : Sozialdemokratisches Tagblatt für die politischen Bezirke Aussig und Leitmeritz.

The "Volksrecht" is a socialist newspaper for the political districts of Aussig (Ústí nad Labem) and Leitmeritz (Litoměřice).Electronic reproduction.Description based on: Jahrgang 25, Nr. 276 (1. Dez. 1920); caption title.Latest issue consulted: Jahrgang 25, Nr. 300 (31. Dez. 1920

Hypomethylation in choESC compred to euESCa.

<p>Hypomethylation in choESC compred to euESCa.</p

The DNA methylation status as determined by the methylation-sensitive high resolution analyses (MS-HRMA) of NR5A1 (A), STAR (B), STRA6 (C) and HSD17B2 (D) in euESCa and choESC.

<p>Sample No.-wide DNA methylation and transpciptome as euESCa, and sample No. 8, 9, 10 were also analyzed as choESC. Sample No. 1 and 10 were analyzed for bisulfite sequencing as euESCa and choESC, respectively. The confidence value calculated by MS-HRMA indirectly indicates the DNA methylation level. The DNA methylation status of euESCa-1 was shown as 100% identical with regard to the DNA methylation. The confidence values of each sample were calculated in comparison with euESCa-1. In NR5A1 and STAR, the confidence values were lower in choESC than those in euESCa, indicating that the choESC are hypomethylated compared with the euESCa. In STRA6 and HSD17B2, the DNA methylation status of euESCa-1 was shown as −100% (arbitrary defined reverse axis value) identical regarding DNA methylation, indicating a DNA hypomethylation status. In STRA6, the confidence values were higher in choESC than those in euESCa, indicating that the choESC are hypermethylated compared with the euESCa. In HSD17B2, the DNA methylation status varied among individuals with euESCa and choESC. The samples of euESCa and choESC were isolated from seven and six patients, respectively.</p

The mRNA levels of NR5A1 (A), STAR (B), STRA6 (C) and HSD17B2 (D) in ESCa, choESC, eutopic endometrium and chocolate cysts.

<p>ESCa (n = 7), choESC (n = 5), eutopic endometria (n = 17) and chocolate cysts (n = 6) were subjected to total RNA isolation followed by real-time RT-PCR. The relative mRNA expression normalized to that of TBP (an internal control) was calculated. The values are the means ±SD. *, <i>p</i><0.01. ND: not detected.</p

Hypermethylation in choESC compred to euESCa.

<p>Lists of statistically significant GO terms (Biological process and molecular function) and KEGG pathway terms in hypomethylated genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083612#pone-0083612-t001" target="_blank">Table 1</a>) and in hypermethylated genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083612#pone-0083612-t002" target="_blank">Table 2</a>) in choESC compared to euESCa.</p

The results of the sodium bisulfite sequencing analyses of NR5A1 (A), STAR (B), STRA6 (C) and HSD17B2 (D) in the euESCa and choESC samples.

<p>The DNA methylation profile in the genomic regions of the NR5A1, STAR, STRA6 and HSD17B2 genes was analyzed by a sodium bisulfite sequencing method in a pair of euESCa and choESC from one individual, which had already been analyzed by the Infinium method. In NR5A1 and STRA6, the DNA methylation status of the proximal promoter and first exon was analyzed. The primer pairs BP-A and BP-B amplify region A and B, respectively, in STRA6. In STAR, the distal promoter region was analyzed. In HSD17B2, the first intron and second exon region were analyzed. The arrows indicate the positions of the bisulfite primers. Closed triangles represent the CpG sites analyzed by the Infinium method, and are accompanied by the identification names. •, methylated CpG sites; ◯, unmethylated CpG sites; BP, bisulfite primer.</p

Additional file 1: of SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

Supplementary Methods. Figure S1 Proportions of RNA-seq reads from T. gondii-infected HFFs aligning to a composite hg19/Toxoplasma genome. Figure S2 Comparison of distance weighting effect on gene scores. Figure S3 Representation of simulations demonstrating the effects on high scoring genes of variation of weightings. Figure S4 Comparison of gene scores with reduced and full SMITE models. Figure S5 Examples of modules generated by full and reduced SMITE models. Figure S6 KS test results comparing SMITE and FEM module genes and a random sampling of 10,000 genes. Figure S7 Comparison of the performance of the full SMITE model with the FEM model. Table S1 Criteria for defining genomic contexts in HFFs. Table S2 Weighting criteria used for SMITE analysis of the T. gondii HFF dataset. Appendix 1 R code for analyzing T. gondii HFF dataset with SMITE. Appendix 2 R code for analyzing T. gondii HFF dataset with FEM. Supplementary references (PDF 5642 kb

Additional file 2: of SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

Supplementary Tables. Table S3 Gene symbol and score of the high scoring genes using three different methods: SMITE full model, SMITE reduced model, and FEM. Table S4 Modules discovered using FEM and genes composing the modules with their DNA methylation, expression, and overall statistics. Table S5 Modules discovered using the reduced model of SMITE (SMITE-R) with spin-glass. Table S6 Modules discovered using the full model of SMITE (SMITE-F) with spin-glass. Table S7 Pathways associated with the genes composing the modules discovered by FEM. Table S8 Pathways associated with the genes composing the modules discovered by the reduced model of SMITE (SMITE-R) using spin-glass. Table S9 Pathways associated with the genes composing the modules discovered by the full model of SMITE (SMITE-F) using spin-glass. Table S10 Quantifying the number of times pathways were found to be associated the modules discovered by either FEM, the reduced model of SMITE (SMITE-R) using spin-glass, or the full model of SMITE(SMITE-F) using spin-glass. Table S11 Genes composing the “summary network” found by either the reduced (SMITE-R) or full (SMITE-F) SMITE models using the Heinz algorithm. Table S12 Pathways associated with the genes composing the “summary network” discovered by the reduced model of SMITE(SMITE-R) using the Heinz algorithm. Table S13 Pathways associated with the genes composing the “summary network” discovered by the full model of SMITE (SMITE-F) using the Heinz algorithm. Table S14 Genes composing the “modules” found using no weights instead of weighting by distance. Table S15 Pathways associated with the genes in the modules identified without using distance weighting. (XLSX 269 kb

Additional file 6: Figure S4. of Amnion as a surrogate tissue reporter of the effects of maternal preeclampsia on the fetus

Comparison of the DNA methylation distribution of variable HpaII sites. The distributions of the DNA methylation levels of variable methylation sites in severe PE-exposed (PE_S, green) (proteinuria grade ≥3 and systolic blood pressure ≥160 mmHg), less severe PE-exposed (PE_M, orange) (proteinuria grade ≤1 and systolic blood pressure ≥ 140 mmHg) and control (blue) were summarized graphically in violin plots. (PDF 895 kb