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Ena-DATASET-ErasmusMC-hema-08-05-2020-13:39:57:784-17 - samples

ABSTRACT: 3q-capture DNA sequencing was performed as we described previously 13. In summary, genomic DNA was fragmented using the Covaris shearing device (Covaris), and sample libraries were assembled following the TruSeq DNA Sample Preparation Guide (Illumina). After ligation of adapters and an amplification step, target sequences of chromosomal regions 3q21.1-q26.2 were captured using custom in-solution oligonucleotide baits (Nimblegen SeqCap EZ Choice XL). The design of target sequences was based on the human genome assembly hg19: chr3q21.1:126036241-130672290 - chr3q26.2:157712147-175694147. Amplified captured sample libraries were paired-end sequenced (2x100 bp) on the HiSeq 2500 platform (Illumina) and aligned against the hg19 reference genome using the Burrows-Wheeler Aligner (BWA)25

PROVIDER: EGAD00001006123 | EGA |

REPOSITORIES: EGA

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Project description:Purpose: To investigate the role of DNA methylation in human uterine leiomyoma tumorigenesis, we profiled genome-wide DNA methylation patterns of leiomyoma cells (LM) and adjacent normal myometrial cells (MM) treated with or without DNA methylation inhibitor 5-Aza. Methods: Primary cells were isolated from fresh uterine myometrium or leiomyoma tissue. Cells were treated with vehicle (DMSO) or 5 µM 5’-aza for 96h with medium refreshed every 24h. Genomic DNA was extracted from LM and MM cells using DNeasy Blood and Tissue Kit (Qiagen, 69504) and fragmented to 300-500bp using Covaris M220. Methylated DNA fragments were captured using the MethylCap Kit (Diagenode, C02020010). Next-generation sequencing libraries were prepared using the KAPA Hyper Prep Kit (KAPA Biosystems, KK8502) and KAPA Single-Indexed Adapter Kit (KAPA Biosystems, KK8710). Libraries were sequenced under 75bp paired-end sequencing format. Sequences were aligned to the hg19 reference genome using Bowtie2. Histone style peak calling, differential binding analysis (getDifferentialPeaks) and motif analysis were performed using Homer under default settings. Differentily methylated regions (DMRs) were detected with a fold enrichment over background tag count ≥4 and poisson enrichment p-value over background tag count <0.0001. Differential DNA methylation between MM and LM (with or without treatment) at select regions were validated through qPCR. Results: More than 100,000 methyalted regions were detected in each sample. We discovered 21,086 DMRs between MM and LM cells under basal (vehicle treated) condition. In cells treated with 5-aza, we detected 8811 DMRs between MM and LM. We compared the genomic context of DMRs identified by MethylCap-Seq with the probe distribution of the HM450 array and detected large differences between these two methods. For example, 44.23% of DMR were located in intergenic regions by MethylCap-Seq, which was drastically higher than the percentage of HM450 array probes located in intergenic regions Downstream network enrichment analysis and motif analysis indicated that DMR-associated genes and regions are crucial for the tumor progression and hormone responsiveness of LM. Conclusions: Our study represents the detailed analysis of differential DNA methyaltion profiles between MM and LM using MethylCap-seq. Our results showed a more comprehensive evaluation of DNA methyaltion landscapes of MM and LM compared with previous array based analysis. We conclude that DNA methylation plays a crucial role in hormone-dependent LM tumorigenesis.

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Ena-DATASET-ErasmusMC-hema-08-05-2020-13:39:57:784-17 - samples

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