MeDIP Sequencing (MeDIP-Seq)

CD Genomics is providing Methylated DNA Immunoprecipitation Sequencing (MeDIP) service to enrich and capture methylated DNA fragments for gene-specific DNA methylation studies on a genome wide scale.

The Introduction of MeDIP Sequencing

DNA methylation refers to the postreplicative maintenance or de novo addition of a methyl group to the carbon-5 position of the cytosine pyrimidine ring by DNA methyltransferases. In mammals, DNA methylation often occurs in CG (mCG) and non-CG (mCHG or mCHH, referred to as mC) contexts. mCG accounts for about 60-80% of total CG dinucleotides. Variants were recently discovered, including 5-hyfroxymethylcytosine (hmC), 5-formylcytosine, and 5-carboxylcytosin. Although structurally similar to mC, hmC is much less abundant and is formed by the TET family of dioxygenase enzyme. Studies suggest DNA methylation plays an important part in embryonic development, gene regulation, and disease genesis including carcinogenesis. Comprehensive genome-wide DNA methylation and hydroxymethylation studies provide a way to thoroughly understand normal development and to identify potential epigenetic mutations.

MeDIP-seq combines methylated DNA immunoprecipitation with the next-generation sequencing for epigenetic studies at genome-wide level or any given regions of interest. MeDIP is capable of detecting methylated cytosines in mC and mCG contexts, and possibly hmC, 5-formylcytosine, and 5-carboxylcytosine as well. It uses anti-5-methylcytosine antibodies coupled to magnetic beads to select for genomic fragments that are methylated. These fragments are then sequenced and the coverage of these reads can be used to estimate the methylation level of the region that they map to. The 5-hydroxymethylcytosine (hmC) can also be detected genome-wide by using a hydroxymethylated DNA immunoprecipitation (hMeDIP) procedure with a 5hmC-specific antibody. MeDIP-seq neither introduces any mutations nor requires uracil-tolerant DNA polymerase. MeDIP-seq technology is feasible to profile genome-wide DNA methylation in low amounts of DNA samples with the resolution of several hundred base pairs in minimal selection bias and at a competitive cost.

Advantages of MeDIP Sequencing

• Can target mC, mCG, or hmC
• Whole genome or any regions of interest
• Near-unbiased and hypothesis
• Epigenetic biomarker discovery
• Single-nucleotide resolution and cost-efficiency
• Low input DNA requirement

Applications of MeDIP Sequencing

• Epigenetic Heterogeneity
• Environment and Epigenetics
• Genetic Expression Regulation
• Disease Research
• Genetic Imprinting
• Embryonic Development
• Detection of DNA methylation-enriched regions in disease samples and inference of candidate gene sets whose expression is suppressed in the samples.
• Monitoring the dynamic DNA methylation patterns at different stages of disease occurrence and development, particularly in lesion sites, to screen for epigenetic markers that help define the extent of disease progression.
• Comparative analysis of the differences in signal and location distribution of DNA methylation regions between disease and normal samples, identification of disease-specific DNA methylation regions, and observation of the genes surrounding these regions to narrow down the list of candidate genes related to the disease.

MeDIP Sequencing Workflow

The general workflow for MeDIP sequencing is outlined below. Briefly, the extracted DNA is fragmented, denatured, ligated with adaptor and captured using the antibody directed against 5 methylcytosine. The enriched methylated DNA is then amplified with PCR, size-selected, and sequenced on Illumina sequencing instrument.

Service Specification

	Sample Requirements Genomic DNA ≥ 2 μg, Minimum Quantity: 1 μg, Concentration≥ 100 ng/µl OD 260/280=1.8~2.0 All DNA should be RNase-treated and should show no degradation or contamination. Note: Sample amounts are listed for reference only. For detailed information, please contact us with your customized requests.
Click	Sequencing Strategies MeDIP library preparation Illumina HiSeq4000, PE150 50M reads or 10 G data per sample More than 80% of bases with a ≥Q30 quality score
	Data Analysis We provide multiple customized bioinformatics analyses: Alignment against reference genome Alignment against reference genome Whole genome region distribution Peak distribution statistics Peak associated gene screening Functional annotation with GO/KEGG analysis Differential expression level analysis of peak associated genes Note: Recommended data outputs and analysis contents displayed are for reference only. For detailed information, please contact us with your customized requests.

Analysis Pipeline

Deliverables

• The original sequencing data
• Experimental results
• Data analysis report
• Details in MeDIP Sequencing (MeDIP-Seq) for your writing (customization)

With professional bioinformatics capability, CD Genomics offers high-quality MeDIP-Seq as an end-to-end, genome-wide epigenetic service to identify differentially methylated regions, and ultimately help to expedite epigenetic research. If you have additional requirements or questions, please feel free to contact us.

Reference:

1. Taiwo O, Wilson G A, Morris T, et al. Methylome analysis using MeDIP-seq with low DNA concentrations. Nature protocols, 2012, 7(4): 617.

Partial results are shown below:

1. Compared with other methods, what are the advantages of MeDIP sequencing?

Currently, sequencing-based approach for methylome analysis can be classified into bisulfite conversion-based and enrichment-based approaches. Bisulfite conversion-based methods include whole-genome or targeted bisulfite sequencing or reduced representation bisulfite sequencing (RRBS). Although bisulfite conversion-based methods are considered to be the gold standard of DNA methylation analysis at single-base resolution, they cannot distinguish between mC and hmC. Additionally, whole genome bisulfite sequencing is expensive for application on large sample sizes and by smaller research groups. And RRBS can only provide limited genome coverage (5-10%) and is centered on CpG island and promoter regions.

Besides MeDIP sequencing, enrichment-based technologies also include MBD-seq, which uses the methyl-binding protiens MBD2 and MDB3L1, and methylCap-seq that use methyl-binding domain of MECP2 for methyl capture. But MBD-seq and methylCap-seq are restricted to the analysis of mCG and whole-genome protocols often require high concentrations of genomic DNA (more than 1,000 ng). Therefore, MeDIP is a versatile, accurate, and costly method with a low input DNA requirement and is applicable to a wide range of samples and studies.

2. What are the requirements for MeDIP sequencing samples?

MeDIP sequencing samples require reference genome or sequences for alignment, and the assembled results directly affect the accuracy of data analysis. Reference genome from closely related species or assembled results from transcriptome can also be used despite the loss of partial methylation information.

3. What factors can affect the results of MeDIP-seq?

Every process involved in MeDIP-seq may affect the results, especially the immunoprecipitation and PCR. Immunoprecipitation is the process to enrich methylated regions, and reaction conditions may influence the enrichment results. If insufficient DNA is recycled, PCR is used to scale it up, which can biase results. Furthermore, contamination should be avoided throughout the whole course.

Epigenetic alterations in TRAMP mice: epigenome DNA methylation profiling using MeDIP-seq

Journal: Cells & Bioscience
Published: 12 January 2018

Abstract

The authors profiled the methylome of the mouse prostate (TRAMP) cancer model and to analyze the crosstalk among targeted genes and the related functional pathway. By utilizing MeDIP sequencing, they analyzed DNA methylation profiles and performed relevant informatics analyses, which could provide strategies for prevention and treatment approaches for prostate cancer.

Materials & Methods

Results

1. MeDIP-seq results comparison

In total, 2147 genes between TRAMP and control animals showed a significant change in methylated peaks. Compared with the control, significantly increased methylation of 1042 genes and significantly decreased methylation of 1105 genes were observed in TRAMP. Four genes of interest, DYNC1I1, SLC1A4, XRCC6BP1, and TTR were analyzed by IGV. TRAMP mice showed increased methylation ratio of DYNC111 and SLC1A4, and decreased methylation ratio of TTR and XRCC6BP1, which was in accordance with the MeDIP-seq results.

Figure 1. integrative genomics viewer visualization of the aligned reads' distribution against reference genome for four target genes, DYNC1I1, SLC1A4, XRCC6BP1, and TTR.

2. qPCR validation of selected gene expression

The expression levels of CRYZ, DYNC1I1, HNMT, SLC1A4, and TTR were measured in both TRAMP and wide type group (Figure 2). Among them, TTR expression was increased by 9.05-fold over WT. And the expression levels of TTR were significantly higher in prostate cancer tissue than in normal and benign prostate hyperplasia tissue. Furthermore, they found decreased methylation in promoter region of TTR but increased gene expression. In contrast, DNA methylation in the gene body or downstream may or may not follow a reciprocal relationship.

Figure 2. Comparison of miRNA expression of CRYZ, DYNC1I1, HNMT, SLC1A4, and TTR among wide type and TRAMP mice prostate samples.

3. Canonical pathway, diseases and functions and network analyses by IPA

The 2147 genes with significant change in methylation were analyzed by IPA software package. The cancer-related networks accounted for the majority (Table 2), which suggested that the difference between the TRAMP and control lay in organ development and cancer development. The most associated disease, cancer, gastrointestinal disease, organismal abnormalities, reproductive system disease and dermatological diseases were ranked within the top five (Figure 3).

Table 1. Top ten altered canonical pathways by IPA

Table 2. Top networks analyzed by IPA.

Figure 3. Top five associated disease categories (a) and top five cancer subtypes (b) analyzed by IPA.

Discussion

Analysis of canonical pathway would provide information for the development of new therapeutic targets. As shown in Figure 4, the genes with significant methylation in the top canonical pathway was the neuropathic pain signaling pathway, which is consistent with the former finding that the most common malignancy in TRAMP is of neuroendocrine origin. Table 3 lists the genes involved in this pathway that showed modified methylation. CREB was found to be closely related to cellular proliferation, differentiation and adaptive responses in the neuronal system, and methylation of the CREB1 gene was found to be decreased by 2.274 in TRAMP. Furthermore, CREB was also found to regulate other carcinogenesis pathways. All of these data indicate that CREB is highly linked with cancer therapy and may be new strategy for prostate cancer prevention and therapy.

Figure 4. Genes mapped to the canonical neuropathic pain signaling pathway by IPA.

Table 3. Altered methylation genes mapped to the neuropathic pain signaling pathway by IPA.

Reference:

1. Li W, Huang Y, Sargsyan D, et al. Epigenetic alterations in TRAMP mice: epigenome DNA methylation profiling using MeDIP-seq. Cell & bioscience, 2018, 8(1): 3.