Search

DNA methylation has been characterized as the representative example of epigenetic modifications and implicated in numerous biological processes, such as genomic imprinting and X chromosome inactivation. It primarily occurs at CpG dinucleotides in mammals and plays a critical role in transcriptional regulations. Examination of DNA methylation patterns in gene(s) or across a genome is vital to understand the role of epigenetic modulation in the progress of development and tumorigenesis. Currently, lots of approaches have been developed to investigate DNA methylation patterns for either limited regions or for genome-scale studies, but some of them rely on using restriction enzymes. In this chapter, we describe two commonly used protocols to detect enrichment of methylated DNA regions, namely, methylated DNA immunoprecipitation (MeDIP) and capture of methylated DNA by methyl-CpG binding domain-based (MBD) proteins (MBDCap). They are the most economical and effective methods to study DNA methylation either at a single locus or in genome-wide scale.

DNA motifs are short sequences varying from 6 to 25 bp and can be highly variable and degenerated. One major approach for predicting transcription factor (TF) binding is using position weight matrix (PWM) to represent information content of regulatory sites; however, when used as the sole means of identifying binding sites suffers from the limited amount of training data available and a high rate of false-positive predictions. ChIPMotifs program is a de novo motif finding tool developed for ChIP-based high-throughput data, and W-ChIPMotifs is a Web application tool for ChIPMotifs. It composes various ab initio motif discovery tools such as MEME, MaMF, Weeder and optimizes the significance of the detected motifs by using bootstrap re-sampling error estimation and a Fisher test. Using these techniques, we determined a PWM for OCT4 which is similar to canonical OCT4 consensus sequence. In a separate study, we also use de novo motif discovery to suggest that ZNF263 binds to a 24-nt site that differs from the motif predicted by the zinc finger code in several positions.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a high-throughput antibody-based method to study genome-wide protein-DNA binding interactions. ChIP-seq technology allows scientist to obtain more accurate data providing genome-wide coverage with less starting material and in shorter time compared to older ChIP-chip experiments. Herein we describe a step-by-step guideline in analyzing ChIP-seq data including data preprocessing, nonlinear normalization to enable comparison between different samples and experiments, statistical-based method to identify differential binding sites using mixture modeling and local false discovery rates (fdrs), and binding pattern characterization. In addition, we provide a sample analysis of ChIP-seq data using the steps provided in the guideline.

Differential Methylation Hybridization (DMH) is a high-throughput DNA methylation screening tool that utilizes methylation-sensitive restriction enzymes to profile methylated fragments by hybridizing them to a CpG island microarray. This array contains probes spanning all the 27,800 islands annotated in the UCSC Genome Browser. Herein we describe a DMH protocol with clearly identified quality control points. In this manner, samples that are unlikely to provide good read-outs for differential methylation profiles between the test and the control samples will be identified and repeated with appropriate modifications. The step-by-step laboratory DMH protocol is described. In addition, we provide descriptions regarding DMH data analysis, including image quantification, background correction, and statistical procedures for both exploratory analysis and more formal inferences. Issues regarding quality control are addressed as well.

Differential methylation hybridization (DMH) is a high-throughput DNA methylation screening tool that utilizes methylation-sensitive restriction enzymes to profile methylated fragments by hybridizing them to a CpG island microarray. This array contains probes spanning all the 27,800 islands annotated in the UCSC Genome Browser. Herein we describe a revised DMH protocol with clearly identified quality control points. In this manner, samples that are unlikely to provide good readouts for differential methylation profiles between the test and the control samples will be identified and repeated with appropriate modifications. In addition to the step-by-step laboratory DMH protocol, we also provide a detailed description regarding DMH data analysis. The suggested microarray platform contains 244,000 probes and it can be a daunting barrier for researchers with no prior experience in analyzing DNA methylation data. We have created a data analysis pipeline available in a user friendly, publicly available interface, the Broad Institute's GenePattern software, which can be accessed at http://bisr.osumc.edu :8080/gp. This permits scientists to use our existing data analysis modules on their own data. As we continue to update our analysis algorithm and approaches to integrate high-throughput methylation data with other large-scale data types, we will make these new computation protocols available through the GenePattern platform.

The methylated DNA immunoprecipitation microarray (MeDIP-chip) is a genome-wide, high-resolution approach to detect DNA methylation in whole genome or CpG (cytosine base followed by a guanine base) islands. The method utilizes anti-methylcytosine antibody to immunoprecipitate DNA that contains highly methylated CpG sites. Enriched methylated DNA can be interrogated using DNA microarrays or by massive parallel sequencing techniques. This combined approach allows researchers to rapidly identify methylated regions in a genome-wide manner, and compare DNA methylation patterns between two samples with diversely different DNA methylation status. MeDIP-chip has been applied successfully for analyses of methylated DNA in the different targets including animal and plant tissues (1, 2). Here we present a MeDIP-chip protocol that is routinely used in our laboratory, illustrated with specific examples from MeDIP-chip analysis of breast cancer cell lines. Potential technical pitfalls and solutions are also provided to serve as workflow guidelines.

Breast cancer growth is regulated by the coordinated action of estrogen receptor UPalpha and multiple signaling pathways. Profiling of estrogen-regulated gene expression provides mechanisms on estrogenic control of proliferation in breast cancer cells. However, little is known of the hierarchy and the trans-acting factors in ER UPalpha signaling network. Chromatin immunoprecipitation microarray (or ChIP-chip) has emerged as a powerful technique to identify the binding sites of transcription factors and profile the histone modifications in a genome-wide scale. This technology permits, for the first time, comprehensive identification and characterization of direct ERα target genes. Recruitment of ERα to the genome does not only occur at proximal promoter regions, but also involves distal enhancer elements. By using integrated ChIP-chip and bioinformatics analyses, new transcription factor partners (FoxA1 and c-MYC) are being uncovered that play critical roles in ERα -mediated transcription. Based on these findings, a model is constructed to show the complex network of ERα signaling. These new insights will support the development of more precise therapeutic regimen for breast cancer treatment.

The methylation-specific oligonucleotide (MSO) microarray is a high-throughput approach capable of detecting DNA methylation in genes across several CpG sites. Based on the bisulfite modification of DNA that converts unmethylated cytosines to uracil but leaves the 5'methylcytosine intact, the method utilizes short oligonucleotides corresponding to the methylated and unmethylated alleles as probes affixed on solid support and products amplified from bisulfite-treated DNA as targets for hybridization. MSO is suitable for examining a panel of genes across multiple clinical samples. This approach can generate a robust dataset for discovering profiles of gene methylation in cancer with aberrant DNA methylation in the neoplastic genome and widespread hypermethylation in tumor suppressor genes. MSO and other oligonucleotide-based arrays have been applied successfully for analyses of single genes and have been useful in delineating and predicting tumor subgroups using clustering methods. Here we focus on design criteria important to the interrogation of multiple CpG sites across several genes.