MBDDiff: an R package designed specifically for processing MBDcap-seq datasets.

BACKGROUND: Since its initial discovery in 1975, DNA methylation has been intensively studied and shown to be involved in various biological processes, such as development, aging and tumor progression. Many experimental techniques have been developed to measure the level of DNA methylation. Methyl-CpG binding domain-based capture followed by high-throughput sequencing (MBDCap-seq) is a widely used method for characterizing DNA methylation patterns in a genome-wide manner. However, current methods for processing MBDCap-seq datasets does not take into account of the region-specific genomic characteristics that might have an impact on the measurements of the amount of methylated DNA (signal) and background fluctuation (noise). Thus, specific software needs to be developed for MBDCap-seq experiments. RESULTS: A new differential methylation quantification algorithm for MBDCap-seq, MBDDiff, was implemented. To evaluate the performance of the MBDDiff algorithm, a set of simulated signal based on negative binomial and Poisson distribution with parameters estimated from real MBDCap-seq datasets accompanied with different background noises were generated, and then performed against a set of commonly used algorithms for MBDCap-seq data analysis in terms of area under the ROC curve (AUC), number of false discoveries and statistical power. In addition, we also demonstrated the effective of MBDDiff algorithm to a set of in-house prostate cancer samples, endometrial cancer samples published earlier, and a set of public-domain triple negative breast cancer samples to identify potential factors that contribute to cancer development and recurrence. CONCLUSIONS: In this paper we developed an algorithm, MBDDiff, designed specifically for datasets derived from MBDCap-seq. MBDDiff contains three modules: quality assessment of datasets and quantification of DNA methylation; determination of differential methylation of promoter regions; and visualization functionalities. Simulation results suggest that MBDDiff performs better compared to MEDIPS and DESeq in terms of AUC and the number of false discoveries at different levels of background noise. MBDDiff outperforms MEDIPS with increased backgrounds noise, but comparable performance when noise level is lower. By applying MBDDiff to several MBDCap-seq datasets, we were able to identify potential targets that contribute to the corresponding biological processes. Taken together, MBDDiff provides user an accurate differential methylation analysis for data generated by MBDCap-seq, especially under noisy conditions.

Bacteria pathogens drive host colonic epithelial cell promoter hypermethylation of tumor suppressor genes in colorectal cancer.

BACKGROUND: Altered microbiome composition and aberrant promoter hypermethylation of tumor suppressor genes (TSGs) are two important hallmarks of colorectal cancer (CRC). Here we performed concurrent 16S rRNA gene sequencing and methyl-CpG binding domain-based capture sequencing in 33 tissue biopsies (5 normal colonic mucosa tissues, 4 pairs of adenoma and adenoma-adjacent tissues, and 10 pairs of CRC and CRC-adjacent tissues) to identify significant associations between TSG promoter hypermethylation and CRC-associated bacteria, followed by functional validation of the methylation-associated bacteria. RESULTS: Fusobacterium nucleatum and Hungatella hathewayi were identified as the top two methylation-regulating bacteria. Targeted analysis on bona fide TSGs revealed that H. hathewayi and Streptococcus spp. significantly correlated with CDX2 and MLH1 promoter hypermethylation, respectively. Mechanistic validation with cell-line and animal models revealed that F. nucleatum and H. hathewayi upregulated DNA methyltransferase. H. hathewayi inoculation also promoted colonic epithelial cell proliferation in germ-free and conventional mice. CONCLUSION: Our integrative analysis revealed previously unknown epigenetic regulation of TSGs in host cells through inducing DNA methyltransferase by F. nucleatum and H. hathewayi, and established the latter as CRC-promoting bacteria. Video abstract.

DNA Methylation Analysis.

DNA methylation is a process by which methyl groups are added to cytosine or adenine. DNA methylation can change the activity of the DNA molecule without changing the sequence. Methylation of 5-methylcytosine (5mC) is widespread in both eukaryotes and prokaryotes, and it is a very important epigenetic modification event, which can regulate gene activity and influence a number of key processes such as genomic imprinting, cell differentiation, transcriptional regulation, and chromatin remodeling. Profiling DNA methylation across the genome is critical to understanding the influence of methylation in normal biology and diseases including cancer. Recent discoveries of 5-methylcytosine (5mC) oxidation derivatives including 5-hydroxymethylcytosine (5hmC), 5-formylcytsine (5fC), and 5-carboxycytosine (5caC) in mammalian genome further expand our understanding of the methylation regulation. Genome-wide analyses such as microarrays and next-generation sequencing technologies have been used to assess large fractions of the methylome. A number of different quantitative approaches have also been established to map the DNA epigenomes with single-base resolution, as represented by the bisulfite-based methods, such as classical bisulfite sequencing, pyrosequencing etc. These methods have been used to generate base-resolution maps of 5mC and its oxidation derivatives in genomic samples. The focus of this chapter is to provide the methodologies that have been developed to detect the cytosine derivatives in the genomic DNA.