Statistical quantification of methylation levels by next-generation sequencing

Genome-wide DNA methylation in suicide victims revealing impact on gene expression

BACKGROUND

Suicidal behavior is a multifactorial, polygenic state that affects millions worldwide. It is a result of interplay between hereditary and environmental factors, tied together by epigenetics. Despite vast knowledge on suicidality complete mechanism and factors leading to suicide are unknown. However there is an indication between changes in DNA methylation patterns and suicidal behavior.

METHODS

To identify differential methylation we formed a homogenous group of male suicide victims who died by hanging and control group. Altogether our study included 18 subjects in which two brain regions, Brodmann area 9 (9 suicide victims and 9 controls)) and hippocampus (6 suicide victims and 6 controls) were investigated using next-generation sequencing (NGS).

RESULTS

Our results have shown several differences in methylation level between suicide victims and controls in both brain regions (>25% difference in methylation and q-value < 0.01), with gene ontology pointing towards cell structural integrity and nervous system regulation. Additional gene expression analysis identified changes in two genes, ZNF714 (p-value = 0.002) and NRIP3 (p-value = 0.046).

LIMITATIONS

Major limitation is small sample size. Our analysis was conducted on brain tissue including different cell types so the results are a representation of a methylation pattern for the whole brain tissue sample.

CONCLUSIONS

We performed a preliminary methylation study with single base pair resolution using NGS on one of the world populations with a very high suicide risk. Obtained results offer novel insights into altered methylation patterns in suicide victims, which could provide a starting point for further studies on clinical samples with highly expressed suicide risk.

Epigenetic modifications in prostate cancer

Prostate cancer is the most common cancer in men and the second leading cause of cancer deaths in men in France. Apart from the genetic alterations in prostate cancer, epigenetics modifications are involved in the development and progression of this disease. Epigenetic events are the main cause in gene regulation and the three most epigenetic mechanisms studied include DNA methylation, histone modifications and microRNA expression. In this review, we summarized epigenetic mechanisms in prostate cancer. Epigenetic drugs that inhibit DNA methylation, histone methylation and histone acetylation might be able to reactivate silenced gene expression in prostate cancer. However, further understanding of interactions of these enzymes and their effects on transcription regulation in prostate cancer is needed and has become a priority in biomedical research. In this study, we summed up epigenetic changes with emphasis on pharmacologic epigenetic target agents.

Epigenetics: relevance and implications for public health

Improved understanding of the multilayer regulation of the human genome has led to a greater appreciation of environmental, nutritional, and epigenetic risk factors for human disease. Chromatin remodeling, histone tail modifications, and DNA methylation are dynamic epigenetic changes responsive to external stimuli. Careful interpretation can provide insights for actionable public health through collaboration between population and basic scientists and through integration of multiple data sources. We review key findings in environmental epigenetics both in human population studies and in animal models, and discuss the implications of these results for risk assessment and public health protection. To ultimately succeed in identifying epigenetic mechanisms leading to complex phenotypes and disease, researchers must integrate the various animal models, human clinical approaches, and human population approaches while paying attention to life-stage sensitivity, to generate effective prescriptions for human health evaluation and disease prevention.

Big data analysis using modern statistical and machine learning methods in medicine

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

A novel method for identification and quantification of consistently differentially methylated regions

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach

Affinity capture of DNA methylation combined with high-throughput sequencing strikes a good balance between the high cost of whole genome bisulfite sequencing and the low coverage of methylation arrays. We present BayMeth, an empirical Bayes approach that uses a fully methylated control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels. BayMeth improves on existing methods, allows explicit modeling of copy number variation, and offers computationally efficient analytical mean and variance estimators. BayMeth is available in the Repitools Bioconductor package.

The role of MGMT testing in clinical practice: a report of the association for molecular pathology

Recent advances in modern molecular technologies allow for the examination and measurement of cancer-related genomic changes. The number of molecular tests for evaluation of diagnostic, prognostic, or predictive markers is expected to increase. In recent years, O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been firmly established as a biomarker in patients diagnosed with gliomas, for both clinical trials and routine clinical management. Similarly, molecular markers, such as loss of heterozygosity (LOH) for 1p/19q have already demonstrated clinical utility in treatment of oligodendroglial tumors, and others might soon show clinical utility. Furthermore, nonrandom associations are being discovered among MGMT, 1p/19q LOH, isocitrate dehydrogenase (IDH) mutations, and other tumor-specific modifications that could possibly enhance our ability to predict outcome and response to therapy. While pathologists are facing new and more complicated requests for clinical genomic testing, clinicians are challenged with increasing numbers of molecular data coming from molecular pathology and genomic medicine. Both pathologists and oncologists need to understand the clinical utility of molecular tests and test results, including issues of turnaround time, and their impact on the application of targeted treatment regimens. This review summarizes the existing data that support the rationale for MGMT promoter methylation testing and possibly other molecular testing in clinically defined glioma subtypes. Various molecular testing platforms for evaluation of MGMT methylation status are also discussed.

Characterization of undermethylated sites in Vibrio cholerae

The activities of DNA methyltransferases are important for a variety of cellular functions in bacteria. In this study, we developed a modified high-throughput technique called methyl homopolymer tail mediated sequencing (methyl HTM-seq) to identify the undermethylated sites in the Vibrio cholerae genome for the two DNA methyltransferases, Dam, an adenine methyltransferase, and VchM, a cytosine methyltransferase, during growth in rich medium in vitro. Many of the undermethylated sites occurred in intergenic regions, and for most of these sites, we identified the transcription factors responsible for undermethylation. This confirmed the presence of previously hypothesized DNA-protein interactions for these transcription factors and provided insight into the biological state of these cells during growth in vitro. DNA adenine methylation has previously been shown to mediate heritable epigenetic switches in gene regulation. However, none of the undermethylated Dam sites tested showed evidence of regulation by this mechanism. This study is the first to identify undermethylated adenines and cytosines genomewide in a bacterium using second-generation sequencing technology.

Current state-of-art of sequencing technologies for plant genomics research

A number of next-generation sequencing (NGS) technologies such as Roche/454, Illumina and AB SOLiD have recently become available. These technologies are capable of generating hundreds of thousands or tens of millions of short DNA sequence reads at a relatively low cost. These NGS technologies, now referred as second-generation sequencing (SGS) technologies, are being utilized for de novo sequencing, genome re-sequencing, and whole genome and transcriptome analysis. Now, new generation of sequencers, based on the 'next-next' or third-generation sequencing (TGS) technologies like the Single-Molecule Real-Time (SMRT™) Sequencer, Heliscope™ Single Molecule Sequencer, and the Ion Personal Genome Machine™ are becoming available that are capable of generating longer sequence reads in a shorter time and at even lower costs per instrument run. Ever declining sequencing costs and increased data output and sample throughput for NGS and TGS sequencing technologies enable the plant genomics and breeding community to undertake genotyping-by-sequencing (GBS). Data analysis, storage and management of large-scale second or TGS projects, however, are essential. This article provides an overview of different sequencing technologies with an emphasis on forthcoming TGS technologies and bioinformatics tools required for the latest evolution of DNA sequencing platforms.