A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters

Yang, Youngik,Nephew, Kenneth,Kim, Sun BioMed Central 2012 BMC bioinformatics Vol.13 No.suppl3

<P><B>Background</B></P><P>DNA methylation is essential for normal development and differentiation and plays a crucial role in the development of nearly all types of cancer. Aberrant DNA methylation patterns, including genome-wide hypomethylation and region-specific hypermethylation, are frequently observed and contribute to the malignant phenotype. A number of studies have recently identified distinct features of genomic sequences that can be used for modeling specific DNA sequences that may be susceptible to aberrant CpG methylation in both cancer and normal cells. Although it is now possible, using next generation sequencing technologies, to assess human methylomes at base resolution, no reports currently exist on modeling cell type-specific DNA methylation susceptibility. Thus, we conducted a comprehensive modeling study of cell type-specific DNA methylation susceptibility at three different resolutions: CpG dinucleotides, CpG segments, and individual gene promoter regions.</P><P><B>Results</B></P><P>Using a k-mer mixture logistic regression model, we effectively modeled DNA methylation susceptibility across five different cell types. Further, at the segment level, we achieved up to 0.75 in AUC prediction accuracy in a 10-fold cross validation study using a mixture of k-mers.</P><P><B>Conclusions</B></P><P>The significance of these results is three fold: 1) this is the first report to indicate that CpG methylation susceptible 'segments' exist; 2) our model demonstrates the significance of certain k-mers for the mixture model, potentially highlighting DNA sequence features (k-mers) of differentially methylated, promoter CpG island sequences across different tissue types; 3) as only 3 or 4 bp patterns had previously been used for modeling DNA methylation susceptibility, ours is the first demonstration that 6-mer modeling can be performed without loss of accuracy.</P>

BioVLAB-MMIA-NGS: microRNA–mRNA integrated analysis using high-throughput sequencing data

Chae, Heejoon,Rhee, Sungmin,Nephew, Kenneth P.,Kim, Sun Oxford University Press 2015 Bioinformatics Vol.31 No.2

<P>Motivation: It is now well established that microRNAs (miRNAs) play a critical role in regulating gene expression in a sequence-specific manner, and genome-wide efforts are underway to predict known and novel miRNA targets. However, the integrated miRNA-mRNA analysis remains a major computational challenge, requiring powerful informatics systems and bioinformatics expertise. Results: The objective of this study was to modify our widely recognized Web server for the integrated mRNA-miRNA analysis (MMIA) and its subsequent deployment on the Amazon cloud (BioVLAB-MMIA) to be compatible with high-throughput platforms, including next-generation sequencing (NGS) data (e.g. RNA-seq). We developed a new version called the BioVLAB-MMIA-NGS, deployed on both Amazon cloud and on a high-performance publicly available server called MAHA. By using NGS data and integrating various bioinformatics tools and databases, BioVLAB-MMIA-NGS offers several advantages. First, sequencing data is more accurate than array-based methods for determining miRNA expression levels. Second, potential novel miRNAs can be detected by using various computational methods for characterizing miRNAs. Third, because miRNA-mediated gene regulation is due to hybridization of an miRNA to its target mRNA, sequencing data can be used to identify many-to-many relationship between miRNAs and target genes with high accuracy.</P>

Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes

Chae, Heejoon,Park, Jinwoo,Lee, Seong-Whan,Nephew, Kenneth P.,Kim, Sun Oxford University Press 2013 Nucleic acids research Vol.41 No.9

<P>CpG islands are GC-rich regions often located in the 5′ end of genes and normally protected from cytosine methylation in mammals. The important role of CpG islands in gene transcription strongly suggests evolutionary conservation in the mammalian genome. However, as CpG dinucleotides are over-represented in CpG islands, comparative CpG island analysis using conventional sequence analysis techniques remains a major challenge in the epigenetics field. In this study, we conducted a comparative analysis of all CpG island sequences in 10 mammalian genomes. As sequence similarity methods and character composition techniques such as information theory are particularly difficult to conduct, we used exact patterns in CpG island sequences and single character discrepancies to identify differences in CpG island sequences. First, by calculating genome distance based on rank correlation tests, we show that k-mer and k-flank patterns around CpG sites can be used to correctly reconstruct the phylogeny of 10 mammalian genomes. Further, we used various machine learning algorithms to demonstrate that CpG islands sequences can be characterized using k-mers. In addition, by testing a human model on the nine different mammalian genomes, we provide the first evidence that k-mer signatures are consistent with evolutionary history.</P>

Genome-Wide Analysis and Modeling of DNA Methylation Susceptibility in 30 Breast Cancer Cell Lines by Using CpG Flanking Sequences

An, J.,Kim, K.,Rhee, S.-M.,Chae, H.,Nephew, K.P.,Kim, S. World Scientific Publishing 2013 Journal of bioinformatics and computational biolog Vol.11 No.3

BioVLAB-mCpG-SNP-<i>EXPRESS</i>: A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data

Chae, Heejoon,Lee, Sangseon,Seo, Seokjun,Jung, Daekyoung,Chang, Hyeonsook,Nephew, Kenneth P.,Kim, Sun Elsevier 2016 Methods Vol.111 No.-

<P>In this study, we report a system called BioVLAB-mCpG-SNP-EXPRESS for the integrated analysis of DNA methylation, sequence variation (SNPs), and gene expression for distinguishing cellular phenotypes at the pairwise and multiple phenotype levels. The system can be deployed on either the Amazon cloud or a publicly available high-performance computing node, and the data analysis and exploration of the analysis result can be conveniently done using a web-based interface. In order to alleviate analysis complexity, all the process are fully automated, and graphical workflow system is integrated to represent real-time analysis progression. The BioVLAB-mCpG-SNP-EXPRESS system works in three stages. First, it processes and analyzes multi-omics data as input in the form of the raw data, i.e., FastQ files. Second, various integrated analyses such as methylation vs. gene expression and mutation vs. methylation are performed. Finally, the analysis result can be explored in a number of ways through a web interface for the multi-level, multi-perspective exploration. Multi-level interpretation can be done by either gene, gene set, pathway or network level and multi-perspective exploration can be explored from either gene expression, DNA methylation, sequence variation, or their relationship perspective. The utility of the system is demonstrated by performing analysis of phenotypically distinct 30 breast cancer cell line data set. BioVLAB-mCpG-SNP-EXPRESS is available at http://biohealth.snu.ac.krfsoftwareibiovlab_mcpg_ snp_expressi. (C) 2016 Elsevier Inc. All rights reserved.</P>

BioVLAB-MMIA: A Cloud Environment for microRNA and mRNA Integrated Analysis (MMIA) on Amazon EC2

Hyungro Lee,Youngik Yang,Heejoon Chae,Seungyoon Nam,Donghoon Choi,Tangchaisin, P.,Herath, C.,Marru, S.,Nephew, K. P.,Sun Kim IEEE 2012 IEEE transactions on nanobioscience Vol.11 No.3

<P>MicroRNAs, by regulating the expression of hundreds of target genes, play critical roles in developmental biology and the etiology of numerous diseases, including cancer. As a vast amount of microRNA expression profile data are now publicly available, the integration of microRNA expression data sets with gene expression profiles is a key research problem in life science research. However, the ability to conduct genome-wide microRNA-mRNA (gene) integration currently requires sophisticated, high-end informatics tools, significant expertise in bioinformatics and computer science to carry out the complex integration analysis. In addition, increased computing infrastructure capabilities are essential in order to accommodate large data sets. In this study, we have extended the BioVLAB cloud workbench to develop an environment for the integrated analysis of microRNA and mRNA expression data, named BioVLAB-MMIA. The workbench facilitates computations on the Amazon EC2 and S3 resources orchestrated by the XBaya Workflow Suite. The advantages of BioVLAB-MMIA over the web-based MMIA system include: 1) readily expanded as new computational tools become available; 2) easily modifiable by re-configuring graphic icons in the workflow; 3) on-demand cloud computing resources can be used on an “as needed” basis; 4) distributed orchestration supports complex and long running workflows asynchronously. We believe that BioVLAB-MMIA will be an easy-to-use computing environment for researchers who plan to perform genome-wide microRNA-mRNA (gene) integrated analysis tasks.</P>

A unique histone deacetylase inhibitor alters microRNA expression and signal transduction in chemoresistant ovarian cancer cells.

Balch, Curt,Naegeli, Kaleb,Nam, Seungyoon,Ballard, Brett,Hyslop, Alan,Melki, Christina,Reilly, Elizabeth,Hur, Man-Wook,Nephew, Kenneth P Landes Bioscience 2012 Cancer Biology & Therapy Vol.13 No.8

<P>Previously, we demonstrated potent antineoplastic activity of a distinctive histone deacetylase inhibitor (HDACI), AR42, against chemoresistant CP70 ovarian cancer cells in vitro and in vivo. Here, in follow-up to that work, we explored AR42 global mechanisms-of-action by examining drug-associated, genome-wide microRNA and mRNA expression profiles, which differed from those of the well-studied HDACI vorinostat. Expression of microRNA genes in negative correlation with their 'target' coding gene (mRNA) transcripts, and transcription factor genes with expression positively correlated with coding genes having their cognate binding sites, were identified and subjected to gene ontology analyses. Those evaluations showed AR42 gene expression patterns to negatively correlate with Wnt signaling (> 18-fold induction of SFRP1), the epithelial-to-mesenchymal transition (40% decreased ATF1), and cell cycle progression (33-fold increased 14-3-3σ). By contrast, AR42 transcriptome alterations correlated positively with extrinsic ('death receptor') apoptosis (> 2.3-fold upregulated DAPK) and favorable ovarian cancer histopathology and prognosis. Inhibition of Wnt signaling was experimentally validated by: (1) > 2.6-fold reduced Wnt reporter activity; and (2) 36% reduction in nuclear, activated β-catenin. Likely AR42 induction of multiple (type I or type II autophagic) cell death cascades was further supported by 57% decreased reliance upon reactive oxygen, increased mitochondrial membrane disruption, and caspase independence, as compared with vorinostat. Taken together, we demonstrate distinct antineoplastic pathway alterations, in aggressive ovarian cancer cells, following treatment with a promising HDACI, AR42. These combined computational and experimental approaches may also represent a straightforward means for mechanistic studies of other promising antineoplastics, and/or the identification of agents that may complement epigenetic therapies.</P>

Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer

Rhee, Je-Keun,Kim, Kwangsoo,Chae, Heejoon,Evans, Jared,Yan, Pearlly,Zhang, Byoung-Tak,Gray, Joe,Spellman, Paul,Huang, Tim H.-M.,Nephew, Kenneth P.,Kim, Sun Oxford University Press 2013 Nucleic acids research Vol.41 No.18

<P>Aberrant DNA methylation of CpG islands, CpG island shores and first exons is known to play a key role in the altered gene expression patterns in all human cancers. To date, a systematic study on the effect of DNA methylation on gene expression using high resolution data has not been reported. In this study, we conducted an integrated analysis of MethylCap-sequencing data and Affymetrix gene expression microarray data for 30 breast cancer cell lines representing different breast tumor phenotypes. As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods. On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale. On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B. Our integrated analysis demonstrates that methylation status of different genomic regions may play a key role in establishing transcriptional patterns in molecular subtypes of human breast cancer.</P>

Derepression of <i>CLDN3</i> and <i>CLDN4</i> during ovarian tumorigenesis is associated with loss of repressive histone modifications

Kwon, Mi Jeong,Kim, Sung-Su,Choi, Yoon-La,Jung, Hun Soon,Balch, Curt,Kim, Su-Hyeong,Song, Yong-Sang,Marquez, Victor E.,Nephew, Kenneth P.,Shin, Young Kee Oxford University Press 2010 Carcinogenesis Vol.31 No.6

<P>Unlike epigenetic silencing of tumor suppressor genes, the role of epigenetic derepression of cancer-promoting genes or oncogenes in carcinogenesis remains less well understood. The tight junction proteins claudin-3 and claudin-4 are frequently overexpressed in ovarian cancer and their overexpression was previously reported to promote the migration and invasion of ovarian epithelial cells. Here, we show that the expression of claudin-3 and claudin-4 is repressed in ovarian epithelial cells in association with promoter ‘bivalent’ histone modifications, containing both the activating trimethylated histone H3 lysine 4 (H3K4me3) mark and the repressive mark of trimethylated histone H3 lysine 27 (H3K27me3). During ovarian tumorigenesis, derepression of <I>CLDN3</I> and <I>CLDN4</I> expression correlates with loss of H3K27me3 in addition to trimethylated histone H4 lysine 20 (H4K20me3), another repressive histone modification. Although <I>CLDN4</I> repression was accompanied by both DNA hypermethylation and repressive histone modifications, DNA methylation was not required for <I>CLDN3</I> repression in immortalized ovarian epithelial cells. Moreover, activation of both <I>CLDN3</I> and <I>CLDN4</I> in ovarian cancer cells was associated with simultaneous changes in multiple histone modifications, whereas H3K27me3 loss alone was insufficient for their derepression. <I>CLDN4</I> repression was robustly reversed by combined treatment targeting both DNA demethylation and histone acetylation. Our study strongly suggests that in addition to the well-known chromatin-associated silencing of tumor suppressor genes, epigenetic derepression by the conversely related loss of repressive chromatin modifications also contributes to ovarian tumorigenesis via activation of cancer-promoting genes or candidate oncogenes.</P>