Impact of mutations in DNA methylation modification genes on genome-wide methylation landscapes and downstream gene activations in pan-cancer.

BACKGROUND: In cancer, mutations of DNA methylation modification genes have crucial roles for epigenetic modifications genome-wide, which lead to the activation or suppression of important genes including tumor suppressor genes. Mutations on the epigenetic modifiers could affect the enzyme activity, which would result in the difference in genome-wide methylation profiles and, activation of downstream genes. Therefore, we investigated the effect of mutations on DNA methylation modification genes such as DNMT1, DNMT3A, MBD1, MBD4, TET1, TET2 and TET3 through a pan-cancer analysis. METHODS: First, we investigated the effect of mutations in DNA methylation modification genes on genome-wide methylation profiles. We collected 3,644 samples that have both of mRNA and methylation data from 12 major cancer types in The Cancer Genome Atlas (TCGA). The samples were divided into two groups according to the mutational signature. Differentially methylated regions (DMR) that overlapped with the promoter region were selected using minfi and differentially expressed genes (DEG) were identified using EBSeq. By integrating the DMR and DEG results, we constructed a comprehensive DNA methylome profiles on a pan-cancer scale. Second, we investigated the effect of DNA methylations in the promoter regions on downstream genes by comparing the two groups of samples in 11 cancer types. To investigate the effects of promoter methylation on downstream gene activations, we performed clustering analysis of DEGs. Among the DEGs, we selected highly correlated gene set that had differentially methylated promoter regions using graph based sub-network clustering methods. RESULTS: We chose an up-regulated DEGs cluster where had hypomethylated promoter in acute myeloid leukemia (LAML) and another down-regulated DEGs cluster where had hypermethylated promoter in colon adenocarcinoma (COAD). To rule out effects of gene regulation by transcription factor (TF), if differentially expressed TFs bound to the promoter of DEGs, that DEGs did not included to the gene set that effected by DNA methylation modifiers. Consequently, we identified 54 hypomethylated promoter DMR up-regulated DEGs in LAML and 45 hypermethylated promoter DMR down-regulated DEGs in COAD. CONCLUSIONS: Our study on DNA methylation modification genes in mutated vs. non-mutated groups could provide useful insight into the epigenetic regulation of DEGs in cancer.

StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis.

BACKGROUND: Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the stress response are available in databases. With the data, an integrated analysis of multiple stresses is possible, which identifies stress-responsive genes with higher specificity because considering multiple stress can capture the effect of interference between stresses. To analyze such data, a machine learning model needs to be built. RESULTS: In this study, we developed StressGenePred, a neural network-based machine learning method, to integrate time-series transcriptome data of multiple stress types. StressGenePred is designed to detect single stress-specific biomarker genes by using a simple feature embedding method, a twin neural network model, and Confident Multiple Choice Learning (CMCL) loss. The twin neural network model consists of a biomarker gene discovery and a stress type prediction model that share the same logical layer to reduce training complexity. The CMCL loss is used to make the twin model select biomarker genes that respond specifically to a single stress. In experiments using Arabidopsis gene expression data for four major environmental stresses, such as heat, cold, salt, and drought, StressGenePred classified the types of stress more accurately than the limma feature embedding method and the support vector machine and random forest classification methods. In addition, StressGenePred discovered known stress-related genes with higher specificity than the Fisher method. CONCLUSIONS: StressGenePred is a machine learning method for identifying stress-related genes and predicting stress types for an integrated analysis of multiple stress time-series transcriptome data. This method can be used to other phenotype-gene associated studies.

In silico experiment system for testing hypothesis on gene functions using three condition specific biological networks.

Determining functions of a gene requires time consuming, expensive biological experiments. Scientists can speed up this experimental process if the literature information and biological networks can be adequately provided. In this paper, we present a web-based information system that can perform in silico experiments of computationally testing hypothesis on the function of a gene. A hypothesis that is specified in English by the user is converted to genes using a literature and knowledge mining system called BEST. Condition-specific TF, miRNA and PPI (protein-protein interaction) networks are automatically generated by projecting gene and miRNA expression data to template networks. Then, an in silico experiment is to test how well the target genes are connected from the knockout gene through the condition-specific networks. The test result visualizes path from the knockout gene to the target genes in the three networks. Statistical and information-theoretic scores are provided on the resulting web page to help scientists either accept or reject the hypothesis being tested. Our web-based system was extensively tested using three data sets, such as E2f1, Lrrk2, and Dicer1 knockout data sets. We were able to re-produce gene functions reported in the original research papers. In addition, we comprehensively tested with all disease names in MalaCards as hypothesis to show the effectiveness of our system. Our in silico experiment system can be very useful in suggesting biological mechanisms which can be further tested in vivo or in vitro. AVAILABILITY: http://biohealth.snu.ac.kr/software/insilico/.

Integrated analysis of omics data using microRNA-target mRNA network and PPI network reveals regulation of Gnai1 function in the spinal cord of Ews/Ewsr1 KO mice.

BACKGROUND: Multifunctional transcription factor (TF) gene EWS/EWSR1 is involved in various cellular processes such as transcription regulation, noncoding RNA regulation, splicing regulation, genotoxic stress response, and cancer generation. Role of a TF gene can be effectively studied by measuring genome-wide gene expression, i.e., transcriptome, in an animal model of Ews/Ewsr1 knockout (KO). However, when a TF gene has complex multi-functions, conventional approaches such as differentially expressed genes (DEGs) analysis are not successful to characterize the role of the EWS gene. In this regard, network-based analyses that consider associations among genes are the most promising approach. METHODS: Networks are constructed and used to show associations among biological entities at various levels, thus different networks represent association at different levels. Taken together, in this paper, we report contributions on both computational and biological sides. RESULTS: Contribution on the computational side is to develop a novel computational framework that combines miRNA-gene network and protein-protein interaction network information to characterize the multifunctional role of EWS gene. On the biological side, we report that EWS regulates G-protein, Gnai1, in the spinal cord of Ews/Ewsr1 KO mice using the two biological network integrated analysis method. Neighbor proteins of Gnai1, G-protein complex subunits Gnb1, Gnb2 and Gnb4 were also down-regulated at their gene expression level. Interestingly, up-regulated genes, such as Rgs1 and Rgs19, are linked to the inhibition of Gnai1 activities. We further verified the altered expression of Gnai1 by qRT-PCR in Ews/Ewsr1 KO mice. CONCLUSIONS: Our integrated analysis of miRNA-transcriptome network and PPI network combined with qRT-PCR verifies that Gnai1 function is impaired in the spinal cord of Ews/Ewsr1 KO mice.

Uvrag targeting by Mir125a and Mir351 modulates autophagy associated with Ewsr1 deficiency.

The EWSR1 (EWS RNA-binding protein 1/Ewing Sarcoma Break Point Region 1) gene encodes a RNA/DNA binding protein that is ubiquitously expressed and involved in various cellular processes. EWSR1 deficiency leads to impairment of development and accelerated senescence but the mechanism is not known. Herein, we found that EWSR1 modulates the Uvrag (UV radiation resistance associated) gene at the post-transcription level. Interestingly, EWSR1 deficiency led to the activation of the DROSHA-mediated microprocessor complex and increased the level of Mir125a and Mir351, which directly target Uvrag. Moreover, the Mir125a- and Mir351-mediated reduction of Uvrag was associated with the inhibition of autophagy that was confirmed in ewsr1 knockout (KO) MEFs and ewsr1 KO mice. Taken together, our data indicate that EWSR1 is involved in the post-transcriptional regulation of Uvrag via a miRNA-dependent pathway, resulting in the deregulation of autophagy inhibition. The mechanism of Uvrag and autophagy regulation by EWSR1 provides new insights into the role of EWSR1 deficiency-related cellular dysfunction.

Determining the effect of DNA methylation on gene expression in cancer cells.

DNA methylation, a DNA modification by adding methyl group to cytosine, has an important role in the regulation of gene expression. DNA methylation is known to be associated with gene transcription by interfering with DNA-binding proteins, such as transcription factors. DNA methylation is closely related to tumorigenesis, and the methylation state of some genes can be used as a biomarker for tumorigenesis. Aberrant DNA methylation of genomic regions, including CpG islands, CpG shores, and first exons, is related to the altered gene expression pattern characteristics of all human cancers. Subheading 1 surveys recent developments on DNA methylation and gene expressions in cancer. Then we provide analysis of DNA methylation and gene expression in 30 breast cancer cell lines representing different tumor phenotypes. This study conducted an integrated analysis to identify the relationship between DNA methylation in various genomic regions and expression levels of downstream genes, using MethylCapseq data (affinity purification followed by next-generation sequencing of eluted DNA) and Affymetrix gene expression microarray data. The goal of this study was to assess genome-wide methylation profiles associated with different molecular subtypes of human breast cancer (luminal, basal A, and basal B) and to comprehensively investigate the effect of DNA methylation on gene expression in breast cancer phenotypes. This showed that methylation of genomic regions near transcription start sites, CpG island, CpG shore, and first exon was strongly associated with gene repression, and the effects of the regions on gene expression patterns were different for different molecular subtypes of breast cancer. The results further indicated that aberrant methylation of specific genomic regions was significantly associated with different breast cancer subtypes.