51
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Sensitivity of transcription factors to DNA methylation. Essays Biochem 2020; 63:727-741. [PMID: 31755929 PMCID: PMC6923324 DOI: 10.1042/ebc20190033] [Citation(s) in RCA: 161] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/28/2019] [Accepted: 10/29/2019] [Indexed: 12/17/2022]
Abstract
Dynamic binding of transcription factors (TFs) to regulatory elements controls transcriptional states throughout organism development. Epigenetics modifications, such as DNA methylation mostly within cytosine-guanine dinucleotides (CpGs), have the potential to modulate TF binding to DNA. Although DNA methylation has long been thought to repress TF binding, a more recent model proposes that TF binding can also inhibit DNA methylation. Here, we review the possible scenarios by which DNA methylation and TF binding affect each other. Further in vivo experiments will be required to generalize these models.
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52
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Rosikiewicz W, Chen X, Dominguez PM, Ghamlouch H, Aoufouchi S, Bernard OA, Melnick A, Li S. TET2 deficiency reprograms the germinal center B cell epigenome and silences genes linked to lymphomagenesis. SCIENCE ADVANCES 2020; 6:eaay5872. [PMID: 32596441 PMCID: PMC7299612 DOI: 10.1126/sciadv.aay5872] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 03/25/2020] [Indexed: 05/22/2023]
Abstract
The TET2 DNA hydroxymethyltransferase is frequently disrupted by somatic mutations in diffuse large B cell lymphomas (DLBCLs), a tumor that originates from germinal center (GC) B cells. Here, we show that TET2 deficiency leads to DNA hypermethylation of regulatory elements in GC B cells, associated with silencing of the respective genes. This hypermethylation affects the binding of transcription factors including those involved in exit from the GC reaction and involves pathways such as B cell receptor, antigen presentation, CD40, and others. Normal GC B cells manifest a typical hypomethylation signature, which is caused by AID, the enzyme that mediates somatic hypermutation. However, AID-induced demethylation is markedly impaired in TET2-deficient GC B cells, suggesting that AID epigenetic effects are partially dependent on TET2. Last, we find that TET2 mutant DLBCLs also manifest the aberrant TET2-deficient GC DNA methylation signature, suggesting that this epigenetic pattern is maintained during and contributes to lymphomagenesis.
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Affiliation(s)
- Wojciech Rosikiewicz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Xiaowen Chen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Pilar M. Dominguez
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Hussein Ghamlouch
- INSERM U1170, équipe labelisée Ligue Nationale Contre le Cancer, Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Said Aoufouchi
- CNRS UMR8200, équipe labelisée Ligue Nationale Contre le Cancer, Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Olivier A. Bernard
- INSERM U1170, équipe labelisée Ligue Nationale Contre le Cancer, Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Ari Melnick
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- The Jackson Laboratory Cancer Center, Bar Harbor, ME, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
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53
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Chiu TP, Xin B, Markarian N, Wang Y, Rohs R. TFBSshape: an expanded motif database for DNA shape features of transcription factor binding sites. Nucleic Acids Res 2020; 48:D246-D255. [PMID: 31665425 PMCID: PMC7145579 DOI: 10.1093/nar/gkz970] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/08/2019] [Accepted: 10/11/2019] [Indexed: 12/31/2022] Open
Abstract
TFBSshape (https://tfbsshape.usc.edu) is a motif database for analyzing structural profiles of transcription factor binding sites (TFBSs). The main rationale for this database is to be able to derive mechanistic insights in protein-DNA readout modes from sequencing data without available structures. We extended the quantity and dimensionality of TFBSshape, from mostly in vitro to in vivo binding and from unmethylated to methylated DNA. This new release of TFBSshape improves its functionality and launches a responsive and user-friendly web interface for easy access to the data. The current expansion includes new entries from the most recent collections of transcription factors (TFs) from the JASPAR and UniPROBE databases, methylated TFBSs derived from in vitro high-throughput EpiSELEX-seq binding assays and in vivo methylated TFBSs from the MeDReaders database. TFBSshape content has increased to 2428 structural profiles for 1900 TFs from 39 different species. The structural profiles for each TFBS entry now include 13 shape features and minor groove electrostatic potential for standard DNA and four shape features for methylated DNA. We improved the flexibility and accuracy for the shape-based alignment of TFBSs and designed new tools to compare methylated and unmethylated structural profiles of TFs and methods to derive DNA shape-preserving nucleotide mutations in TFBSs.
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Affiliation(s)
- Tsu-Pei Chiu
- Quantitative and Computational Biology, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Beibei Xin
- Quantitative and Computational Biology, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Nicholas Markarian
- Quantitative and Computational Biology, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Yingfei Wang
- Quantitative and Computational Biology, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Remo Rohs
- Quantitative and Computational Biology, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA
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54
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Wang Z, Yin J, Zhou W, Bai J, Xie Y, Xu K, Zheng X, Xiao J, Zhou L, Qi X, Li Y, Li X, Xu J. Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types. Nucleic Acids Res 2020; 48:2287-2302. [PMID: 32002550 PMCID: PMC7049702 DOI: 10.1093/nar/gkaa041] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 01/14/2020] [Indexed: 12/18/2022] Open
Abstract
Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.
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Affiliation(s)
- Zishan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jiaqi Yin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jing Bai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunjin Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kang Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xiangyi Zheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Li Zhou
- Department of Nephrology, Affiliated Hospital of Chengde Medical College, Chengde, Hebei Province, China
| | - Xiaolin Qi
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, Hainan 571199, China
| | - Yongsheng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, Hainan 571199, China.,College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 570100, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, Hainan 571199, China.,College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 570100, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, Hainan 571199, China.,College of Biomedical Information and Engineering, Hainan Medical University, Haikou, Hainan 570100, China
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55
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Tang J, Zou J, Zhang X, Fan M, Tian Q, Fu S, Gao S, Fan S. PretiMeth: precise prediction models for DNA methylation based on single methylation mark. BMC Genomics 2020; 21:364. [PMID: 32414326 PMCID: PMC7227319 DOI: 10.1186/s12864-020-6768-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 05/04/2020] [Indexed: 11/29/2022] Open
Abstract
Background The computational prediction of methylation levels at single CpG resolution is promising to explore the methylation levels of CpGs uncovered by existing array techniques, especially for the 450 K beadchip array data with huge reserves. General prediction models concentrate on improving the overall prediction accuracy for the bulk of CpG loci while neglecting whether each locus is precisely predicted. This leads to the limited application of the prediction results, especially when performing downstream analysis with high precision requirements. Results Here we reported PretiMeth, a method for constructing precise prediction models for each single CpG locus. PretiMeth used a logistic regression algorithm to build a prediction model for each interested locus. Only one DNA methylation feature that shared the most similar methylation pattern with the CpG locus to be predicted was applied in the model. We found that PretiMeth outperformed other algorithms in the prediction accuracy, and kept robust across platforms and cell types. Furthermore, PretiMeth was applied to The Cancer Genome Atlas data (TCGA), the intensive analysis based on precise prediction results showed that several CpG loci and genes (differentially methylated between the tumor and normal samples) were worthy for further biological validation. Conclusion The precise prediction of single CpG locus is important for both methylation array data expansion and downstream analysis of prediction results. PretiMeth achieved precise modeling for each CpG locus by using only one significant feature, which also suggested that our precise prediction models could be probably used for reference in the probe set design when the DNA methylation beadchip update. PretiMeth is provided as an open source tool via https://github.com/JxTang-bioinformatics/PretiMeth.
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Affiliation(s)
- Jianxiong Tang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jianxiao Zou
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xiaoran Zhang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.,Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Mei Fan
- Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qi Tian
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shuyao Fu
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shihong Gao
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shicai Fan
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China. .,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
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56
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Feng C, Ma Z, Yang D, Li X, Zhang J, Li Y. A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features. Front Bioeng Biotechnol 2020; 8:285. [PMID: 32432088 PMCID: PMC7214540 DOI: 10.3389/fbioe.2020.00285] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/18/2020] [Indexed: 11/13/2022] Open
Abstract
The thermostability of proteins is a key factor considered during enzyme engineering, and finding a method that can identify thermophilic and non-thermophilic proteins will be helpful for enzyme design. In this study, we established a novel method combining mixed features and machine learning to achieve this recognition task. In this method, an amino acid reduction scheme was adopted to recode the amino acid sequence. Then, the physicochemical characteristics, auto-cross covariance (ACC), and reduced dipeptides were calculated and integrated to form a mixed feature set, which was processed using correlation analysis, feature selection, and principal component analysis (PCA) to remove redundant information. Finally, four machine learning methods and a dataset containing 500 random observations out of 915 thermophilic proteins and 500 random samples out of 793 non-thermophilic proteins were used to train and predict the data. The experimental results showed that 98.2% of thermophilic and non-thermophilic proteins were correctly identified using 10-fold cross-validation. Moreover, our analysis of the final reserved features and removed features yielded information about the crucial, unimportant and insensitive elements, it also provided essential information for enzyme design.
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Affiliation(s)
- Changli Feng
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Zhaogui Ma
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Deyun Yang
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Xin Li
- College of Information Science and Technology, Taishan University, Tai’an, China
| | - Jun Zhang
- Department of Rehabilitation, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Yanjuan Li
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China
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57
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Dao FY, Lv H, Yang YH, Zulfiqar H, Gao H, Lin H. Computational identification of N6-methyladenosine sites in multiple tissues of mammals. Comput Struct Biotechnol J 2020; 18:1084-1091. [PMID: 32435427 PMCID: PMC7229270 DOI: 10.1016/j.csbj.2020.04.015] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 12/12/2022] Open
Abstract
N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.
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Affiliation(s)
| | | | - Yu-He Yang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hasan Zulfiqar
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Gao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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58
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Meng C, Hu Y, Zhang Y, Guo F. PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides. Front Bioeng Biotechnol 2020; 8:245. [PMID: 32296690 PMCID: PMC7137786 DOI: 10.3389/fbioe.2020.00245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/09/2020] [Indexed: 12/11/2022] Open
Abstract
Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.
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Affiliation(s)
- Chaolu Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China.,College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Yang Hu
- School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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59
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Li HF, Wang XF, Tang H. Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features. Front Bioeng Biotechnol 2020; 8:183. [PMID: 32266225 PMCID: PMC7105632 DOI: 10.3389/fbioe.2020.00183] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 02/24/2020] [Indexed: 12/19/2022] Open
Abstract
Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.
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Affiliation(s)
- Hong-Fei Li
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou, China.,School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Xian-Fang Wang
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Hua Tang
- Department of Pathophysiology, Key Laboratory of Medical Electrophysiology, Ministry of Education, Southwest Medical University, Luzhou, China
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60
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Luo X, Wang F, Wang G, Zhao Y. Identification of methylation states of DNA regions for Illumina methylation BeadChip. BMC Genomics 2020; 21:672. [PMID: 32138668 PMCID: PMC7057447 DOI: 10.1186/s12864-019-6019-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 08/07/2019] [Indexed: 12/24/2022] Open
Abstract
Background Methylation of cytosine bases in DNA is a critical epigenetic mark in many eukaryotes and has also been implicated in the development and progression of normal and diseased cells. Therefore, profiling DNA methylation across the genome is vital to understanding the effects of epigenetic. In recent years the Illumina HumanMethylation450 (HM450K) and MethylationEPIC (EPIC) BeadChip have been widely used to profile DNA methylation in human samples. The methods to predict the methylation states of DNA regions based on microarray methylation datasets are critical to enable genome-wide analyses. Result We report a computational approach based on the two layers two-state hidden Markov model (HMM) to identify methylation states of single CpG site and DNA regions in HM450K and EPIC BeadChip. Using this mothed, all CpGs detected by HM450K and EPIC in H1-hESC and GM12878 cell lines are identified as un-methylated, middle-methylated and full-methylated states. A large number of DNA regions are segmented into three methylation states as well. Comparing the identified regions with the result from the whole genome bisulfite sequencing (WGBS) datasets segmented by MethySeekR, our method is verified. Genome-wide maps of chromatin states show that methylation state is inversely correlated with active histone marks. Genes regulated by un-methylated regions are expressed and regulated by full-methylated regions are repressed. Our method is illustrated to be useful and robust. Conclusion Our method is valuable for DNA methylation genome-wide analyses. It is focusing on identification of DNA methylation states on microarray methylation datasets. For the features of array datasets, using two layers two-state HMM to identify to methylation states on CpG sites and regions creatively, our method which takes into account the distribution of genome-wide methylation levels is more reasonable than segmentation with a fixed threshold. Electronic supplementary material The online version of this article (10.1186/s12864-019-6019-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ximei Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Fang Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Guohua Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Yuming Zhao
- Information and Computer Engineering College, Northeast Forestry University, Harbin, China.
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61
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Wang C, Zhao N, Yuan L, Liu X. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers. Cells 2020; 9:E326. [PMID: 32019269 PMCID: PMC7072524 DOI: 10.3390/cells9020326] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 01/28/2020] [Accepted: 01/28/2020] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.
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Affiliation(s)
- Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Ning Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China;
| | - Linlin Yuan
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150080, China
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62
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Malousi A, Kouidou S, Tsagiopoulou M, Papakonstantinou N, Bouras E, Georgiou E, Tzimagiorgis G, Stamatopoulos K. MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment. Sci Rep 2019; 9:19148. [PMID: 31844073 PMCID: PMC6915744 DOI: 10.1038/s41598-019-55453-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 11/19/2019] [Indexed: 12/16/2022] Open
Abstract
DNA methylation studies have been reformed with the advent of single-base resolution arrays and bisulfite sequencing methods, enabling deeper investigation of methylation-mediated mechanisms. In addition to these advancements, numerous bioinformatics tools address important computational challenges, covering DNA methylation calling up to multi-modal interpretative analyses. However, contrary to the analytical frameworks that detect driver mutational signatures, the identification of putatively actionable epigenetic events remains an unmet need. The present work describes a novel computational framework, called MeinteR, that prioritizes critical DNA methylation events based on the following hypothesis: critical aberrations of DNA methylation more likely occur on a genomic substrate that is enriched in cis-acting regulatory elements with distinct structural characteristics, rather than in genomic “deserts”. In this context, the framework incorporates functional cis-elements, e.g. transcription factor binding sites, tentative splice sites, as well as conformational features, such as G-quadruplexes and palindromes, to identify critical epigenetic aberrations with potential implications on transcriptional regulation. The evaluation on multiple, public cancer datasets revealed significant associations between the highest-ranking loci with gene expression and known driver genes, enabling for the first time the computational identification of high impact epigenetic changes based on high-throughput DNA methylation data.
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Affiliation(s)
- Andigoni Malousi
- Lab. of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Sofia Kouidou
- Lab. of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Maria Tsagiopoulou
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Nikos Papakonstantinou
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
| | - Emmanouil Bouras
- Lab. of Hygiene, Social-Preventive Medicine & Medical Statistics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Elisavet Georgiou
- Lab. of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Tzimagiorgis
- Lab. of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kostas Stamatopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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Ru X, Cao P, Li L, Zou Q. Selecting Essential MicroRNAs Using a Novel Voting Method. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:16-23. [PMID: 31479921 PMCID: PMC6727015 DOI: 10.1016/j.omtn.2019.07.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 06/20/2019] [Accepted: 07/08/2019] [Indexed: 02/06/2023]
Abstract
Among the large number of known microRNAs (miRNAs), some miRNAs play negligible roles in cell regulation. Therefore, selecting essential miRNAs is an important initial step for a deeper understanding of miRNAs and their functions. In this study, we generated 60 classification models by combining 12 representative feature extraction methods and 5 commonly used classification algorithms. The optimal model for essential miRNA classification that we obtained is based on the Mismatch feature extraction method combined with the random forest algorithm. The F-Measure, area under the curve, and accuracy values of this model were 93.2%, 96.7%, and 93.0%, respectively. We also found that the distribution of the positive and negative examples of the first few features greatly influenced the classification results. The feature extraction methods performed best when the differences between the positive and negative examples were obvious, and this led to better classification of essential miRNAs. Because each classifier's predictions for the same sample may be different, we employed a novel voting method to improve the accuracy of the classification of essential miRNAs. The performance results showed that the best classification results were obtained when five classification models were used in the voting. The five classification models were constructed based on the Mismatch, pseudo-distance structure status pair composition, Subsequence, Kmer, and Triplet feature extraction methods. The voting result was 95.3%. Our results suggest that the voting method can be an important tool for selecting essential miRNAs.
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Affiliation(s)
- Xiaoqing Ru
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China
| | - Peigang Cao
- Department of Cardiology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Lihong Li
- School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
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64
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Taxonomy dimension reduction for colorectal cancer prediction. Comput Biol Chem 2019; 83:107160. [DOI: 10.1016/j.compbiolchem.2019.107160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/02/2019] [Accepted: 11/04/2019] [Indexed: 02/01/2023]
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65
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Köhler F, Rodríguez-Paredes M. DNA Methylation in Epidermal Differentiation, Aging, and Cancer. J Invest Dermatol 2019; 140:38-47. [PMID: 31427190 DOI: 10.1016/j.jid.2019.05.011] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/24/2019] [Accepted: 05/17/2019] [Indexed: 12/22/2022]
Abstract
The formation and maintenance of the epidermis depend on epidermal stem cell differentiation and must be tightly regulated. Epigenetic mechanisms such as DNA methylation allow the precise gene expression cascade needed during cellular differentiation. However, these mechanisms become deregulated during aging and tumorigenesis, where cellular function and identity become compromised. Here we provide a review of this rapidly developing field. We discuss recent discoveries related to epidermal homeostasis, aging, and cancer, including the functional role of DNA methyltransferases, the methylation clock, and the determination of tumor cells-of-origin. Finally, we focus on future advances, greatly influenced by single-cell sequencing technologies.
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Affiliation(s)
- Florian Köhler
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Manuel Rodríguez-Paredes
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany.
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66
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Cejas RB, Ferguson DC, Quiñones-Lombraña A, Bard JE, Blanco JG. Contribution of DNA methylation to the expression of FCGRT in human liver and myocardium. Sci Rep 2019; 9:8674. [PMID: 31209240 PMCID: PMC6572836 DOI: 10.1038/s41598-019-45203-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 05/31/2019] [Indexed: 01/05/2023] Open
Abstract
FcRn mediates recycling and transcytosis of IgG and albumin in various cell types. The MHC-class-I-like protein of the FcRn heterodimer is encoded by FCGRT. Few determinants of variable FCGRT expression in humans have been identified so far. In this study, we investigated the presence of DNA methylation in regulatory regions of FCGRT in samples of human liver and myocardium tissue, and we examined the impact of FCGRT methylation on FcRn expression in model cell lines. Quantitative DNA methylation analysis of the FCGRT locus revealed differentially methylated regions in DNA from liver and myocardium. Methylation status in individual CpG sites correlated with FCGRT mRNA expression. Data from model cell lines suggest that differential methylation in the -1058 to -587 bp regulatory region of FCGRT contributes to FcRn expression. Chromatin immunoprecipitation assays indicate that CpG site methylation impacts the binding of the methylation sensitive transcription factors Zbtb7a and Sp1. This study provides a foundation to further define the contribution of epigenetic factors during the control of FcRn expression and IgG traffic in human tissues.
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Affiliation(s)
- R B Cejas
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, 14214, USA
| | - D C Ferguson
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, 14214, USA
| | - A Quiñones-Lombraña
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, 14214, USA
| | - J E Bard
- Genomics and Bioinformatics Core, New York State Center of Excellence in Bioinformatics and Life Sciences, The State University of New York at Buffalo, Buffalo, NY, 14203, USA
| | - J G Blanco
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, 14214, USA.
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67
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Sutton MN, Huang GY, Zhou J, Mao W, Langley R, Lu Z, Bast RC. Amino Acid Deprivation-Induced Autophagy Requires Upregulation of DIRAS3 through Reduction of E2F1 and E2F4 Transcriptional Repression. Cancers (Basel) 2019; 11:cancers11050603. [PMID: 31052266 PMCID: PMC6562629 DOI: 10.3390/cancers11050603] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 04/19/2019] [Accepted: 04/26/2019] [Indexed: 01/07/2023] Open
Abstract
Failure to cure ovarian cancer relates to the persistence of dormant, drug-resistant cancer cells following surgery and chemotherapy. “Second look” surgery can detect small, poorly vascularized nodules of persistent ovarian cancer in ~50% of patients, where >80% are undergoing autophagy and express DIRAS3. Autophagy is one mechanism by which dormant cancer cells survive in nutrient poor environments. DIRAS3 is a tumor suppressor gene downregulated in >60% of primary ovarian cancers by genetic, epigenetic, transcriptional and post-transcriptional mechanisms, that upon re-expression can induce autophagy and dormancy in a xenograft model of ovarian cancer. We examined the expression of DIRAS3 and autophagy in ovarian cancer cells following nutrient deprivation and the mechanism by which they are upregulated. We have found that DIRAS3 mediates autophagy induced by amino acid starvation, where nutrient sensing by mTOR plays a central role. Withdrawal of amino acids downregulates mTOR, decreases binding of E2F1/4 to the DIRAS3 promoter, upregulates DIRAS3 and induces autophagy. By contrast, acute amino acid deprivation did not affect epigenetic regulation of DIRAS3 or expression of miRNAs that regulate DIRAS3. Under nutrient poor conditions DIRAS3 can be transcriptionally upregulated, inducing autophagy that could sustain dormant ovarian cancer cells.
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Affiliation(s)
- Margie N Sutton
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
| | - Gilbert Y Huang
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
| | - Jinhua Zhou
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China.
| | - Weiqun Mao
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
| | - Robert Langley
- Office of Translational Research, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
| | - Zhen Lu
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
| | - Robert C Bast
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
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68
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Banerjee S, Wei X, Xie H. Recursive Motif Analyses Identify Brain Epigenetic Transcription Regulatory Modules. Comput Struct Biotechnol J 2019; 17:507-515. [PMID: 31011409 PMCID: PMC6462766 DOI: 10.1016/j.csbj.2019.04.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/12/2019] [Accepted: 04/03/2019] [Indexed: 01/26/2023] Open
Abstract
DNA methylation is an epigenetic modification modulating the structure of DNA molecule and the interactions with its binding proteins. Accumulating large-scale methylation data motivates the development of analytic tools to facilitate methylome data mining. One critical phenomenon associated with dynamic DNA methylation is the altered DNA binding affinity of transcription factors, which plays key roles in gene expression regulation. In this study, we conceived an algorithm to predict epigenetic regulatory modules through recursive motif analyses on differentially methylated loci. A two-step procedure was implemented to first group differentially methylated loci into clusters according to their correlations in methylation profiles and then to repeatedly identify the transcription factor binding motifs significantly enriched in each cluster. We applied this tool on methylome datasets generated for mouse brains which have a lack of DNA demethylation enzymes TET1 or TET2. Compared with wild type control, the differentially methylated CpG sites identified in TET1 knockout mouse brains differed significantly from those determined for TET2 knockout. Transcription factors with zinc finger DNA binding domains including Egr1, Zic3, and Zeb1 were predicted to be associated with TET1 mediated brain methylome programming, while Lhx family members with Homeobox domains were predicted to be associated with TET2 function. Interestingly, genomic loci from a co-methylated cluster often host motifs for transcription factors sharing the same DNA binding domains. Altogether, our study provided a systematic approach for epigenetic regulatory module identification and will help throw light on the interplay of DNA methylation and transcription factors.
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Affiliation(s)
- Sharmi Banerjee
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA.,Biocomplexity Institute of Virginia Tech, Blacksburg, VA 24061, USA
| | - Xiaoran Wei
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA 24061, USA.,Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA 24061, USA
| | - Hehuang Xie
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA 24061, USA.,Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Blacksburg, VA 24061, USA.,School of Neuroscience, Blacksburg, VA 24061, USA
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69
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Aavik E, Babu M, Ylä-Herttuala S. DNA methylation processes in atherosclerotic plaque. Atherosclerosis 2019; 281:168-179. [DOI: 10.1016/j.atherosclerosis.2018.12.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 11/09/2018] [Accepted: 12/14/2018] [Indexed: 12/18/2022]
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