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Hong Y, Liu L, Feng Y, Zhang Z, Hou R, Xu Q, Shi J. mHapBrowser: a comprehensive database for visualization and analysis of DNA methylation haplotypes. Nucleic Acids Res 2024; 52:D929-D937. [PMID: 37831137 PMCID: PMC10767976 DOI: 10.1093/nar/gkad881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/13/2023] [Accepted: 09/29/2023] [Indexed: 10/14/2023] Open
Abstract
DNA methylation acts as a vital epigenetic regulatory mechanism involved in controlling gene expression. Advances in sequencing technologies have enabled characterization of methylation patterns at single-base resolution using bisulfite sequencing approaches. However, existing methylation databases have primarily focused on mean methylation levels, overlooking phased methylation patterns. The methylation status of CpGs on individual sequencing reads represents discrete DNA methylation haplotypes (mHaps). Here, we present mHapBrowser, a comprehensive database for visualizing and analyzing mHaps. We systematically processed data of diverse tissues in human, mouse and rat from public repositories, generating mHap format files for 6366 samples. mHapBrowser enables users to visualize eight mHap metrics across the genome through an integrated WashU Epigenome Browser. It also provides an online server for comparing mHap patterns across samples. Additionally, mHap files for all samples can be downloaded to facilitate local processing using downstream analysis toolkits. The utilities of mHapBrowser were demonstrated through three case studies: (i) mHap patterns are associated with gene expression; (ii) changes in mHap patterns independent of mean methylation correlate with differential expression between lung cancer subtypes; and (iii) the mHap metric MHL outperforms mean methylation for classifying tumor and normal samples from cell-free DNA. The database is freely accessible at http://mhap.sibcb.ac.cn/.
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Affiliation(s)
- Yuyang Hong
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Leiqin Liu
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Feng
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Rui Hou
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qiong Xu
- Department of Respiratory Disease, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Jiantao Shi
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
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2
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Yu Y, Wang S, Luo Y, Gu C, Shi X, Shen F. Quantitative Investigation of Methylation Heterogeneity by Digital Melting Curve Analysis on a SlipChip for Atrial Fibrillation. ACS Sens 2023; 8:3595-3603. [PMID: 37590470 DOI: 10.1021/acssensors.3c01309] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Methylation is an essential epigenetic modification involved in regulating gene expression and maintaining genome stability. Methylation patterns can be heterogeneous, exhibiting variations in both level and density. However, current methods of methylation analysis, including sequencing, methylation-specific PCR, and high-resolution melting curve analysis (HRM), face limitations of high cost, time-consuming workflows, and the difficulty of both accurate heterogeneity analysis and precise quantification. Here, a droplet array SlipChip-based (da-SlipChip-based) digital melting curve analysis (MCA) method was developed for the accurate quantification of both methylation level (ratio of methylated molecules to total molecules) and methylation density (ratio of methylated CpG sites to total CpG sites). The SlipChip-based digital MCA system supplements an in situ thermal cycler with a fluorescence imaging module for real-time MCA. The da-SlipChip can generate 10,656 droplets of 1 nL each, which can be separated into four lanes, enabling the simultaneous analysis of four samples. This method's clinical application was demonstrated by analyzing samples from ten healthy individuals and twenty patients with atrial fibrillation (AF), the most common arrhythmia. This method can distinguish healthy individuals from those with AF of both the paroxysmal and persistent types. It also holds potential for broader application in various research and clinical settings requiring methylation analysis.
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Affiliation(s)
- Yan Yu
- School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai 200030, China
| | - Sheng Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai 200030, China
| | - Yang Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai 200030, China
| | - Chang Gu
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- Department of Cardiothoracic Surgery, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China
| | - Xin Shi
- Department of Cardiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Feng Shen
- School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai 200030, China
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3
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Zhang S, He S, Zhu X, Wang Y, Xie Q, Song X, Xu C, Wang W, Xing L, Xia C, Wang Q, Li W, Zhang X, Yu J, Ma S, Shi J, Gu H. DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues. Nat Commun 2023; 14:5686. [PMID: 37709764 PMCID: PMC10502058 DOI: 10.1038/s41467-023-41015-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/18/2023] [Indexed: 09/16/2023] Open
Abstract
Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).
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Affiliation(s)
- Shirong Zhang
- Translational Medicine Research Center, Hangzhou First People's Hospital, 310006, Hangzhou, Zhejiang Province, China
- Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou First People's Hospital, 310006, Hangzhou, Zhejiang Province, China
| | - Shutao He
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, 200031, Shanghai, China
- Institute of Biotechnology and Health, Beijing Academy of Science and Technology, 100089, Beijing, China
| | - Xin Zhu
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Cancer Hospital, 310022, Hangzhou, Zhejiang Province, China
| | - Yunfei Wang
- Zhejiang ShengTing Biotech Co. Ltd, 310018, Hangzhou, Zhejiang Province, China
| | - Qionghuan Xie
- Zhejiang ShengTing Biotech Co. Ltd, 310018, Hangzhou, Zhejiang Province, China
| | - Xianrang Song
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Chunwei Xu
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, 210002, Nanjing, Jiangshu Province, China
| | - Wenxian Wang
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Cancer Hospital, 310022, Hangzhou, Zhejiang Province, China
| | - Ligang Xing
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Chengqing Xia
- Zhejiang ShengTing Biotech Co. Ltd, 310018, Hangzhou, Zhejiang Province, China
| | - Qian Wang
- Department of Respiratory Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, 210029, Nanjing, Jiangshu Province, China
| | - Wenfeng Li
- Department of Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, 325000, Wenzhou, Zhejiang Province, China
| | - Xiaochen Zhang
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 310006, Hangzhou, Zhejiang Province, China
| | - Jinming Yu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117, Jinan, Shandong Province, China
| | - Shenglin Ma
- Translational Medicine Research Center, Hangzhou First People's Hospital, 310006, Hangzhou, Zhejiang Province, China.
- Department of Oncology, Hangzhou Cancer Hospital, 310006, Hangzhou, Zhejiang Province, China.
| | - Jiantao Shi
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, 200031, Shanghai, China.
| | - Hongcang Gu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 230031, Hefei, Anhui Province, China.
- Hefei Cancer Hospital, Chinese Academy of Sciences, 230031, Hefei, Anhui Province, China.
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4
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Xu J, Hu Z, Cao H, Zhang H, Luo P, Zhang J, Wang X, Cheng Q, Li J. Multi-omics pan-cancer study of cuproptosis core gene FDX1 and its role in kidney renal clear cell carcinoma. Front Immunol 2022; 13:981764. [PMID: 36605188 PMCID: PMC9810262 DOI: 10.3389/fimmu.2022.981764] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/28/2022] [Indexed: 01/07/2023] Open
Abstract
Background The mechanism of copper-induced cellular death was newly discovered and termed cuproptosis. Inducing cuproptosis in cancer cells is well anticipated for its curative potential in treating tumor diseases. However, ferredoxin 1 (FDX1), the core regulatory gene in cuproptosis, is rarely studied, and the regulation of FDX1 in tumor biology remains obscure. A comprehensive pan-cancer analysis of FDX1 is needed. Methods Thirty-three types of tumors were included with paired normal tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets. The interaction between transcription, protein, phosphorylation, and promoter methylation levels was analyzed. Survival, immune infiltration, single-cell FDX1 expression, FDX1-related tumor mutational burden (TMB), microsatellite instability (MSI), stemness, tumor immune dysfunction and exclusion (TIDE), and immunotherapy-related analyses were performed. FDX1 protein expression was assessed by kidney renal clear cell carcinoma (KIRC) tissue microarray immunohistochemistry. The function of FDX1 in KIRC was further explored by experiments in 786-O cell lines in vitro. Results FDX1 is highly expressed in 15 tumor types and lowly expressed in 11 tumor types. The corresponding changes in protein expression, phosphorylation, and promoter methylation level of FDX1 have been described in several tumors. Survival analysis showed that FDX1 was related to favorable or poor overall survival in eight tumors and progression-free survival in nine tumors. Immune infiltration and single-cell analysis indicated the indispensable role of FDX1 expression in macrophages and monocytes. Multiple established immunotherapy cohorts suggested that FDX1 may be a potential predictor of treatment effects for tumor patients. Tissue microarray analysis showed decreased FDX1 expression in KIRC patients' tumor tissues. Knockdown of FDX1 resulted in the downregulation of cuproptosis in kidney renal clear tumor cells. Mechanistically, the FDX1-associated gene expression signature in KIRC is related to the enrichment of genes involved in the tricarboxylic acid (TCA) cycle, NOTCH pathway, etc. Several NOTCH pathway genes were differentially expressed in the high- and low-FDX1 groups in KIRC. Conclusion Our analysis showed that the central regulatory gene of cuproptosis, FDX1, has differential expression and modification levels in various tumors, which is associated with cellular function, immune modulation, and disease prognosis. Thus, FDX1-dependent cuproptosis may serve as a brand-new target in future therapeutic approaches against tumors.
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Affiliation(s)
- Jiahao Xu
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Zhengang Hu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Cao
- Brain Hospital of Hunan Province, The Second People's Hospital of Hunan Province, Changsha, China
| | - Hao Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaoyan Wang
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, China,*Correspondence: Quan Cheng, ; Jingbo Li,
| | - Jingbo Li
- Department of Gastroenterology, The Third Xiangya Hospital, Central South University, Changsha, China,*Correspondence: Quan Cheng, ; Jingbo Li,
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5
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De Riso G, Sarnataro A, Scala G, Cuomo M, Della Monica R, Amente S, Chiariotti L, Miele G, Cocozza S. MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data. NAR Genom Bioinform 2022; 4:lqac096. [PMID: 36601577 PMCID: PMC9803872 DOI: 10.1093/nargab/lqac096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/24/2022] [Accepted: 12/08/2022] [Indexed: 01/01/2023] Open
Abstract
DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.
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Affiliation(s)
- Giulia De Riso
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, via Sergio Pansini 5, 80131 Naples, Italy
| | - Antonella Sarnataro
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, via Sergio Pansini 5, 80131 Naples, Italy
| | - Giovanni Scala
- Department of Biology, University of Naples Federico II, Via Vicinale Cupa Cintia 21, 80126 Naples, Italy
| | - Mariella Cuomo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, via Sergio Pansini 5, 80131 Naples, Italy
- CEINGE - Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145 Naples, Italy
| | - Rosa Della Monica
- CEINGE - Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145 Naples, Italy
| | - Stefano Amente
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, via Sergio Pansini 5, 80131 Naples, Italy
| | - Lorenzo Chiariotti
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, via Sergio Pansini 5, 80131 Naples, Italy
- CEINGE - Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145 Naples, Italy
| | - Gennaro Miele
- Department of Physics “E. Pancini”, University of Naples “Federico II”, Via Cinthia, 80126 Naples, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Napoli, 80126 Naples, Italy
| | - Sergio Cocozza
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, via Sergio Pansini 5, 80131 Naples, Italy
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6
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Ding Y, Cai K, Liu L, Zhang Z, Zheng X, Shi J. mHapTk: a comprehensive toolkit for the analysis of DNA methylation haplotypes. Bioinformatics 2022; 38:5141-5143. [PMID: 36179079 DOI: 10.1093/bioinformatics/btac650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/22/2022] [Accepted: 09/29/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY Bisulfite sequencing remains the gold standard technique to detect DNA methylation profiles at single-nucleotide resolution. The DNA methylation status of CpG sites on the same fragment represents a discrete methylation haplotype (mHap). The mHap-level metrics were demonstrated to be promising cancer biomarkers and explain more gene expression variation than average methylation. However, most existing tools focus on average methylation and neglect mHap patterns. Here, we present mHapTk, a comprehensive python toolkit for the analysis of DNA mHap. It calculates eight mHap-level summary statistics in predefined regions or across individual CpG in a genome-wide manner. It identifies methylation haplotype blocks, in which methylations of pairwise CpGs are tightly correlated. Furthermore, mHap patterns can be visualized with the built-in functions in mHapTk or external tools such as IGV and deepTools. AVAILABILITY AND IMPLEMENTATION https://jiantaoshi.github.io/mhaptk/index.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi Ding
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kangwen Cai
- Department of Mathematics, Shanghai Normal University, Shanghai 200234, China
| | - Leiqin Liu
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhiqiang Zhang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoqi Zheng
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiantao Shi
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
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7
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Zhang S, Ou K, Huang J, Fang L, Wang C. In utero exposure to mixed PAHs causes heart mass reduction in adult male mice. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 225:112804. [PMID: 34555720 DOI: 10.1016/j.ecoenv.2021.112804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 06/13/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a risk factor for the occurrence of cardiac diseases. The present study was conducted to investigate the influence of prenatal exposure to a mixed PAHs on heart and the underlying mechanism. Pregnant mice were orally administered with a mixture of 8 kinds of PAHs (0, 5, 50, 500 μg/kg body weight) once every 2 days for a total of 8 dosages. The mixed PAHs contained naphthalene, acenaphthylene, phenanthrene, fluoranthene, pyrene, benzo[a]pyrene, dibenzo[a,h]anthracene and benzo[g,h,i]perylene at a weight ratio of 10: 10: 10: 10: 10: 1: 1: 1. The adult males, not females, showed significantly decreased heart/body weight ratio, which was attributed to the loss of cardiac fiber and the increase of cell apoptosis. The protein expression of transforming growth factor β1 and its downstream transcription factors, Smad3 and Smad4, was significantly downregulated, which caused the loss of cardiac fiber. The downregulated phosphatidylinositol 3-kinase and AKT led to increased expression of caspase3, caspase9, BAX and reduced expression of Bcl-2, which was responsible for the increased cell apoptosis. Different levels of aromatic hydrocarbon receptor and sex hormone receptors between males and females were associated with the distinct effect on heart.
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Affiliation(s)
- Shenli Zhang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, PR China
| | - Kunlin Ou
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, PR China
| | - Jie Huang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, PR China
| | - Lu Fang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, PR China
| | - Chonggang Wang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, PR China.
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8
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Shi J, Xu J, Chen YE, Li JS, Cui Y, Shen L, Li JJ, Li W. The concurrence of DNA methylation and demethylation is associated with transcription regulation. Nat Commun 2021; 12:5285. [PMID: 34489442 PMCID: PMC8421433 DOI: 10.1038/s41467-021-25521-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 08/12/2021] [Indexed: 12/22/2022] Open
Abstract
The mammalian DNA methylome is formed by two antagonizing processes, methylation by DNA methyltransferases (DNMT) and demethylation by ten-eleven translocation (TET) dioxygenases. Although the dynamics of either methylation or demethylation have been intensively studied in the past decade, the direct effects of their interaction on gene expression remain elusive. Here, we quantify the concurrence of DNA methylation and demethylation by the percentage of unmethylated CpGs within a partially methylated read from bisulfite sequencing. After verifying ‘methylation concurrence’ by its strong association with the co-localization of DNMT and TET enzymes, we observe that methylation concurrence is strongly correlated with gene expression. Notably, elevated methylation concurrence in tumors is associated with the repression of 40~60% of tumor suppressor genes, which cannot be explained by promoter hypermethylation alone. Furthermore, methylation concurrence can be used to stratify large undermethylated regions with negligible differences in average methylation into two subgroups with distinct chromatin accessibility and gene regulation patterns. Together, methylation concurrence represents a unique methylation metric important for transcription regulation and is distinct from conventional metrics, such as average methylation and methylation variation. The global pattern of the mammalian methylome is formed by changes in methylation and demethylation. Here the authors describe a metric methylation concurrence that measures the ratio of unmethylated CpGs inside the partially methylated reads and show that methylation concurrence is associated with epigenetically regulated tumour suppressor genes.
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Affiliation(s)
- Jiejun Shi
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Jianfeng Xu
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Yiling Elaine Chen
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Jason Sheng Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Ya Cui
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Lanlan Shen
- Department of Pediatrics, Baylor College of Medicine, USDA/ARS Children's Nutrition Research Center, Houston, TX, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA.
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9
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Zhang Z, Dan Y, Xu Y, Zhang J, Zheng X, Shi J. The DNA methylation haplotype (mHap) format and mHapTools. Bioinformatics 2021; 37:4892-4894. [PMID: 34179956 DOI: 10.1093/bioinformatics/btab458] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 05/23/2021] [Accepted: 06/16/2021] [Indexed: 12/30/2022] Open
Abstract
SUMMARY Bisulfite sequencing (BS-seq) is currently the gold standard for measuring genome-wide DNA methylation profiles at single-nucleotide resolution. Most analyses focus on mean CpG methylation and ignore methylation states on the same DNA fragments [DNA methylation haplotypes (mHaps)]. Here, we propose mHap, a simple DNA mHap format for storing DNA BS-seq data. This format reduces the size of a BAM file by 40- to 140-fold while retaining complete read-level CpG methylation information. It is also compatible with the Tabix tool for fast and random access. We implemented a command-line tool, mHapTools, for converting BAM/SAM files from existing platforms to mHap files as well as post-processing DNA methylation data in mHap format. With this tool, we processed all publicly available human reduced representation bisulfite sequencing data and provided these data as a comprehensive mHap database. AVAILABILITY AND IMPLEMENTATION https://jiantaoshi.github.io/mHap/index.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhiqiang Zhang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuhao Dan
- Department of Mathematics, Shanghai Normal University, Shanghai 200234, China
| | - Yaochen Xu
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiarui Zhang
- Shanghai Science and Technology Development Co., Ltd, Shanghai 200235, China
| | - Xiaoqi Zheng
- Department of Mathematics, Shanghai Normal University, Shanghai 200234, China
| | - Jiantao Shi
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science Chinese Academy of Sciences, Shanghai 200031, China
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10
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Zhu X, Li F, Wang M, Su H, Wu X, Qiu H, Zhou W, Shan C, Wang C, Wei L. Integrated Analysis of Omics Data Reveal AP-1 as a Potential Regulation Hub in the Inflammation-Induced Hyperalgesia Rat Model. Front Immunol 2021; 12:672498. [PMID: 34122430 PMCID: PMC8194263 DOI: 10.3389/fimmu.2021.672498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 05/13/2021] [Indexed: 11/13/2022] Open
Abstract
Inflammation-associated chronic pain is a global clinical problem, affecting millions of people worldwide. However, the underlying mechanisms that mediate inflammation-associated chronic pain remain unclear. A rat model of cutaneous inflammation induced by Complete Freund's Adjuvant (CFA) has been widely used as an inflammation-induced pain hypersensitivity model. We present the transcriptomics profile of CFA-induced inflammation in the rat dorsal root ganglion (DRG) via an approach that targets gene expression, DNA methylation, and post-transcriptional regulation. We identified 418 differentially expressed mRNAs, 120 differentially expressed microRNAs (miRNAs), and 2,670 differentially methylated regions (DMRs), which were all highly associated with multiple inflammation-related pathways, including nuclear factor kappa B (NF-κB) and interferon (IFN) signaling pathways. An integrated analysis further demonstrated that the activator protein 1 (AP-1) network, which may act as a regulator of the inflammatory response, is regulated at both the transcriptomic and epigenetic levels. We believe our data will not only provide drug screening targets for the treatment of chronic pain and inflammation but will also shed light on the molecular network associated with inflammation-induced hyperalgesia.
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Affiliation(s)
- Xiang Zhu
- Department of Anesthesiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Feng Li
- The Yancheng Clinical College of XuZhou Medical University, The First People's Hospital of Yancheng, Yancheng, China
| | - Miqun Wang
- Department of Anesthesiology, Qingdao Women and Children's Hospital, Qingdao, China
| | - Huibin Su
- Department of Anesthesiology, Suzhou Municipal Hospital Affiliated to Nanjing Medical University, Suzhou, China
| | - Xuedong Wu
- Department of Anesthesiology, Suzhou Municipal Hospital Affiliated to Nanjing Medical University, Suzhou, China
| | - Haiyan Qiu
- Department of Anesthesiology, The First People's Hospital of Yancheng, Yancheng, China
| | - Wang Zhou
- Department of Anesthesiology, Affiliated Hospital of Nantong University, Nantong, China.,Graduate School of Nantong University, Nantong, China
| | - Chunli Shan
- Department of Anesthesiology, Affiliated Hospital of Nantong University, Nantong, China.,Graduate School of Nantong University, Nantong, China
| | - Cancan Wang
- Graduate School of Nantong University, Nantong, China
| | - Lei Wei
- Department of Anesthesiology, Suzhou Municipal Hospital Affiliated to Nanjing Medical University, Suzhou, China
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Tavolara TE, Niazi MKK, Gower AC, Ginese M, Beamer G, Gurcan MN. Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice. EBioMedicine 2021; 67:103388. [PMID: 34000621 PMCID: PMC8138606 DOI: 10.1016/j.ebiom.2021.103388] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology which predicts selected gene expression values (microarray) from haematoxylin and eosin whole-slide images as an intermediate data modality to identify fulminant-like pulmonary tuberculosis ('supersusceptible') in an experimentally infected cohort of Diversity Outbred mice (n=77). METHODS Gradient-boosted trees were utilized as a novel feature selector to identify gene transcripts predictive of fulminant-like pulmonary tuberculosis. A novel attention-based multiple instance learning model for regression was used to predict selected genes' expression from whole-slide images. Gene expression predictions were shown to be sufficiently replicated to identify supersusceptible mice using gradient-boosted trees trained on ground truth gene expression data. FINDINGS The model was accurate, showing high positive correlations with ground truth gene expression on both cross-validation (n = 77, 0.63 ≤ ρ ≤ 0.84) and external testing sets (n = 33, 0.65 ≤ ρ ≤ 0.84). The sensitivity and specificity for gene expression predictions to identify supersusceptible mice (n=77) were 0.88 and 0.95, respectively, and for an external set of mice (n=33) 0.88 and 0.93, respectively. IMPLICATIONS Our methodology maps histopathology to gene expression with sufficient accuracy to predict a clinical outcome. The proposed methodology exemplifies a computational template for gene expression panels, in which relatively inexpensive and widely available tissue histopathology may be mapped to specific genes' expression to serve as a diagnostic or prognostic tool. FUNDING National Institutes of Health and American Lung Association.
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Affiliation(s)
- Thomas E Tavolara
- Center for Biomedical Informatics, Wake Forest School of Medicine, 486 Patterson Avenue, Winston-Salem, NC 27101, United States
| | - M K K Niazi
- Center for Biomedical Informatics, Wake Forest School of Medicine, 486 Patterson Avenue, Winston-Salem, NC 27101, United States.
| | - Adam C Gower
- Department of Medicine, Boston University School of Medicine, 72 E. Concord St Evans Building, Boston, MA 02118, United States
| | - Melanie Ginese
- Department of Infectious Disease and Global Health, Tufts University Cummings School of Veterinary Medicine, 200 Westboro Rd., North Grafton, MA 01536, United States
| | - Gillian Beamer
- Department of Infectious Disease and Global Health, Tufts University Cummings School of Veterinary Medicine, 200 Westboro Rd., North Grafton, MA 01536, United States
| | - Metin N Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, 486 Patterson Avenue, Winston-Salem, NC 27101, United States
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