1
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Marinello A, Tagliamento M, Pagliaro A, Conci N, Cella E, Vasseur D, Remon J, Levy A, Dall'Olio FG, Besse B. Circulating tumor DNA to guide diagnosis and treatment of localized and locally advanced non-small cell lung cancer. Cancer Treat Rev 2024; 129:102791. [PMID: 38963991 DOI: 10.1016/j.ctrv.2024.102791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/15/2024] [Accepted: 06/22/2024] [Indexed: 07/06/2024]
Abstract
Liquid biopsy is a minimally invasive method for biomarkers detection in body fluids, particularly in blood, which offers an elevated and growing number of clinical applications in oncology. As a result of the improvement in the techniques for DNA analysis, above all next-generation sequencing (NGS) assays, circulating tumor DNA (ctDNA) has become the most informing tumor-derived material for most types of cancer, including non-small cell lung cancer (NSCLC). Although ctDNA concentration is higher in patients with advanced tumors, it can be detected even in patients with early-stage disease. Therefore, numerous clinical applications of ctDNA in the management of early-stage lung cancer are emerging, such as lung cancer screening, the identification of minimal residual disease (MRD), and the prediction of relapse before radiologic progression. Moreover, a high number of clinical trials are ongoing to better define the impact of ctDNA evaluation in this setting. Aim of this review is to offer a comprehensive overview of the most relevant implementations in using ctDNA for the management of early-stage lung cancer, addressing available data, technical aspects, limitations, and future perspectives.
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Affiliation(s)
- Arianna Marinello
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; INSERM Unit 1030 - Molecular Radiotherapy and Therapeutic Innovation, Gustave Roussy, Villejuif, France
| | - Marco Tagliamento
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; Department of Internal Medicine and Medical Specialties, University of Genova, Genova, Italy.
| | - Arianna Pagliaro
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; Department of Medical Oncology, IRCCS Istituto Clinico Humanitas, Rozzano, Italy
| | - Nicole Conci
- Department of Medical Oncology, IRCCS Sant'Orsola-Malpighi, Bologna, Italy
| | - Eugenia Cella
- Department of Internal Medicine and Medical Specialties, University of Genova, Genova, Italy
| | - Damien Vasseur
- Department of Medical Biology and Pathology, Gustave Roussy, Villejuif, France
| | - Jordi Remon
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France
| | - Antonin Levy
- Department of Radiotherapy, Gustave Roussy, Villejuif, France
| | | | - Benjamin Besse
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France
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2
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O'Keefe CM, Zhao Y, Cope LM, Ho C, Fader AN, Stone R, Ferris JS, Beavis A, Levinson K, Wethington S, Wang T, Pisanic TR, Shih I, Wang T. Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer. Clin Transl Med 2024; 14:e1778. [PMID: 39083293 PMCID: PMC11290349 DOI: 10.1002/ctm2.1778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 05/22/2024] [Accepted: 07/09/2024] [Indexed: 08/02/2024] Open
Abstract
Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour-specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low-cost methods remains challenging, especially in early-stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian-cancer-specific aberrations of DNA methylation from routinely-collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high-resolution melt to analyze locus-specific DNA methylation patterns on a molecule-by-molecule basis at or near single CpG-site resolution at a fraction (< 1/10th) of the cost of next-generation sequencing techniques. We demonstrate the feasibility of the platform by assessing intermolecular heterogeneity of DNA methylation in a panel of methylation biomarker loci using DNA derived from Pap specimens obtained from a cohort of 43 women, including 18 cases with ovarian cancer and 25 cancer-free controls. PapDREAM leverages systematic multidimensional bioinformatic analyses of locus-specific methylation heterogeneity to improve upon Pap-specimen-based detection of ovarian cancer, demonstrating a clinical sensitivity of 50% at 99% specificity in detecting ovarian cancer cases with an area under the receiver operator curve of 0.90. We then establish a logistic regression model that could be used to identify high-risk patients for subsequent clinical follow-up and monitoring. The results of this study support the utility of PapDREAM as a simple, low-cost screening method with the potential to integrate with existing clinical workflows for early detection of ovarian cancer. KEY POINTS: We present a microfluidic platform for detection and analysis of rare, heterogeneously methylated DNA within Pap specimens towards detection of ovarian cancer. The platform achieves high sensitivity (fractions <0.00005%) at a suitably low cost (∼$25) for routine screening applications. Furthermore, it provides molecule-by-molecule quantitative analysis to facilitate further study on the effect of heterogeneous methylation on cancer development.
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Affiliation(s)
- Christine M. O'Keefe
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Yang Zhao
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Leslie M. Cope
- Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Departments of Oncology and BiostatisticsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Chih‐Ming Ho
- Gynecologic Cancer CenterDepartment of Obstetrics and GynecologyCathay General HospitalTaipeiTaiwan
- School of MedicineFu Jen Catholic UniversityNew TaipeiTaiwan
| | - Amanda N. Fader
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Rebecca Stone
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - James S. Ferris
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Anna Beavis
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Kimberly Levinson
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Greater Baltimore Medical CenterTowsonMarylandUSA
| | - Stephanie Wethington
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Tian‐Li Wang
- Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of PathologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Thomas R. Pisanic
- Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Institute for NanoBioTechnologyJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Ie‐Ming Shih
- Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of Gynecology and ObstetricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of PathologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Tza‐Huei Wang
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
- Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Institute for NanoBioTechnologyJohns Hopkins UniversityBaltimoreMarylandUSA
- Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
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3
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Unterman I, Avrahami D, Katsman E, Triche TJ, Glaser B, Berman BP. CelFiE-ISH: a probabilistic model for multi-cell type deconvolution from single-molecule DNA methylation haplotypes. Genome Biol 2024; 25:151. [PMID: 38858759 PMCID: PMC11163775 DOI: 10.1186/s13059-024-03275-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/13/2024] [Indexed: 06/12/2024] Open
Abstract
Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and of tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.
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Affiliation(s)
- Irene Unterman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dana Avrahami
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Efrat Katsman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Timothy J Triche
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, USA
| | - Benjamin Glaser
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Benjamin P Berman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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4
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Karetnikov DI, Romanov SE, Baklaushev VP, Laktionov PP. Age Prediction Using DNA Methylation Heterogeneity Metrics. Int J Mol Sci 2024; 25:4967. [PMID: 38732187 PMCID: PMC11084170 DOI: 10.3390/ijms25094967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 04/27/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.
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Affiliation(s)
- Dmitry I. Karetnikov
- Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Stanislav E. Romanov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Vladimir P. Baklaushev
- Federal Center for Brain and Neurotechnologies, Federal Medical and Biological Agency of Russia, 117513 Moscow, Russia
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
- Department of Medical Nanobiotechnology, Medical and Biological Faculty, Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, 117997 Moscow, Russia
| | - Petr P. Laktionov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
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5
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Teschendorff AE. On epigenetic stochasticity, entropy and cancer risk. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230054. [PMID: 38432318 PMCID: PMC10909509 DOI: 10.1098/rstb.2023.0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 09/26/2023] [Indexed: 03/05/2024] Open
Abstract
Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Andrew E. Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, People's Republic of China
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6
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Abstract
Lymphoid neoplasms represent a heterogeneous group of disease entities and subtypes with markedly different molecular and clinical features. Beyond genetic alterations, lymphoid tumors also show widespread epigenomic changes. These severely affect the levels and distribution of DNA methylation, histone modifications, chromatin accessibility, and three-dimensional genome interactions. DNA methylation stands out as a tracer of cell identity and memory, as B cell neoplasms show epigenetic imprints of their cellular origin and proliferative history, which can be quantified by an epigenetic mitotic clock. Chromatin-associated marks are informative to uncover altered regulatory regions and transcription factor networks contributing to the development of distinct lymphoid tumors. Tumor-intrinsic epigenetic and genetic aberrations cooperate and interact with microenvironmental cells to shape the transcriptome at different phases of lymphoma evolution, and intraclonal heterogeneity can now be characterized by single-cell profiling. Finally, epigenetics offers multiple clinical applications, including powerful diagnostic and prognostic biomarkers as well as therapeutic targets.
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Affiliation(s)
- Martí Duran-Ferrer
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain;
| | - José Ignacio Martín-Subero
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain;
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Departamento de Fundamentos Clínicos, Universitat de Barcelona, Barcelona, Spain
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7
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Ulrich A, Wu Y, Draisma H, Wharton J, Swietlik EM, Cebola I, Vasilaki E, Balkhiyarova Z, Jarvelin MR, Auvinen J, Herzig KH, Coghlan JG, Lordan J, Church C, Howard LS, Pepke-Zaba J, Toshner M, Wort SJ, Kiely DG, Condliffe R, Lawrie A, Gräf S, Morrell NW, Wilkins MR, Prokopenko I, Rhodes CJ. Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension. Nat Commun 2024; 15:330. [PMID: 38184627 PMCID: PMC10771427 DOI: 10.1038/s41467-023-44683-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 12/28/2023] [Indexed: 01/08/2024] Open
Abstract
Pulmonary arterial hypertension (PAH) is characterised by pulmonary vascular remodelling causing premature death from right heart failure. Established DNA variants influence PAH risk, but susceptibility from epigenetic changes is unknown. We addressed this through epigenome-wide association study (EWAS), testing 865,848 CpG sites for association with PAH in 429 individuals with PAH and 1226 controls. Three loci, at Cathepsin Z (CTSZ, cg04917472), Conserved oligomeric Golgi complex 6 (COG6, cg27396197), and Zinc Finger Protein 678 (ZNF678, cg03144189), reached epigenome-wide significance (p < 10-7) and are hypermethylated in PAH, including in individuals with PAH at 1-year follow-up. Of 16 established PAH genes, only cg10976975 in BMP10 shows hypermethylation in PAH. Hypermethylation at CTSZ is associated with decreased blood cathepsin Z mRNA levels. Knockdown of CTSZ expression in human pulmonary artery endothelial cells increases caspase-3/7 activity (p < 10-4). DNA methylation profiles are altered in PAH, exemplified by the pulmonary endothelial function modifier CTSZ, encoding protease cathepsin Z.
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Affiliation(s)
- Anna Ulrich
- Department of Clinical and Experimental Medicine, University of Surrey, Surrey, UK
| | - Yukyee Wu
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Harmen Draisma
- Department of Clinical and Experimental Medicine, University of Surrey, Surrey, UK
- Section of Genetics & Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - John Wharton
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Emilia M Swietlik
- VPD Heart & Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Inês Cebola
- Section of Genetics & Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Eleni Vasilaki
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Zhanna Balkhiyarova
- Department of Clinical and Experimental Medicine, University of Surrey, Surrey, UK
- Section of Genetics & Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- People-Centred Artificial Intelligence Institute, University of Surrey, Guildford, UK
| | - Marjo-Riitta Jarvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Juha Auvinen
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Karl-Heinz Herzig
- Institute of Biomedicine, Medical Research Center Oulu, Oulu University and Oulu University Hospital, Oulu, Finland
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, Poznan, Poland
| | | | | | - Colin Church
- Golden Jubilee National Hospital and University of Glasgow, Glasgow, UK
| | - Luke S Howard
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Mark Toshner
- VPD Heart & Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Stephen J Wort
- National Heart and Lung Institute, Imperial College London, London, UK
- National PH Service, Royal Brompton Hospital, London, UK
| | - David G Kiely
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
- NIHR Biomedical Research Centre Sheffield, Sheffield, UK
| | - Robin Condliffe
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - Allan Lawrie
- National Heart and Lung Institute, Imperial College London, London, UK
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Stefan Gräf
- VPD Heart & Lung Research Institute, University of Cambridge, Cambridge, UK
- NIHR BioResource for Translational Research, Cambridge Biomedical Campus, Cambridge, UK
| | - Nicholas W Morrell
- VPD Heart & Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Martin R Wilkins
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Inga Prokopenko
- Department of Clinical and Experimental Medicine, University of Surrey, Surrey, UK
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8
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Lin PY, Chang YT, Huang YC, Chen PY. Estimating genome-wide DNA methylation heterogeneity with methylation patterns. Epigenetics Chromatin 2023; 16:44. [PMID: 37941029 PMCID: PMC10634068 DOI: 10.1186/s13072-023-00521-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 10/30/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment. RESULTS Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection. CONCLUSIONS We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.
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Affiliation(s)
- Pei-Yu Lin
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
| | - Ya-Ting Chang
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
| | - Yu-Chun Huang
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, 115, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei, 115, Taiwan
| | - Pao-Yang Chen
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115, Taiwan.
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, 115, Taiwan.
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9
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Kerr L, Kafetzopoulos I, Grima R, Sproul D. Genome-wide single-molecule analysis of long-read DNA methylation reveals heterogeneous patterns at heterochromatin that reflect nucleosome organisation. PLoS Genet 2023; 19:e1010958. [PMID: 37782664 PMCID: PMC10569558 DOI: 10.1371/journal.pgen.1010958] [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: 05/01/2023] [Revised: 10/12/2023] [Accepted: 09/04/2023] [Indexed: 10/04/2023] Open
Abstract
High-throughput sequencing technology is central to our current understanding of the human methylome. The vast majority of studies use chemical conversion to analyse bulk-level patterns of DNA methylation across the genome from a population of cells. While this technology has been used to probe single-molecule methylation patterns, such analyses are limited to short reads of a few hundred basepairs. DNA methylation can also be directly detected using Nanopore sequencing which can generate reads measuring megabases in length. However, thus far these analyses have largely focused on bulk-level assessment of DNA methylation. Here, we analyse DNA methylation in single Nanopore reads from human lymphoblastoid cells, to show that bulk-level metrics underestimate large-scale heterogeneity in the methylome. We use the correlation in methylation state between neighbouring sites to quantify single-molecule heterogeneity and find that heterogeneity varies significantly across the human genome, with some regions having heterogeneous methylation patterns at the single-molecule level and others possessing more homogeneous methylation patterns. By comparing the genomic distribution of the correlation to epigenomic annotations, we find that the greatest heterogeneity in single-molecule patterns is observed within heterochromatic partially methylated domains (PMDs). In contrast, reads originating from euchromatic regions and gene bodies have more ordered DNA methylation patterns. By analysing the patterns of single molecules in more detail, we show the existence of a nucleosome-scale periodicity in DNA methylation that accounts for some of the heterogeneity we uncover in long single-molecule DNA methylation patterns. We find that this periodic structure is partially masked in bulk data and correlates with DNA accessibility as measured by nanoNOMe-seq, suggesting that it could be generated by nucleosomes. Our findings demonstrate the power of single-molecule analysis of long-read data to understand the structure of the human methylome.
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Affiliation(s)
- Lyndsay Kerr
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Ioannis Kafetzopoulos
- MRC Human Genetics Unit and CRUK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
- Current address: Altos Labs Cambridge Institute, Cambridge, United Kingdom
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Duncan Sproul
- MRC Human Genetics Unit and CRUK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
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10
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Ashouri A, Zhang C, Gaiti F. Decoding Cancer Evolution: Integrating Genetic and Non-Genetic Insights. Genes (Basel) 2023; 14:1856. [PMID: 37895205 PMCID: PMC10606072 DOI: 10.3390/genes14101856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
The development of cancer begins with cells transitioning from their multicellular nature to a state akin to unicellular organisms. This shift leads to a breakdown in the crucial regulators inherent to multicellularity, resulting in the emergence of diverse cancer cell subpopulations that have enhanced adaptability. The presence of different cell subpopulations within a tumour, known as intratumoural heterogeneity (ITH), poses challenges for cancer treatment. In this review, we delve into the dynamics of the shift from multicellularity to unicellularity during cancer onset and progression. We highlight the role of genetic and non-genetic factors, as well as tumour microenvironment, in promoting ITH and cancer evolution. Additionally, we shed light on the latest advancements in omics technologies that allow for in-depth analysis of tumours at the single-cell level and their spatial organization within the tissue. Obtaining such detailed information is crucial for deepening our understanding of the diverse evolutionary paths of cancer, allowing for the development of effective therapies targeting the key drivers of cancer evolution.
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Affiliation(s)
- Arghavan Ashouri
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
| | - Chufan Zhang
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Federico Gaiti
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
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11
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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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12
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Benetatos L, Vartholomatos G. Embryonic transcription and epigenetics: root of the evil. Hum Cell 2023; 36:1830-1833. [PMID: 37330916 DOI: 10.1007/s13577-023-00937-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 06/13/2023] [Indexed: 06/20/2023]
Affiliation(s)
- Leonidas Benetatos
- Hematology Unit, Preveza General Hospital, Selefkias 2, 48100, Preveza, Greece.
| | - George Vartholomatos
- Molecular Biology Laboratory, Ioannina University Hospital, Niarchos Ave, 45100, Ioannina, Greece
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13
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Lee H, Lin PY, Chen PY. There's more to it: uncovering genomewide DNA methylation heterogeneity. Epigenomics 2023; 15:687-691. [PMID: 37485924 DOI: 10.2217/epi-2023-0228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023] Open
Abstract
Tweetable abstract Monitoring changes in methylation heterogeneity can be powerful in detecting disease progression early. This editorial highlights the importance of profiling methylation heterogeneity and identifies existing measures and research gaps.
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Affiliation(s)
- HueyTyng Lee
- Institute of Plant & Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan
| | - Pei-Yu Lin
- Institute of Plant & Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan
| | - Pao-Yang Chen
- Institute of Plant & Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan
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14
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Yu X, Ren J, Cui Y, Zeng R, Long H, Ma C. DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era. Front Med (Lausanne) 2023; 10:1187430. [PMID: 37215722 PMCID: PMC10192687 DOI: 10.3389/fmed.2023.1187430] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction The DNA N4-methylcytosine (4mC) site levels of those suffering from digestive system cancers were higher, and the pathogenesis of digestive system cancers may also be related to the changes in DNA 4mC levels. Identifying DNA 4mC sites is a very important step in studying the analysis of biological function and cancer prediction. Extracting accurate features from DNA sequences is the key to establishing a prediction model of effective DNA 4mC sites. This study sought to develop a new predictive model, DRSN4mCPred, which aimed to improve the performance of the predicting DNA 4mC sites. Methods The model adopted multi-scale channel attention to extract features and used attention feature fusion (AFF) to fuse features. In order to capture features information more accurately and effectively, this model utilized Deep Residual Shrinkage Network with Channel-Wise thresholds (DRSN-CW) to eliminate noise-related features and achieve a more precise feature representation, thereby, distinguishing the sites in DNA with 4mC and non-4mC. Additionally, the predictive model incorporated an inverted residual block, a Multi-scale Channel Attention Module (MS-CAM), a Bi-directional Long Short Term Memory Network (Bi-LSTM), AFF, and DRSN-CW. Results and Discussion The results indicated the predictive model DRSN4mCPred had extremely good performance in predicting the DNA 4mC sites across different species. This paper will potentially provide support for the diagnosis and treatment of gastrointestinal cancer based on artificial intelligence in the precise medical era.
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Affiliation(s)
- Xia Yu
- School of Information and Communication Engineering, Hainan University, Haikou, Hainan, China
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
| | - Jia Ren
- Industrial Design School, Shandong University of ART and Design, Jinan, Shandong, China
| | - Yani Cui
- School of Information and Communication Engineering, Hainan University, Haikou, Hainan, China
| | - Rao Zeng
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
| | - Haixia Long
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
| | - Cuihua Ma
- School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China
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15
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Fang Y, Ji Z, Zhou W, Abante J, Koldobskiy MA, Ji H, Feinberg A. DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence. Nucleic Acids Res 2023; 51:2046-2065. [PMID: 36762477 PMCID: PMC10018346 DOI: 10.1093/nar/gkad050] [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: 04/20/2022] [Revised: 12/02/2022] [Accepted: 02/04/2023] [Indexed: 02/11/2023] Open
Abstract
Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.
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Affiliation(s)
- Yuqi Fang
- Center for Epigenetics, Johns Hopkins University, 855 N. Wolfe St., Baltimore, MD 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Zhicheng Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27708, USA
| | - Weiqiang Zhou
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, USA
| | - Jordi Abante
- Department of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Michael A Koldobskiy
- Center for Epigenetics, Johns Hopkins University, 855 N. Wolfe St., Baltimore, MD 21205, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 855 N. Wolfe St., Baltimore, MD 21205, USA
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, USA
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University, 855 N. Wolfe St., Baltimore, MD 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21205, USA
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16
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Lee D, Koo B, Yang J, Kim S. Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments. PLoS Comput Biol 2023; 19:e1010946. [PMID: 36940213 PMCID: PMC10062925 DOI: 10.1371/journal.pcbi.1010946] [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: 09/19/2022] [Revised: 03/30/2023] [Accepted: 02/13/2023] [Indexed: 03/21/2023] Open
Abstract
Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.
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Affiliation(s)
- Dohoon Lee
- Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea
- BK21 FOUR Intelligence Computing, Seoul National University, Seoul, Republic of Korea
| | - Bonil Koo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Jeewon Yang
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
- MOGAM Institute for Biomedical Research, Yong-in, Republic of Korea
- * E-mail:
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17
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Mao G, Pang Z, Zuo K, Liu J. Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data. J Comput Biol 2023; 30:619-631. [PMID: 36877552 DOI: 10.1089/cmb.2022.0355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On the other hand, the noise and dropout of single-cell data bring great difficulties to the analysis of scRNA-seq data, resulting in lower accuracy of gene regulatory networks reconstructed by traditional methods. In this article, we propose a novel supervised convolutional neural network (CNNSE), which can extract gene expression information from 2D co-expression matrices of gene doublets and identify interactions between genes. Our method can avoid the loss of extreme point interference by constructing a 2D co-expression matrix of gene pairs and significantly improve the regulation precision between gene pairs. And the CNNSE model is able to obtain detailed and high-level semantic information from the 2D co-expression matrix. Our method achieves satisfactory results on simulated data [accuracy (ACC): 0.712, F1: 0.724]. On two real scRNA-seq datasets, our method exhibits higher stability and accuracy in inference tasks compared with other existing gene regulatory network inference algorithms.
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Affiliation(s)
- Guo Mao
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Zhengbin Pang
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Ke Zuo
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
| | - Jie Liu
- Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China
- Laboratory of Software Engineering for Complex System, National University of Defense Technology, Changsha, China
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18
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Kreibich E, Kleinendorst R, Barzaghi G, Kaspar S, Krebs AR. Single-molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation. Mol Cell 2023; 83:787-802.e9. [PMID: 36758546 DOI: 10.1016/j.molcel.2023.01.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 11/21/2022] [Accepted: 01/16/2023] [Indexed: 02/11/2023]
Abstract
Enhancers are cis-regulatory elements that control the establishment of cell identities during development. In mammals, enhancer activation is tightly coupled with DNA demethylation. However, whether this epigenetic remodeling is necessary for enhancer activation is unknown. Here, we adapted single-molecule footprinting to measure chromatin accessibility and transcription factor binding as a function of the presence of methylation on the same DNA molecules. We leveraged natural epigenetic heterogeneity at active enhancers to test the impact of DNA methylation on their chromatin accessibility in multiple cell lineages. Although reduction of DNA methylation appears dispensable for the activity of most enhancers, we identify a class of cell-type-specific enhancers where DNA methylation antagonizes the binding of transcription factors. Genetic perturbations reveal that chromatin accessibility and transcription factor binding require active demethylation at these loci. Thus, in addition to safeguarding the genome from spurious activation, DNA methylation directly controls transcription factor occupancy at active enhancers.
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Affiliation(s)
- Elisa Kreibich
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany; Faculty of Biosciences, Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Heidelberg, Germany
| | - Rozemarijn Kleinendorst
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Guido Barzaghi
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany; Faculty of Biosciences, Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Heidelberg, Germany
| | - Sarah Kaspar
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Arnaud R Krebs
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
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19
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Vaidya H, Jeong HS, Keith K, Maegawa S, Calendo G, Madzo J, Jelinek J, Issa JPJ. DNA methylation entropy as a measure of stem cell replication and aging. Genome Biol 2023; 24:27. [PMID: 36797759 PMCID: PMC9933260 DOI: 10.1186/s13059-023-02866-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 02/05/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Epigenetic marks are encoded by DNA methylation and accumulate errors as organisms age. This drift correlates with lifespan, but the biology of how this occurs is still unexplained. We analyze DNA methylation with age in mouse intestinal stem cells and compare them to nonstem cells. RESULTS Age-related changes in DNA methylation are identical in stem and nonstem cells, affect most prominently CpG islands and correlate weakly with gene expression. Age-related DNA methylation entropy, measured by the Jensen-Shannon Distribution, affects up to 25% of the detectable CpG sites and is a better measure of aging than individual CpG methylation. We analyze this entropy as a function of age in seven other tissues (heart, kidney, skeletal muscle, lung, liver, spleen, and blood) and it correlates strikingly with tissue-specific stem cell division rates. Thus, DNA methylation drift and increased entropy with age are primarily caused by and are sensors for, stem cell replication in adult tissues. CONCLUSIONS These data have implications for the mechanisms of tissue-specific functional declines with aging and for the development of DNA-methylation-based biological clocks.
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Affiliation(s)
- Himani Vaidya
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ 08013 USA
| | - Hye Seon Jeong
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ 08013 USA ,grid.411665.10000 0004 0647 2279Department of Neurology, Chungnam National University Hospital, Daejeon, South Korea
| | - Kelsey Keith
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ 08013 USA
| | - Shinji Maegawa
- grid.240145.60000 0001 2291 4776Department of Pediatrics, University of Texas, MD Anderson Cancer Center, Houston, TX USA
| | - Gennaro Calendo
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ 08013 USA
| | - Jozef Madzo
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ 08013 USA
| | - Jaroslav Jelinek
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ 08013 USA
| | - Jean-Pierre J. Issa
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ 08013 USA
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20
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Feinberg AP, Levchenko A. Epigenetics as a mediator of plasticity in cancer. Science 2023; 379:eaaw3835. [PMID: 36758093 PMCID: PMC10249049 DOI: 10.1126/science.aaw3835] [Citation(s) in RCA: 72] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 12/22/2022] [Indexed: 02/11/2023]
Abstract
The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.
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Affiliation(s)
- Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University Schools of Medicine, Biomedical Engineering, and Public Health, Baltimore, MD 21205, USA
| | - Andre Levchenko
- Yale Systems Biology Institute and Department of Biomedical Engineering, Yale University, West Haven, CT 06516, USA
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21
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Iqbal W, Zhou W. Computational Methods for Single-cell DNA Methylome Analysis. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:48-66. [PMID: 35718270 PMCID: PMC10372927 DOI: 10.1016/j.gpb.2022.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/28/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022]
Abstract
Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.
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Affiliation(s)
- Waleed Iqbal
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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22
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Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas. Brain Sci 2023; 13:brainsci13020186. [PMID: 36831729 PMCID: PMC9953940 DOI: 10.3390/brainsci13020186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.
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23
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Li S. Inferring the Cancer Cellular Epigenome Heterogeneity via DNA Methylation Patterns. Cancer Treat Res 2023; 190:375-393. [PMID: 38113008 DOI: 10.1007/978-3-031-45654-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Tumor cells evolve through space and time, generating genetically and phenotypically diverse cancer cell populations that are continually subjected to the selection pressures of their microenvironment and cancer treatment.
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Affiliation(s)
- Sheng Li
- The Jackson Laboratory for Genomic Medicine and Cancer Center, Farmington, USA.
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24
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Raineri E, Alberola I Pla M, Dabad M, Heath S. cvlr: finding heterogeneously methylated genomic regions using ONT reads. BIOINFORMATICS ADVANCES 2023; 3:vbac101. [PMID: 36726731 PMCID: PMC9887406 DOI: 10.1093/bioadv/vbac101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/07/2022] [Accepted: 01/03/2023] [Indexed: 01/25/2023]
Abstract
Summary Nanopore reads encode information on the methylation status of cytosines in CpG dinucleotides. The length of the reads makes it comparatively easy to look at patterns consisting of multiple loci; here, we exploit this property to search for regions where one can define subpopulations of molecules based on methylation patterns. As an example, we run our clustering algorithm on known imprinted genes; we also scan chromosome 15 looking for windows corresponding to heterogeneous methylation. Our software can also compute the covariance of methylation across these regions while keeping into account the mixture of different types of reads. Availability and implementation https://github.com/EmanueleRaineri/cvlr. Contact simon.heath@cnag.crg.eu. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Emanuele Raineri
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
| | - Mariona Alberola I Pla
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
| | - Marc Dabad
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
| | - Simon Heath
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08028, Spain
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25
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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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Jin J, Yu Y, Wang R, Zeng X, Pang C, Jiang Y, Li Z, Dai Y, Su R, Zou Q, Nakai K, Wei L. iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations. Genome Biol 2022; 23:219. [PMID: 36253864 PMCID: PMC9575223 DOI: 10.1186/s13059-022-02780-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/03/2022] [Indexed: 11/29/2022] Open
Abstract
In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.
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Affiliation(s)
- Junru Jin
- School of Software, Shandong University, Jinan, 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yingying Yu
- School of Software, Shandong University, Jinan, 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan, 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Xin Zeng
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
- Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan
| | - Chao Pang
- School of Software, Shandong University, Jinan, 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yi Jiang
- School of Software, Shandong University, Jinan, 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Zhongshen Li
- School of Software, Shandong University, Jinan, 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yutong Dai
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
- Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Kenta Nakai
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.
- Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan.
| | - Leyi Wei
- School of Software, Shandong University, Jinan, 250101, China.
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China.
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Ren H, Taylor RB, Downing TL, Read EL. Locally correlated kinetics of post-replication DNA methylation reveals processivity and region specificity in DNA methylation maintenance. J R Soc Interface 2022; 19:20220415. [PMID: 36285438 PMCID: PMC9597173 DOI: 10.1098/rsif.2022.0415] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
DNA methylation occurs predominantly on cytosine-phosphate-guanine (CpG) dinucleotides in the mammalian genome, and the methylation landscape is maintained over mitotic cell division. It has been posited that coupling of maintenance methylation activity among neighbouring CpGs is critical to stability over cellular generations; however, the mechanism is unclear. We used mathematical models and stochastic simulation to analyse data from experiments that probe genome-wide methylation of nascent DNA post-replication in cells. We find that DNA methylation maintenance rates on individual CpGs are locally correlated, and the degree of this correlation varies by genomic regional context. By using theory of protein diffusion along DNA, we show that exponential decay of methylation rate correlation with genomic distance is consistent with enzyme processivity. Our results provide quantitative evidence of genome-wide methyltransferase processivity in vivo. We further developed a method to disentangle different mechanistic sources of kinetic correlations. From the experimental data, we estimate that an individual methyltransferase methylates neighbour CpGs processively if they are 36 basepairs apart, on average. But other mechanisms of coupling dominate for longer inter-CpG distances. Our study demonstrates that quantitative insights into enzymatic mechanisms can be obtained from replication-associated, cell-based genome-wide measurements, by combining data-driven statistical analyses with hypothesis-driven mathematical modelling.
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Affiliation(s)
- Honglei Ren
- NSF-Simons Center for Multiscale Cell Fate, University of California, Irvine, CA 92697, USA,Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA
| | - Robert B. Taylor
- Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA,Department of Physics, University of California, Irvine, CA 92697, USA
| | - Timothy L. Downing
- NSF-Simons Center for Multiscale Cell Fate, University of California, Irvine, CA 92697, USA,Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA,Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA,Department of Microbiology and Molecular Genetics, University of California, Irvine, CA 92697, USA
| | - Elizabeth L. Read
- NSF-Simons Center for Multiscale Cell Fate, University of California, Irvine, CA 92697, USA,Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA 92697, USA,Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA
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Dahl E, Villwock S, Habenberger P, Choidas A, Rose M, Klebl BM. White Paper: Mimetics of Class 2 Tumor Suppressor Proteins as Novel Drug Candidates for Personalized Cancer Therapy. Cancers (Basel) 2022; 14:cancers14184386. [PMID: 36139547 PMCID: PMC9496810 DOI: 10.3390/cancers14184386] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/01/2022] [Accepted: 09/07/2022] [Indexed: 11/21/2022] Open
Abstract
Simple Summary A concept is presented for a new therapeutic approach, still in its early stages, which focuses on the phenotypic mimicry (“mimesis”) of proteins encoded by highly disease-relevant class 2 tumor suppressor genes that are silenced by DNA promoter methylation. Proteins derived from tumor suppressor genes are usually considered control systems of cells against oncogenic properties. Thus they represent the brakes in the “car-of-life.” Restoring this “brake function” in tumors by administering mimetic drugs may have a significant therapeutic effect. The proposed approach could thus open up a new, hitherto unexploited area of research for the development of anticancer drugs for difficult-to-treat cancers. Abstract The aim of our proposed concept is to find new target structures for combating cancers with unmet medical needs. This, unfortunately, still applies to the majority of the clinically most relevant tumor entities such as, for example, liver cancer, pancreatic cancer, and many others. Current target structures almost all belong to the class of oncogenic proteins caused by tumor-specific genetic alterations, such as activating mutations, gene fusions, or gene amplifications, often referred to as cancer “driver alterations” or just “drivers.” However, restoring the lost function of tumor suppressor genes (TSGs) could also be a valid approach to treating cancer. TSG-derived proteins are usually considered as control systems of cells against oncogenic properties; thus, they represent the brakes in the “car-of-life.” Restoring these tumor-defective brakes by gene therapy has not been successful so far, with a few exceptions. It can be assumed that most TSGs are not being inactivated by genetic alteration (class 1 TSGs) but rather by epigenetic silencing (class 2 TSGs or short “C2TSGs”). Reactivation of C2TSGs in cancer therapy is being addressed by the use of DNA demethylating agents and histone deacetylase inhibitors which act on the whole cancer cell genome. These epigenetic therapies have neither been particularly successful, probably because they are “shotgun” approaches that, although acting on C2TSGs, may also reactivate epigenetically silenced oncogenic sequences in the genome. Thus, new strategies are needed to exploit the therapeutic potential of C2TSGs, which have also been named DNA methylation cancer driver genes or “DNAme drivers” recently. Here we present a concept for a new translational and therapeutic approach that focuses on the phenotypic imitation (“mimesis”) of proteins encoded by highly disease-relevant C2TSGs/DNAme drivers. Molecular knowledge on C2TSGs is used in two complementary approaches having the translational concept of defining mimetic drugs in common: First, a concept is presented how truncated and/or genetically engineered C2TSG proteins, consisting solely of domains with defined tumor suppressive function can be developed as biologicals. Second, a method is described for identifying small molecules that can mimic the effect of the C2TSG protein lost in the cancer cell. Both approaches should open up a new, previously untapped discovery space for anticancer drugs.
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Affiliation(s)
- Edgar Dahl
- Institute of Pathology, Medical Faculty, RWTH Aachen University, D-52074 Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), D-52074 Aachen, Germany
- Correspondence:
| | - Sophia Villwock
- Institute of Pathology, Medical Faculty, RWTH Aachen University, D-52074 Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), D-52074 Aachen, Germany
| | - Peter Habenberger
- Lead Discovery Center GmbH (LDC), Otto-Hahn-Straße 15, D-44227 Dortmund, Germany
| | - Axel Choidas
- Lead Discovery Center GmbH (LDC), Otto-Hahn-Straße 15, D-44227 Dortmund, Germany
| | - Michael Rose
- Institute of Pathology, Medical Faculty, RWTH Aachen University, D-52074 Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), D-52074 Aachen, Germany
| | - Bert M. Klebl
- Lead Discovery Center GmbH (LDC), Otto-Hahn-Straße 15, D-44227 Dortmund, Germany
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Benetatos L, Benetatou A, Vartholomatos G. Epialleles and epiallelic heterogeneity in hematological malignancies. MEDICAL ONCOLOGY (NORTHWOOD, LONDON, ENGLAND) 2022; 39:139. [PMID: 35834015 DOI: 10.1007/s12032-022-01737-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/22/2022] [Indexed: 10/17/2022]
Abstract
DNA methylation has a well-established role in the pathogenesis, prognosis, and response to treatment in all the spectra of hematological malignancies. However, most of the data reported involve average DNA methylation observed in a sample. The emergence of bisulfite sequencing methods such as enhanced reduced representation that permit analyze adjacent CpGs led to exciting findings. Among these are the epialleles shift and the resulting epigenetic heterogeneity observed in leukemias and lymphomas. Epialleles seem to have an influential role as the cause of mutations that characterize leukemias, may stratify groups with different prognosis and response to treatment, and may be redistributed in the genome at different time points of the disease promoting activation of alternate transcriptional networks. Epiallelic shift may be responsible for the intratumor heterogeneity observed within the cells of the same tumor which increases with disease aggressiveness. It may also responsible for the interpatient heterogeneity explaining why blood cancers exhibit different behavior among different patients. Understanding better epiallelic conformation and the consequent chromatin conformational changes and the pathways that may be affected will permit deeper understanding of hematological malignancies pathogenesis and treatment.
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Affiliation(s)
- Leonidas Benetatos
- Blood Bank, Preveza General Hospital, Selefkias 2, 48100, Preveza, Greece.
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30
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Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00469-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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31
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Janssen SM, Lorincz MC. Interplay between chromatin marks in development and disease. Nat Rev Genet 2022; 23:137-153. [PMID: 34608297 DOI: 10.1038/s41576-021-00416-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 02/07/2023]
Abstract
DNA methylation (DNAme) and histone post-translational modifications (PTMs) have important roles in transcriptional regulation. Although many reports have characterized the functions of such chromatin marks in isolation, recent genome-wide studies reveal surprisingly complex interactions between them. Here, we focus on the interplay between DNAme and methylation of specific lysine residues on the histone H3 tail. We describe the impact of genetic perturbation of the relevant methyltransferases in the mouse on the landscape of chromatin marks as well as the transcriptome. In addition, we discuss the specific neurodevelopmental growth syndromes and cancers resulting from pathogenic mutations in the human orthologues of these genes. Integrating these observations underscores the fundamental importance of crosstalk between DNA and histone H3 methylation in development and disease.
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Affiliation(s)
- Sanne M Janssen
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthew C Lorincz
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada.
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32
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Feng J, Zhao D, Lv F, Yuan Z. Epigenetic Inheritance From Normal Origin Cells Can Determine the Aggressive Biology of Tumor-Initiating Cells and Tumor Heterogeneity. Cancer Control 2022; 29:10732748221078160. [PMID: 35213254 PMCID: PMC8891845 DOI: 10.1177/10732748221078160] [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] [Indexed: 12/03/2022] Open
Abstract
The acquisition of genetic- and epigenetic-abnormalities during transformation has been recognized as the two fundamental factors that lead to tumorigenesis and determine the aggressive biology of tumor cells. However, there is a regularity that tumors derived from less-differentiated normal origin cells (NOCs) usually have a higher risk of vascular involvement, lymphatic and distant metastasis, which can be observed in both lymphohematopoietic malignancies and somatic cancers. Obviously, the hypothesis of genetic- and epigenetic-abnormalities is not sufficient to explain how the linear relationship between the cellular origin and the biological behavior of tumors is formed, because the cell origin of tumor is an independent factor related to tumor biology. In a given system, tumors can originate from multiple cell types, and tumor-initiating cells (TICs) can be mapped to different differentiation hierarchies of normal stem cells, suggesting that the heterogeneity of the origin of TICs is not completely chaotic. TIC’s epigenome includes not only genetic- and epigenetic-abnormalities, but also established epigenetic status of genes inherited from NOCs. In reviewing previous studies, we found much evidence supporting that the status of many tumor-related “epigenetic abnormalities” in TICs is consistent with that of the corresponding NOC of the same differentiation hierarchy, suggesting that they may not be true epigenetic abnormalities. So, we speculate that the established statuses of genes that control NOC’s migration, adhesion and colonization capabilities, cell-cycle quiescence, expression of drug transporters, induction of mesenchymal formation, overexpression of telomerase, and preference for glycolysis can be inherited to TICs through epigenetic memory and be manifested as their aggressive biology. TICs of different origins can maintain different degrees of innate stemness from NOC, which may explain why malignancies with stem cell phenotypes are usually more aggressive.
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Affiliation(s)
- Jiliang Feng
- Clinical-Pathology Center, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
| | - Dawei Zhao
- Medical Imaging Department, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
| | - Fudong Lv
- Clinical-Pathology Center, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
| | - Zhongyu Yuan
- Clinical-Pathology Center, Capital Medical University Affiliated Beijing Youan Hospital, Beijing, China
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Rodriguez-Algarra F, Seaborne RAE, Danson AF, Yildizoglu S, Yoshikawa H, Law PP, Ahmad Z, Maudsley VA, Brew A, Holmes N, Ochôa M, Hodgkinson A, Marzi SJ, Pradeepa MM, Loose M, Holland ML, Rakyan VK. Genetic variation at mouse and human ribosomal DNA influences associated epigenetic states. Genome Biol 2022; 23:54. [PMID: 35164830 PMCID: PMC8842540 DOI: 10.1186/s13059-022-02617-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 01/24/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Ribosomal DNA (rDNA) displays substantial inter-individual genetic variation in human and mouse. A systematic analysis of how this variation impacts epigenetic states and expression of the rDNA has thus far not been performed. RESULTS Using a combination of long- and short-read sequencing, we establish that 45S rDNA units in the C57BL/6J mouse strain exist as distinct genetic haplotypes that influence the epigenetic state and transcriptional output of any given unit. DNA methylation dynamics at these haplotypes are dichotomous and life-stage specific: at one haplotype, the DNA methylation state is sensitive to the in utero environment, but refractory to post-weaning influences, whereas other haplotypes entropically gain DNA methylation during aging only. On the other hand, individual rDNA units in human show limited evidence of genetic haplotypes, and hence little discernible correlation between genetic and epigenetic states. However, in both species, adjacent units show similar epigenetic profiles, and the overall epigenetic state at rDNA is strongly positively correlated with the total rDNA copy number. Analysis of different mouse inbred strains reveals that in some strains, such as 129S1/SvImJ, the rDNA copy number is only approximately 150 copies per diploid genome and DNA methylation levels are < 5%. CONCLUSIONS Our work demonstrates that rDNA-associated genetic variation has a considerable influence on rDNA epigenetic state and consequently rRNA expression outcomes. In the future, it will be important to consider the impact of inter-individual rDNA (epi)genetic variation on mammalian phenotypes and diseases.
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Affiliation(s)
| | - Robert A E Seaborne
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Amy F Danson
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Present Address: German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Selin Yildizoglu
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Harunori Yoshikawa
- Fujii Memorial Institute of Medical Sciences, Institute of Advanced Medical Sciences, Tokushima University, Tokushima, Japan
| | - Pui Pik Law
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Zakaryya Ahmad
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Victoria A Maudsley
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ama Brew
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nadine Holmes
- DeepSeq, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Mateus Ochôa
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Alan Hodgkinson
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Madapura M Pradeepa
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Matthew Loose
- DeepSeq, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Michelle L Holland
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK.
| | - Vardhman K Rakyan
- The Blizard Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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Fried JP, Wu Y, Tilley RD, Gooding JJ. Optical Nanopore Sensors for Quantitative Analysis. NANO LETTERS 2022; 22:869-880. [PMID: 35089719 DOI: 10.1021/acs.nanolett.1c03976] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Nanopore sensors have received significant interest for the detection of clinically important biomarkers with single-molecule resolution. These sensors typically operate by detecting changes in the ionic current through a nanopore due to the translocation of an analyte. Recently, there has been interest in developing optical readout strategies for nanopore sensors for quantitative analysis. This is because they can utilize wide-field microscopy to independently monitor many nanopores within a high-density array. This significantly increases the amount of statistics that can be obtained, thus enabling the analysis of analytes present at ultralow concentrations. Here, we review the use of optical nanopore sensing strategies for quantitative analysis. We discuss optical nanopore sensing assays that have been developed to detect clinically relevant biomarkers, the potential for multiplexing such measurements, and techniques to fabricate high density arrays of nanopores with a view toward the use of these devices for clinical applications.
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Affiliation(s)
- Jasper P Fried
- School of Chemistry, Australian Centre for NanoMedicine, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, The University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Yanfang Wu
- School of Chemistry, Australian Centre for NanoMedicine, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, The University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Richard D Tilley
- School of Chemistry, Australian Centre for NanoMedicine, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, The University of New South Wales, Sydney, New South Wales 2052, Australia
| | - J Justin Gooding
- School of Chemistry, Australian Centre for NanoMedicine, ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, The University of New South Wales, Sydney, New South Wales 2052, Australia
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35
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Wedd L, Kucharski R, Maleszka R. DNA Methylation in Honey Bees and the Unresolved Questions in Insect Methylomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1389:159-176. [DOI: 10.1007/978-3-031-11454-0_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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36
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Sarnataro A, De Riso G, Cocozza S, Pezone A, Majello B, Amente S, Scala G. A novel workflow for the qualitative analysis of DNA methylation data. Comput Struct Biotechnol J 2022; 20:5925-5934. [PMID: 36382198 PMCID: PMC9636440 DOI: 10.1016/j.csbj.2022.10.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/30/2022] Open
Abstract
A novel R package (EpiStatProfiler) for the qualitative analysis of DNA methylation data. A novel workflow for the analysis of CG and non-CG epialleles starting from any type of bisulfite sequencing data. EpiStatProfiler can perform strand-specific characterization of epialleles composition. Important loci can be annotated along with their biological role and potential functions. EpiStatProfiler has the ability to identify loci whose epiallelic profile is associated with disease pathogenesis.
DNA methylation is an epigenetic modification that plays a pivotal role in major biological mechanisms, such as gene regulation, genomic imprinting, and genome stability. Different combinations of methylated cytosines for a given DNA locus generate different epialleles and alterations of these latter have been associated with several pathological conditions. Existing computational methods and statistical tests relevant to DNA methylation analysis are mostly based on the comparison of average CpG sites methylation levels and they often neglect non-CG methylation. Here, we present EpiStatProfiler, an R package that allows the analysis of CpG and non-CpG based epialleles starting from bisulfite sequencing data through a collection of dedicated extraction functions and statistical tests. EpiStatProfiler is provided with a set of useful auxiliary features, such as customizable genomic ranges, strand-specific epialleles analysis, locus annotation and gene set enrichment analysis. We showcase the package functionalities on two public datasets by identifying putative relevant loci in mice harboring the Huntington’s disease-causing Htt gene mutation and in Ctcf +/− mice compared to their wild-type counterparts. To our knowledge, EpiStatProfiler is the first package providing functionalities dedicated to the analysis of epialleles composition derived from any kind of bisulfite sequencing experiment.
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Abstract
Motivation Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns. Results We proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest. Availability and implementation MeConcord is available at https://github.com/WangLabTHU/MeConcord. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xianglin Zhang
- Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaowo Wang
- To whom correspondence should be addressed. E-mail:
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38
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Mc Auley MT. DNA methylation in genes associated with the evolution of ageing and disease: A critical review. Ageing Res Rev 2021; 72:101488. [PMID: 34662746 DOI: 10.1016/j.arr.2021.101488] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/30/2021] [Accepted: 10/12/2021] [Indexed: 12/28/2022]
Abstract
Ageing is characterised by a physical decline in biological functioning which results in a progressive risk of mortality with time. As a biological phenomenon, it is underpinned by the dysregulation of a myriad of complex processes. Recently, however, ever-increasing evidence has associated epigenetic mechanisms, such as DNA methylation (DNAm) with age-onset pathologies, including cancer, cardiovascular disease, and Alzheimer's disease. These diseases compromise healthspan. Consequently, there is a medical imperative to understand the link between epigenetic ageing, and healthspan. Evolutionary theory provides a unique way to gain new insights into epigenetic ageing and health. This review will: (1) provide a brief overview of the main evolutionary theories of ageing; (2) discuss recent genetic evidence which has revealed alleles that have pleiotropic effects on fitness at different ages in humans; (3) consider the effects of DNAm on pleiotropic alleles, which are associated with age related disease; (4) discuss how age related DNAm changes resonate with the mutation accumulation, disposable soma and programmed theories of ageing; (5) discuss how DNAm changes associated with caloric restriction intersect with the evolution of ageing; and (6) conclude by discussing how evolutionary theory can be used to inform investigations which quantify age-related DNAm changes which are linked to age onset pathology.
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Affiliation(s)
- Mark Tomás Mc Auley
- Faculty of Science and Engineering, University of Chester, Exton Park, Chester CH1 4BJ, UK.
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39
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Zagkos L, Roberts J, Auley MM. A mathematical model which examines age-related stochastic fluctuations in DNA maintenance methylation. Exp Gerontol 2021; 156:111623. [PMID: 34774717 DOI: 10.1016/j.exger.2021.111623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/30/2021] [Accepted: 11/04/2021] [Indexed: 10/19/2022]
Abstract
Due to its complexity and its ubiquitous nature the ageing process remains an enduring biological puzzle. Many molecular mechanisms and biochemical process have become synonymous with ageing. However, recent findings have pinpointed epigenetics as having a key role in ageing and healthspan. In particular age related changes to DNA methylation offer the possibility of monitoring the trajectory of biological ageing and could even be used to predict the onset of diseases such as cancer, Alzheimer's disease and cardiovascular disease. At the molecular level emerging evidence strongly suggests the regulatory processes which govern DNA methylation are subject to intracellular stochasticity. It is challenging to fully understand the impact of stochasticity on DNA methylation levels at the molecular level experimentally. An ideal solution is to use mathematical models to capture the essence of the stochasticity and its outcomes. In this paper we present a novel stochastic model which accounts for specific methylation levels within a gene promoter. Uncertainty of the eventual site-specific methylation levels for different values of methylation age, depending on the initial methylation levels were analysed. Our model predicts the observed bistable levels in CpG islands. In addition, simulations with various levels of noise indicate that uncertainty predominantly spreads through the hypermethylated region of stability, especially for large values of input noise. A key outcome of the model is that CpG islands with high to intermediate methylation levels tend to be more susceptible to dramatic DNA methylation changes due to increasing methylation age.
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Affiliation(s)
- Loukas Zagkos
- Department of Mathematics, School of Science and Engineering, University of Chester, Thornton Science Park, Chester CH2 4NU, UK; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, London W2 1PG, UK.
| | - Jason Roberts
- Department of Mathematics, School of Science and Engineering, University of Chester, Thornton Science Park, Chester CH2 4NU, UK
| | - Mark Mc Auley
- Department of Chemical Engineering, School of Science and Engineering, University of Chester, Thornton Science Park, Chester CH2 4NU, UK
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40
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Ni P, Huang N, Nie F, Zhang J, Zhang Z, Wu B, Bai L, Liu W, Xiao CL, Luo F, Wang J. Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning. Nat Commun 2021; 12:5976. [PMID: 34645826 PMCID: PMC8514461 DOI: 10.1038/s41467-021-26278-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 09/28/2021] [Indexed: 11/09/2022] Open
Abstract
In plants, cytosine DNA methylations (5mCs) can happen in three sequence contexts as CpG, CHG, and CHH (where H = A, C, or T), which play different roles in the regulation of biological processes. Although long Nanopore reads are advantageous in the detection of 5mCs comparing to short-read bisulfite sequencing, existing methods can only detect 5mCs in the CpG context, which limits their application in plants. Here, we develop DeepSignal-plant, a deep learning tool to detect genome-wide 5mCs of all three contexts in plants from Nanopore reads. We sequence Arabidopsis thaliana and Oryza sativa using both Nanopore and bisulfite sequencing. We develop a denoising process for training models, which enables DeepSignal-plant to achieve high correlations with bisulfite sequencing for 5mC detection in all three contexts. Furthermore, DeepSignal-plant can profile more 5mC sites, which will help to provide a more complete understanding of epigenetic mechanisms of different biological processes.
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Affiliation(s)
- Peng Ni
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Neng Huang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Fan Nie
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Jun Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Zhi Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Bo Wu
- School of Computing, Clemson University, Clemson, SC, 29634-0974, USA
| | - Lu Bai
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wende Liu
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chuan-Le Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, #7 Jinsui Road, Tianhe District, Guangzhou, China.
| | - Feng Luo
- School of Computing, Clemson University, Clemson, SC, 29634-0974, USA.
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
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41
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Hetzel S, Giesselmann P, Reinert K, Meissner A, Kretzmer H. RLM: Fast and simplified extraction of Read-Level Methylation metrics from bisulfite sequencing data. Bioinformatics 2021; 37:3934-3935. [PMID: 34601556 PMCID: PMC8686677 DOI: 10.1093/bioinformatics/btab663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/11/2021] [Accepted: 09/26/2021] [Indexed: 11/13/2022] Open
Abstract
Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzing single-read patterns. Over the past years, various informative metrics have been established to explore this information. However, limited compatibility with alignment tools, reference genomes or the measurements they provide present a bottleneck for most groups to include this information as standard analysis. To address this, we developed RLM, a fast and scalable tool for the computation of frequently used Read-Level Methylation statistics. RLM supports several common alignment tools, works independently of the reference genome and handles all frequently used sequencing experiment designs. RLM can process large input files with a billion reads in just a few hours on common workstations. AVAILABILITY https://github.com/sarahet/RLM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sara Hetzel
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Pay Giesselmann
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Knut Reinert
- Department of Mathematics and Informatics, Freie Universität, Berlin, Germany
| | - Alexander Meissner
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.,Department of Biology, Chemistry and Pharmacy, Freie Universität, Berlin, Germany.,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, US.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Helene Kretzmer
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
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42
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Chaligne R, Gaiti F, Silverbush D, Schiffman JS, Weisman HR, Kluegel L, Gritsch S, Deochand SD, Gonzalez Castro LN, Richman AR, Klughammer J, Biancalani T, Muus C, Sheridan C, Alonso A, Izzo F, Park J, Rozenblatt-Rosen O, Regev A, Suvà ML, Landau DA. Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states. Nat Genet 2021; 53:1469-1479. [PMID: 34594037 PMCID: PMC8675181 DOI: 10.1038/s41588-021-00927-7] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 07/30/2021] [Indexed: 02/08/2023]
Abstract
Single-cell RNA sequencing has revealed extensive transcriptional cell state diversity in cancer, often observed independently of genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, here we performed multiomics single-cell profiling-integrating DNA methylation, transcriptome and genotype within the same cells-of diffuse gliomas, tumors characterized by defined transcriptional cell state diversity. Direct comparison of the epigenetic profiles of distinct cell states revealed key switches for state transitions recapitulating neurodevelopmental trajectories and highlighted dysregulated epigenetic mechanisms underlying gliomagenesis. We further developed a quantitative framework to directly measure cell state heritability and transition dynamics based on high-resolution lineage trees in human samples. We demonstrated heritability of malignant cell states, with key differences in hierarchal and plastic cell state architectures in IDH-mutant glioma versus IDH-wild-type glioblastoma, respectively. This work provides a framework anchoring transcriptional cancer cell states in their epigenetic encoding, inheritance and transition dynamics.
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Affiliation(s)
- Ronan Chaligne
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Federico Gaiti
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Dana Silverbush
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Joshua S Schiffman
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Hannah R Weisman
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Lloyd Kluegel
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Simon Gritsch
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sunil D Deochand
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - L Nicolas Gonzalez Castro
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alyssa R Richman
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | | | - Christoph Muus
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | | | | | - Franco Izzo
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Jane Park
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Orit Rozenblatt-Rosen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Aviv Regev
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Mario L Suvà
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Dan A Landau
- New York Genome Center, New York, NY, USA.
- Weill Cornell Medicine, New York, NY, USA.
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43
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Johnson KC, Anderson KJ, Courtois ET, Gujar AD, Barthel FP, Varn FS, Luo D, Seignon M, Yi E, Kim H, Estecio MRH, Zhao D, Tang M, Navin NE, Maurya R, Ngan CY, Verburg N, de Witt Hamer PC, Bulsara K, Samuels ML, Das S, Robson P, Verhaak RGW. Single-cell multimodal glioma analyses identify epigenetic regulators of cellular plasticity and environmental stress response. Nat Genet 2021; 53:1456-1468. [PMID: 34594038 PMCID: PMC8570135 DOI: 10.1038/s41588-021-00926-8] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 07/27/2021] [Indexed: 02/08/2023]
Abstract
Glioma intratumoral heterogeneity enables adaptation to challenging microenvironments and contributes to therapeutic resistance. We integrated 914 single-cell DNA methylomes, 55,284 single-cell transcriptomes and bulk multi-omic profiles across 11 adult IDH mutant or IDH wild-type gliomas to delineate sources of intratumoral heterogeneity. We showed that local DNA methylation disorder is associated with cell-cell DNA methylation differences, is elevated in more aggressive tumors, links with transcriptional disruption and is altered during the environmental stress response. Glioma cells under in vitro hypoxic and irradiation stress increased local DNA methylation disorder and shifted cell states. We identified a positive association between genetic and epigenetic instability that was supported in bulk longitudinally collected DNA methylation data. Increased DNA methylation disorder associated with accelerated disease progression and recurrently selected DNA methylation changes were enriched for environmental stress response pathways. Our work identified an epigenetically facilitated adaptive stress response process and highlights the importance of epigenetic heterogeneity in shaping therapeutic outcomes.
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Affiliation(s)
- Kevin C. Johnson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.,These authors contributed equally,Co-corresponding authors: and
| | - Kevin J. Anderson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.,These authors contributed equally
| | - Elise T. Courtois
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Amit D. Gujar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Floris P. Barthel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.,Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Pathology, Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Frederick S. Varn
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Diane Luo
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Martine Seignon
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Eunhee Yi
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Hoon Kim
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Marcos RH Estecio
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, US
| | - Dacheng Zhao
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ming Tang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, US
| | - Nicholas E. Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, US
| | - Rahul Maurya
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Chew Yee Ngan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Niels Verburg
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurosurgery, Brain Tumor Center Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Philip C de Witt Hamer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurosurgery, Brain Tumor Center Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Ketan Bulsara
- Division of Neurosurgery, The University of Connecticut Health Center, Farmington, CT, US
| | | | - Sunit Das
- Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for SickKids, University of Toronto.,Institute of Medical Science, University of Toronto.,Division of Neurosurgery, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, University of Toronto
| | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.,Genetics and Genome Sciences, University of Connecticut School of Medicine
| | - Roel GW Verhaak
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.,Co-corresponding authors: and
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44
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Age-related demethylation of the TDP-43 autoregulatory region in the human motor cortex. Commun Biol 2021; 4:1107. [PMID: 34548609 PMCID: PMC8455575 DOI: 10.1038/s42003-021-02621-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 09/01/2021] [Indexed: 11/21/2022] Open
Abstract
In amyotrophic lateral sclerosis (ALS), TAR DNA-binding protein 43 (TDP-43), which is encoded by TARDBP, forms aggregates in the motor cortex. This aggregate formation may be triggered by an increase in the TDP-43 level with aging. However, the amount of TDP-43 is autoregulated by alternative splicing of the TARDBP 3′UTR, and how this autoregulation is affected by aging remains to be elucidated. We found that DNA demethylation in the autoregulatory region in the TARDBP 3′UTR reduced alternative splicing and increased TARDBP mRNA expression. Furthermore, in the human motor cortex, we found that this region was demethylated with aging, resulting in increased expression of TARDBP mRNA. The acceleration of DNA demethylation in the motor cortex was associated with the age of ALS onset. In summary, the dysregulation of TDP-43 autoregulation by age-related DNA demethylation in the motor cortex may explain the contribution of aging and motor system selectivity in ALS. In order to assess the effects of aging on the autoregulation of TAR DNA-binding protein 43 (TDP-43) and the potential effects of this on the role of TDP-43 in Amyotrophic Lateral Sclerosis (ALS), Koike et al examined post-mortem motor cortex tissue from ALS patients. They found that DNA demethylation in the autoregulatory region of the TARDBP 3′UTR, which encodes TDP-43, increased with age and was associated with the onset age of ALS and thus could be indicative of a role for dysregulation of TDP-43 autoregulation in ALS pathology.
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45
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Batra RN, Lifshitz A, Vidakovic AT, Chin SF, Sati-Batra A, Sammut SJ, Provenzano E, Ali HR, Dariush A, Bruna A, Murphy L, Purushotham A, Ellis I, Green A, Garrett-Bakelman FE, Mason C, Melnick A, Aparicio SAJR, Rueda OM, Tanay A, Caldas C. DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation. Nat Commun 2021; 12:5406. [PMID: 34518533 PMCID: PMC8437946 DOI: 10.1038/s41467-021-25661-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/18/2021] [Indexed: 11/08/2022] Open
Abstract
DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis-specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors.
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Affiliation(s)
- Rajbir Nath Batra
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
- German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
| | - Aviezer Lifshitz
- Department of Computer Science and Applied Mathematics, and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | | | - Suet-Feung Chin
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Ankita Sati-Batra
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Stephen-John Sammut
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, Cambridge, UK
- Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Elena Provenzano
- Cancer Research UK Cambridge Centre, Cambridge, UK
- Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - H Raza Ali
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, Cambridge, UK
- Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Ali Dariush
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Alejandra Bruna
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Leigh Murphy
- Research Institute in Oncology and Hematology, Winnipeg, Manitoba, Canada
| | - Arnie Purushotham
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Ian Ellis
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospital NHS Trust, Nottingham, UK
| | - Andrew Green
- Division of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospital NHS Trust, Nottingham, UK
| | - Francine E Garrett-Bakelman
- Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA
- Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Chris Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ari Melnick
- Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA
| | - Samuel A J R Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Oscar M Rueda
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Amos Tanay
- Department of Computer Science and Applied Mathematics, and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel.
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.
- Department of Oncology, University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Centre, Cambridge, UK.
- Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
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46
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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: 32] [Impact Index Per Article: 10.7] [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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47
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Krebs AR. Studying transcription factor function in the genome at molecular resolution. Trends Genet 2021; 37:798-806. [DOI: 10.1016/j.tig.2021.03.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 12/11/2022]
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Derrien J, Guérin-Charbonnel C, Gaborit V, Campion L, Devic M, Douillard E, Roi N, Avet-Loiseau H, Decaux O, Facon T, Mallm JP, Eils R, Munshi NC, Moreau P, Herrmann C, Magrangeas F, Minvielle S. The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability. Genome Med 2021; 13:127. [PMID: 34372935 PMCID: PMC8351364 DOI: 10.1186/s13073-021-00938-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 07/09/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Cancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. METHODS Here, we performed an analysis of 42 MM samples from 21 patients by using enhanced reduced representation bisulfite sequencing (eRRBS). We combined several metrics of epigenetic heterogeneity to analyze DNA methylation heterogeneity in MM patients. RESULTS We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High combinatorial entropy change is associated with poor outcomes in our pilot study and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. CONCLUSIONS We propose that disrupted DNA methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.
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Affiliation(s)
- Jennifer Derrien
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
| | - Catherine Guérin-Charbonnel
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Institut de Cancérologie de l'Ouest, Nantes-Saint Herblain, France
| | - Victor Gaborit
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- LS2N, CNRS, Université de Nantes, Nantes, France
| | - Loïc Campion
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Institut de Cancérologie de l'Ouest, Nantes-Saint Herblain, France
| | - Magali Devic
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Elise Douillard
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Nathalie Roi
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Hervé Avet-Loiseau
- Institut Universitaire du Cancer, CHU, Centre de Recherche en Cancérologie de Toulouse, INSERM 1037, Toulouse, France
| | | | | | - Jan-Philipp Mallm
- Research Group Genome Organization & Function, DKFZ, and BioQuant Heidelberg, Heidelberg, 69120, Germany
| | - Roland Eils
- Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg, 69120, Germany
- Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt- Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany
- Berlin Institute of Health (BIH), Center for Digital Health, Anna-Louisa-Karsch-Strasse 2, Berlin, 10178, Germany
| | - Nikhil C Munshi
- Dana-Farber Cancer Institute, Harvard Medical School, LeBow Institute for Myeloma Therapeutics and Jerome Lipper Center for Multiple Myeloma Research, Boston, MA, United States
| | - Philippe Moreau
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Carl Herrmann
- Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg, 69120, Germany
| | - Florence Magrangeas
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Stéphane Minvielle
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France.
- Centre Hospitalier Universitaire, Nantes, France.
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Morrison J, Koeman JM, Johnson BK, Foy KK, Beddows I, Zhou W, Chesla DW, Rossell LL, Siegwald EJ, Adams M, Shen H. Evaluation of whole-genome DNA methylation sequencing library preparation protocols. Epigenetics Chromatin 2021; 14:28. [PMID: 34147133 PMCID: PMC8214260 DOI: 10.1186/s13072-021-00401-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/27/2021] [Indexed: 12/18/2022] Open
Abstract
Background With rapidly dropping sequencing cost, the popularity of whole-genome DNA methylation sequencing has been on the rise. Multiple library preparation protocols currently exist. We have performed 22 whole-genome DNA methylation sequencing experiments on snap frozen human samples, and extensively benchmarked common library preparation protocols for whole-genome DNA methylation sequencing, including three traditional bisulfite-based protocols and a new enzyme-based protocol. In addition, different input DNA quantities were compared for two kits compatible with a reduced starting quantity. In addition, we also present bioinformatic analysis pipelines for sequencing data from each of these library types. Results An assortment of metrics were collected for each kit, including raw read statistics, library quality and uniformity metrics, cytosine retention, and CpG beta value consistency between technical replicates. Overall, the NEBNext Enzymatic Methyl-seq and Swift Accel-NGS Methyl-Seq kits performed quantitatively better than the other two protocols. In addition, the NEB and Swift kits performed well at low-input amounts, validating their utility in applications where DNA is the limiting factor. Results The NEBNext Enzymatic Methyl-seq kit appeared to be the best option for whole-genome DNA methylation sequencing of high-quality DNA, closely followed by the Swift kit, which potentially works better for degraded samples. Further, a general bioinformatic pipeline is applicable across the four protocols, with the exception of extra trimming needed for the Swift Biosciences’s Accel-NGS Methyl-Seq protocol to remove the Adaptase sequence. Supplementary Information The online version contains supplementary material available at 10.1186/s13072-021-00401-y.
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Affiliation(s)
- Jacob Morrison
- Department of Epigenetics, Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI, 49503, USA
| | - Julie M Koeman
- Genomics Core, Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI, 49503, USA
| | - Benjamin K Johnson
- Department of Epigenetics, Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI, 49503, USA
| | - Kelly K Foy
- Department of Epigenetics, Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI, 49503, USA
| | - Ian Beddows
- Department of Epigenetics, Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI, 49503, USA
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, 3501 Civic Center Boulevard, Philadelphia, PA, 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David W Chesla
- Spectrum Health Office of Research and Education, Spectrum Health System, 15 Michigan Street NE, Grand Rapids, MI, 49503, USA
| | - Larissa L Rossell
- Spectrum Health Office of Research and Education, Spectrum Health System, 15 Michigan Street NE, Grand Rapids, MI, 49503, USA
| | - Emily J Siegwald
- Spectrum Health Office of Research and Education, Spectrum Health System, 15 Michigan Street NE, Grand Rapids, MI, 49503, USA
| | - Marie Adams
- Genomics Core, Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI, 49503, USA.
| | - Hui Shen
- Department of Epigenetics, Van Andel Research Institute, 333 Bostwick Avenue NE, Grand Rapids, MI, 49503, USA.
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Pan H, Renaud L, Chaligne R, Bloehdorn J, Tausch E, Mertens D, Fink AM, Fischer K, Zhang C, Betel D, Gnirke A, Imielinski M, Moreaux J, Hallek M, Meissner A, Stilgenbauer S, Wu CJ, Elemento O, Landau DA. Discovery of Candidate DNA Methylation Cancer Driver Genes. Cancer Discov 2021; 11:2266-2281. [PMID: 33972312 DOI: 10.1158/2159-8290.cd-20-1334] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/25/2021] [Accepted: 04/15/2021] [Indexed: 02/07/2023]
Abstract
Epigenetic alterations, such as promoter hypermethylation, may drive cancer through tumor suppressor gene inactivation. However, we have limited ability to differentiate driver DNA methylation (DNAme) changes from passenger events. We developed DNAme driver inference-MethSig-accounting for the varying stochastic hypermethylation rate across the genome and between samples. We applied MethSig to bisulfite sequencing data of chronic lymphocytic leukemia (CLL), multiple myeloma, ductal carcinoma in situ, glioblastoma, and to methylation array data across 18 tumor types in TCGA. MethSig resulted in well-calibrated quantile-quantile plots and reproducible inference of likely DNAme drivers with increased sensitivity/specificity compared with benchmarked methods. CRISPR/Cas9 knockout of selected candidate CLL DNAme drivers provided a fitness advantage with and without therapeutic intervention. Notably, DNAme driver risk score was closely associated with adverse outcome in independent CLL cohorts. Collectively, MethSig represents a novel inference framework for DNAme driver discovery to chart the role of aberrant DNAme in cancer. SIGNIFICANCE: MethSig provides a novel statistical framework for the analysis of DNA methylation changes in cancer, to specifically identify candidate DNA methylation driver genes of cancer progression and relapse, empowering the discovery of epigenetic mechanisms that enhance cancer cell fitness.This article is highlighted in the In This Issue feature, p. 2113.
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Affiliation(s)
- Heng Pan
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York
| | - Loïc Renaud
- New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York.,Inserm, UMR-S 1172, Lille, France
| | - Ronan Chaligne
- New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | | | - Eugen Tausch
- Department of Internal Medicine III, Ulm University, Ulm, Germany
| | - Daniel Mertens
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anna Maria Fink
- German CLL Study Group, and Department I of Internal Medicine, and Center of Integrated Oncology ABCD, University of Cologne, Cologne, Germany
| | - Kirsten Fischer
- German CLL Study Group, and Department I of Internal Medicine, and Center of Integrated Oncology ABCD, University of Cologne, Cologne, Germany
| | - Chao Zhang
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Doron Betel
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Andreas Gnirke
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Marcin Imielinski
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Jérôme Moreaux
- IGH, CNRS, Univ Montpellier, France.,CHU Montpellier, Department of Biological Hematology, Montpellier, France.,UFR de Médecine, Univ Montpellier, Montpellier, France.,Institut Universitaire de France (IUF), France
| | - Michael Hallek
- German CLL Study Group, and Department I of Internal Medicine, and Center of Integrated Oncology ABCD, University of Cologne, Cologne, Germany
| | - Alexander Meissner
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Max Planck Institute for Molecular Genetics, Berlin, Germany
| | | | - Catherine J Wu
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Dan A Landau
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York. .,New York Genome Center, New York, New York.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, New York
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