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Zhu T, Tong H, Du Z, Beck S, Teschendorff AE. An improved epigenetic counter to track mitotic age in normal and precancerous tissues. Nat Commun 2024; 15:4211. [PMID: 38760334 PMCID: PMC11101651 DOI: 10.1038/s41467-024-48649-8] [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: 09/24/2023] [Accepted: 05/09/2024] [Indexed: 05/19/2024] Open
Abstract
The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC's mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.
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Affiliation(s)
- Tianyu Zhu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China
| | - Huige Tong
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China
| | - Zhaozhen Du
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China
| | - Stephan Beck
- Medical Genomics Group, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, UK
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China.
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2
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Luo Q, Dwaraka VB, Chen Q, Tong H, Zhu T, Seale K, Raffaele JM, Zheng SC, Mendez TL, Chen Y, Carreras N, Begum S, Mendez K, Voisin S, Eynon N, Lasky-Su JA, Smith R, Teschendorff AE. A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes. Genome Med 2023; 15:59. [PMID: 37525279 PMCID: PMC10388560 DOI: 10.1186/s13073-023-01211-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/10/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition. METHODS Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes. RESULTS Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity. CONCLUSIONS This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.
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Affiliation(s)
- Qi Luo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Varun B Dwaraka
- TruDiagnostics, 881 Corporate Dr., Lexington, KY, 40503, USA
| | - Qingwen Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Huige Tong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Tianyu Zhu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Kirsten Seale
- Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC, 3011, Australia
| | - Joseph M Raffaele
- PhysioAge LLC, 30 Central Park South / Suite 8A, New York, NY, 10019, USA
| | - Shijie C Zheng
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Tavis L Mendez
- TruDiagnostics, 881 Corporate Dr., Lexington, KY, 40503, USA
| | - Yulu Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | | | - Sofina Begum
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Kevin Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Sarah Voisin
- Institute for Health and Sport (iHeS), Victoria University, Footscray, VIC, 3011, Australia
| | - Nir Eynon
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
| | - Ryan Smith
- TruDiagnostics, 881 Corporate Dr., Lexington, KY, 40503, USA.
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
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Zhu T, Teschendorff AE. Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE. Methods Mol Biol 2023; 2629:23-42. [PMID: 36929072 DOI: 10.1007/978-1-0716-2986-4_3] [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: 03/18/2023]
Abstract
DNA methylation data generated from bulk tissue represents a mixture of many different cell types. Variation in the cell-type composition of tissues is thus a major confounder when inferring differential DNA methylation. Due to the high cost of single-cell methylome sequencing, computational methods that can dissect the cell-type heterogeneity of bulk DNA methylomes offer an efficient and cost-effective solution, especially in the context of large-scale EWAS. In this chapter, we present a step-by-step tutorial of Epigenetic cell-type deconvolution using Single-Cell Omic References (EpiSCORE), a reference-based method that leverages the high-resolution nature of single-cell RNA-Seq datasets to facilitate microdissection of bulk-tissue DNA methylomes.
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Affiliation(s)
- Tianyu Zhu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- UCL Cancer Institute, Paul O'Gorman Building, University College London, London, UK.
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Targeted Methylation Profiling of Single Laser-Capture Microdissected Post-Mortem Brain Cells by Adapted Limiting Dilution Bisulfite Pyrosequencing (LDBSP). Int J Mol Sci 2022; 23:ijms232415571. [PMID: 36555213 PMCID: PMC9779089 DOI: 10.3390/ijms232415571] [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: 10/28/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 12/13/2022] Open
Abstract
A reoccurring issue in neuroepigenomic studies, especially in the context of neurodegenerative disease, is the use of (heterogeneous) bulk tissue, which generates noise during epigenetic profiling. A workable solution to this issue is to quantify epigenetic patterns in individually isolated neuronal cells using laser capture microdissection (LCM). For this purpose, we established a novel approach for targeted DNA methylation profiling of individual genes that relies on a combination of LCM and limiting dilution bisulfite pyrosequencing (LDBSP). Using this approach, we determined cytosine-phosphate-guanine (CpG) methylation rates of single alleles derived from 50 neurons that were isolated from unfixed post-mortem brain tissue. In the present manuscript, we describe the general workflow and, as a showcase, demonstrate how targeted methylation analysis of various genes, in this case, RHBDF2, OXT, TNXB, DNAJB13, PGLYRP1, C3, and LMX1B, can be performed simultaneously. By doing so, we describe an adapted data analysis pipeline for LDBSP, allowing one to include and correct CpG methylation rates derived from multi-allele reactions. In addition, we show that the efficiency of LDBSP on DNA derived from LCM neurons is similar to the efficiency obtained in previously published studies using this technique on other cell types. Overall, the method described here provides the user with a more accurate estimation of the DNA methylation status of each target gene in the analyzed cell pools, thereby adding further validity to this approach.
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Manu DM, Mwinyi J, Schiöth HB. Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health. Int J Mol Sci 2022; 23:ijms23105856. [PMID: 35628666 PMCID: PMC9147258 DOI: 10.3390/ijms23105856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 12/10/2022] Open
Abstract
The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene–environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.
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Zhu T, Liu J, Beck S, Pan S, Capper D, Lechner M, Thirlwell C, Breeze CE, Teschendorff AE. A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nat Methods 2022; 19:296-306. [PMID: 35277705 PMCID: PMC8916958 DOI: 10.1038/s41592-022-01412-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 01/28/2022] [Indexed: 02/07/2023]
Abstract
Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data. This resource presents an in silico generated DNA methylation atlas that can be used for cell-type deconvolution of human tissues.
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7
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Qi L, Teschendorff AE. Cell-type heterogeneity: Why we should adjust for it in epigenome and biomarker studies. Clin Epigenetics 2022; 14:31. [PMID: 35227298 PMCID: PMC8887190 DOI: 10.1186/s13148-022-01253-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/21/2022] [Indexed: 12/18/2022] Open
Abstract
Most studies aiming to identify epigenetic biomarkers do so from complex tissues that are composed of many different cell-types. By definition, these cell-types vary substantially in terms of their epigenetic profiles. This cell-type specific variation among healthy cells is completely independent of the variation associated with disease, yet it dominates the epigenetic variability landscape. While cell-type composition of tissues can change in disease and this may provide accurate and reproducible biomarkers, not adjusting for the underlying cell-type heterogeneity may seriously limit the sensitivity and precision to detect disease-relevant biomarkers or hamper our understanding of such biomarkers. Given that computational and experimental tools for tackling cell-type heterogeneity are available, we here stress that future epigenetic biomarker studies should aim to provide estimates of underlying cell-type fractions for all samples in the study, and to identify biomarkers before and after adjustment for cell-type heterogeneity, in order to obtain a more complete and unbiased picture of the biomarker-landscape. This is critical, not only to improve reproducibility and for the eventual clinical application of such biomarkers, but importantly, to also improve our molecular understanding of disease itself.
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Affiliation(s)
- Luo Qi
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. .,UCL Cancer Institute, University College London, London, WC1E 8BT, UK.
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8
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Maity AK, Stone TC, Ward V, Webster AP, Yang Z, Hogan A, McBain H, Duku M, Ho KMA, Wolfson P, Graham DG, Beck S, Teschendorff AE, Lovat LB. Novel epigenetic network biomarkers for early detection of esophageal cancer. Clin Epigenetics 2022; 14:23. [PMID: 35164838 PMCID: PMC8845366 DOI: 10.1186/s13148-022-01243-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 02/04/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Early detection of esophageal cancer is critical to improve survival. Whilst studies have identified biomarkers, their interpretation and validity is often confounded by cell-type heterogeneity. RESULTS Here we applied systems-epigenomic and cell-type deconvolution algorithms to a discovery set encompassing RNA-Seq and DNA methylation data from esophageal adenocarcinoma (EAC) patients and matched normal-adjacent tissue, in order to identify robust biomarkers, free from the confounding effect posed by cell-type heterogeneity. We identify 12 gene-modules that are epigenetically deregulated in EAC, and are able to validate all 12 modules in 4 independent EAC cohorts. We demonstrate that the epigenetic deregulation is present in the epithelial compartment of EAC-tissue. Using single-cell RNA-Seq data we show that one of these modules, a proto-cadherin module centered around CTNND2, is inactivated in Barrett's Esophagus, a precursor lesion to EAC. By measuring DNA methylation in saliva from EAC cases and controls, we identify a chemokine module centered around CCL20, whose methylation patterns in saliva correlate with EAC status. CONCLUSIONS Given our observations that a CCL20 chemokine network is overactivated in EAC tissue and saliva from EAC patients, and that in independent studies CCL20 has been found to be overactivated in EAC tissue infected with the bacterium F. nucleatum, a bacterium that normally inhabits the oral cavity, our results highlight the possibility of using DNAm measurements in saliva as a proxy for changes occurring in the esophageal epithelium. Both the CTNND2/CCL20 modules represent novel promising network biomarkers for EAC that merit further investigation.
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Affiliation(s)
- Alok K Maity
- CAS Key Lab of Computational Biology, Shanghai Institute for Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Timothy C Stone
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Vanessa Ward
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Amy P Webster
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Zhen Yang
- Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Aine Hogan
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Hazel McBain
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Margaraet Duku
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Kai Man Alexander Ho
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Paul Wolfson
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - David G Graham
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK.,Division of GI Services, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | | | - Stephan Beck
- UCL Cancer Institute, University College London, Gower Street, London, WC1E 6BT, UK
| | - Andrew E Teschendorff
- CAS Key Lab of Computational Biology, Shanghai Institute for Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
| | - Laurence B Lovat
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK. .,Division of GI Services, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK.
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Shanthikumar S, Neeland MR, Saffery R, Ranganathan SC, Oshlack A, Maksimovic J. DNA Methylation Profiles of Purified Cell Types in Bronchoalveolar Lavage: Applications for Mixed Cell Paediatric Pulmonary Studies. Front Immunol 2021; 12:788705. [PMID: 35003108 PMCID: PMC8727592 DOI: 10.3389/fimmu.2021.788705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 12/03/2021] [Indexed: 01/15/2023] Open
Abstract
In epigenome-wide association studies analysing DNA methylation from samples containing multiple cell types, it is essential to adjust the analysis for cell type composition. One well established strategy for achieving this is reference-based cell type deconvolution, which relies on knowledge of the DNA methylation profiles of purified constituent cell types. These are then used to estimate the cell type proportions of each sample, which can then be incorporated to adjust the association analysis. Bronchoalveolar lavage is commonly used to sample the lung in clinical practice and contains a mixture of different cell types that can vary in proportion across samples, affecting the overall methylation profile. A current barrier to the use of bronchoalveolar lavage in DNA methylation-based research is the lack of reference DNA methylation profiles for each of the constituent cell types, thus making reference-based cell composition estimation difficult. Herein, we use bronchoalveolar lavage samples collected from children with cystic fibrosis to define DNA methylation profiles for the four most common and clinically relevant cell types: alveolar macrophages, granulocytes, lymphocytes and alveolar epithelial cells. We then demonstrate the use of these methylation profiles in conjunction with an established reference-based methylation deconvolution method to estimate the cell type composition of two different tissue types; a publicly available dataset derived from artificial blood-based cell mixtures and further bronchoalveolar lavage samples. The reference DNA methylation profiles developed in this work can be used for future reference-based cell type composition estimation of bronchoalveolar lavage. This will facilitate the use of this tissue in studies examining the role of DNA methylation in lung health and disease.
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Affiliation(s)
- Shivanthan Shanthikumar
- Respiratory and Sleep Medicine, Royal Children’s Hospital, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
- *Correspondence: Shivanthan Shanthikumar,
| | - Melanie R. Neeland
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Molecular Immunity, Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Richard Saffery
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Molecular Immunity, Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Sarath C. Ranganathan
- Respiratory and Sleep Medicine, Royal Children’s Hospital, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Alicia Oshlack
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
- School of BioScience, University of Melbourne, Parkville, VIC, Australia
| | - Jovana Maksimovic
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
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Lu T, Cardenas A, Perron P, Hivert MF, Bouchard L, Greenwood CMT. Detecting cord blood cell type-specific epigenetic associations with gestational diabetes mellitus and early childhood growth. Clin Epigenetics 2021; 13:131. [PMID: 34174944 PMCID: PMC8236204 DOI: 10.1186/s13148-021-01114-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 06/14/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. However, an important limitation of conventional EWAS is that profiles of epigenetic variability are often obtained in samples of mixed cell types. Here, we aim to assess whether changes in cord blood DNA methylation (DNAm) associated with gestational diabetes mellitus (GDM) exposure and early childhood growth markers occur in a cell type-specific manner. RESULTS We analyzed 275 cord blood samples collected at delivery from a prospective pre-birth cohort with genome-wide DNAm profiled by the Illumina MethylationEPIC array. We estimated proportions of seven common cell types in each sample using a cord blood-specific DNAm reference panel. Leveraging a recently developed approach named CellDMC, we performed cell type-specific EWAS to identify CpG loci significantly associated with GDM, or 3-year-old body mass index (BMI) z-score. A total of 1410 CpG loci displayed significant cell type-specific differences in methylation level between 23 GDM cases and 252 controls with a false discovery rate < 0.05. Gene Ontology enrichment analysis indicated that LDL transportation emerged from CpG specifically identified from B-cells DNAm analyses and the mitogen-activated protein kinase pathway emerged from CpG specifically identified from natural killer cells DNAm analyses. In addition, we identified four and six loci associated with 3-year-old BMI z-score that were specific to CD8+ T-cells and monocytes, respectively. By performing genome-wide permutation tests, we validated that most of our detected signals had low false positive rates. CONCLUSION Compared to conventional EWAS adjusting for the effects of cell type heterogeneity, the proposed approach based on cell type-specific EWAS could provide additional biologically meaningful associations between CpG methylation, prenatal maternal GDM or 3-year-old BMI. With careful validation, these findings may provide new insights into the pathogenesis, programming, and consequences of related childhood metabolic dysregulation. Therefore, we propose that cell type-specific analyses are worth cautious explorations.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de La Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, QC, Canada
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health and Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Patrice Perron
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalier, Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Marie-France Hivert
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Luigi Bouchard
- Centre de Recherche du Centre Hospitalier, Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
- Department of Medical Biology, Centre Intégré Universitaire de Santé et de Services Sociaux Saguenay-Lac-Saint-Jean - Hôpital Universitaire de Chicoutimi, Saguenay, QC, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de La Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
- Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada.
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Fong J, Gardner JR, Andrews JM, Cashen AF, Payton JE, Weinberger KQ, Edwards JR. Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM. Nucleic Acids Res 2021; 49:e93. [PMID: 34157105 PMCID: PMC8450090 DOI: 10.1093/nar/gkab516] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/04/2021] [Indexed: 12/26/2022] Open
Abstract
Epigenetic changes, such as aberrant DNA methylation, contribute to cancer clonal expansion and disease progression. However, identifying subpopulation-level changes in a heterogeneous sample remains challenging. Thus, we have developed a computational approach, DXM, to deconvolve the methylation profiles of major allelic subpopulations from the bisulfite sequencing data of a heterogeneous sample. DXM does not require prior knowledge of the number of subpopulations or types of cells to expect. We benchmark DXM's performance and demonstrate improvement over existing methods. We further experimentally validate DXM predicted allelic subpopulation-methylation profiles in four Diffuse Large B-Cell Lymphomas (DLBCLs). Lastly, as proof-of-concept, we apply DXM to a cohort of 31 DLBCLs and relate allelic subpopulation methylation profiles to relapse. We thus demonstrate that DXM can robustly find allelic subpopulation methylation profiles that may contribute to disease progression using bisulfite sequencing data of any heterogeneous sample.
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Affiliation(s)
- Jerry Fong
- Center for Pharmacogenomics, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacob R Gardner
- Center for Data Science for Improved Decision Making, Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Jared M Andrews
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Amanda F Cashen
- Oncology Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacqueline E Payton
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kilian Q Weinberger
- Center for Data Science for Improved Decision Making, Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - John R Edwards
- Center for Pharmacogenomics, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
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12
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Campbell KA, Colacino JA, Park SK, Bakulski KM. Cell Types in Environmental Epigenetic Studies: Biological and Epidemiological Frameworks. Curr Environ Health Rep 2021; 7:185-197. [PMID: 32794033 DOI: 10.1007/s40572-020-00287-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE OF REVIEW This article introduces the roles of perinatal DNA methylation in human health and disease, highlights the challenges of tissue and cellular heterogeneity to studying DNA methylation, summarizes approaches to overcome these challenges, and offers recommendations in conducting research in environmental epigenetics. RECENT FINDINGS Epigenetic modifications are essential for human development and are labile to environmental influences, especially during gestation. Epigenetic dysregulation is also a hallmark of multiple diseases. Environmental epigenetic studies routinely measure DNA methylation in readily available tissues. However, tissues and cell types exhibit specific epigenetic patterning and heterogeneity between samples complicates epigenetic studies. Failure to account for cell-type heterogeneity limits identification of biological mechanisms and biases study results. Tissue-level epigenetic measures represent a convolution of epigenetic signals from individual cell types. Tissue-specific epigenetics is an evolving field and the use of disease-affected target, surrogate, or multiple tissues has inherent trade-offs and affects inference. Likewise, experimental and bioinformatic approaches to accommodate cell-type heterogeneity have varying assumptions and inherent trade-offs that affect inference. The relationships between exposure, disease, tissue-level DNA methylation, cell type-specific DNA methylation, and cell-type heterogeneity must be carefully considered in study design and analysis. Causal diagrams can inform study design and analytic strategies. Properly addressing cell-type heterogeneity limits sources of potential bias, avoids misinterpretation of study results, and allows investigators to distinguish shifts in cell-type proportions from direct changes to cellular epigenetic programming, both of which provide insights into environmental disease etiology and aid development of novel methods for prevention and treatment.
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Affiliation(s)
- Kyle A Campbell
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA.
| | - Justin A Colacino
- Department of Environmental Health Sciences, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sung Kyun Park
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA.,Department of Environmental Health Sciences, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kelly M Bakulski
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
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13
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You C, Wu S, Zheng SC, Zhu T, Jing H, Flagg K, Wang G, Jin L, Wang S, Teschendorff AE. A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes. Nat Commun 2020; 11:4779. [PMID: 32963246 PMCID: PMC7508850 DOI: 10.1038/s41467-020-18618-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
Highly reproducible smoking-associated DNA methylation changes in whole blood have been reported by many Epigenome-Wide-Association Studies (EWAS). These epigenetic alterations could have important implications for understanding and predicting the risk of smoking-related diseases. To this end, it is important to establish if these DNA methylation changes happen in all blood cell subtypes or if they are cell-type specific. Here, we apply a cell-type deconvolution algorithm to identify cell-type specific DNA methylation signals in seven large EWAS. We find that most of the highly reproducible smoking-associated hypomethylation signatures are more prominent in the myeloid lineage. A meta-analysis further identifies a myeloid-specific smoking-associated hypermethylation signature enriched for DNase Hypersensitive Sites in acute myeloid leukemia. These results may guide the design of future smoking EWAS and have important implications for our understanding of how smoking affects immune-cell subtypes and how this may influence the risk of smoking related diseases. Smoking-associated DNA methylation changes in whole blood have been reported by many EWAS. Here, the authors use a cell-type deconvolution algorithm to identify cell-type specific DNA methylation signals in seven EWAS, identifying lineage-specific smoking-associated DNA methylation changes.
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Affiliation(s)
- Chenglong You
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China
| | - Sijie Wu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China.,Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, China.,State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Shijie C Zheng
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China.,Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Tianyu Zhu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China
| | - Han Jing
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China
| | - Ken Flagg
- Guangzhou Regenerative Medicine Guangdong Laboratory, Guangzhou, China
| | - Guangyu Wang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Li Jin
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China.,Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, China.,State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China. .,UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
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14
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Teschendorff AE. A comparison of epigenetic mitotic-like clocks for cancer risk prediction. Genome Med 2020; 12:56. [PMID: 32580750 PMCID: PMC7315560 DOI: 10.1186/s13073-020-00752-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 06/10/2020] [Indexed: 12/19/2022] Open
Abstract
Background DNA methylation changes that accrue in the stem cell pool of an adult tissue in line with the cumulative number of cell divisions may contribute to the observed variation in cancer risk among tissues and individuals. Thus, the construction of epigenetic “mitotic” clocks that can measure the lifetime number of stem cell divisions is of paramount interest. Methods Building upon a dynamic model of DNA methylation gain in unmethylated CpG-rich regions, we here derive a novel mitotic clock (“epiTOC2”) that can directly estimate the cumulative number of stem cell divisions in a tissue. We compare epiTOC2 to a different mitotic model, based on hypomethylation at solo-WCGW sites (“HypoClock”), in terms of their ability to measure mitotic age of normal adult tissues and predict cancer risk. Results Using epiTOC2, we estimate the intrinsic stem cell division rate for different normal tissue types, demonstrating excellent agreement (Pearson correlation = 0.92, R2 = 0.85, P = 3e−6) with those derived from experiment. In contrast, HypoClock’s estimates do not (Pearson correlation = 0.30, R2 = 0.09, P = 0.29). We validate these results in independent datasets profiling normal adult tissue types. While both epiTOC2 and HypoClock correctly predict an increased mitotic rate in cancer, epiTOC2 is more robust and significantly better at discriminating preneoplastic lesions characterized by chronic inflammation, a major driver of tissue turnover and cancer risk. Our data suggest that DNA methylation loss at solo-WCGWs is significant only when cells are under high replicative stress and that epiTOC2 is a better mitotic age and cancer risk prediction model for normal adult tissues. Conclusions These results have profound implications for our understanding of epigenetic clocks and for developing cancer risk prediction or early detection assays. We propose that measurement of DNAm at the 163 epiTOC2 CpGs in adult pre-neoplastic lesions, and potentially in serum cell-free DNA, could provide the basis for building feasible pre-diagnostic or cancer risk assays. epiTOC2 is freely available from 10.5281/zenodo.2632938
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for 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, China. .,UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
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15
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Allum F, Grundberg E. Capturing functional epigenomes for insight into metabolic diseases. Mol Metab 2020; 38:100936. [PMID: 32199819 PMCID: PMC7300388 DOI: 10.1016/j.molmet.2019.12.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/23/2019] [Accepted: 12/30/2019] [Indexed: 12/12/2022] Open
Abstract
Background Metabolic diseases such as obesity are known to be driven by both environmental and genetic factors. Although genome-wide association studies of common variants and their impact on complex traits have provided some biological insight into disease etiology, identified genetic variants have been found to contribute only a small proportion to disease heritability, and to map mainly to non-coding regions of the genome. To link variants to function, association studies of cellular traits, such as epigenetic marks, in disease-relevant tissues are commonly applied. Scope of the review We review large-scale efforts to generate genome-wide maps of coordinated epigenetic marks and their utility in complex disease dissection with a focus on DNA methylation. We contrast DNA methylation profiling methods and discuss the advantages of using targeted methods for single-base resolution assessments of methylation levels across tissue-specific regulatory regions to deepen our understanding of contributing factors leading to complex diseases. Major conclusions Large-scale assessments of DNA methylation patterns in metabolic disease-linked study cohorts have provided insight into the impact of variable epigenetic variants in disease etiology. In-depth profiling of epigenetic marks at regulatory regions, particularly at tissue-specific elements, will be key to dissect the genetic and environmental components contributing to metabolic disease onset and progression. Changes in epigenetic marks have been linked to metabolic disease phenotypes. Disease-linked sites of variable DNA methylation status are enriched in distal regulatory regions of disease-linked tissues. Distal regulatory elements remain underrepresented in popular array-based methylation profiling technologies. Novel next-generation capture methods provide cost-effective solutions to assess the impact of DNA methylation in metabolic diseases specifically at regulatory elements. Improvements in methodologies to account for tissue heterogeneity and causality will be crucial in future epigenome-wide association studies.
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Affiliation(s)
- Fiona Allum
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0C7, Canada; McGill University and Genome Quebec Innovation Centre, Montréal, Québec, H3A 0G1, Canada
| | - Elin Grundberg
- Children's Mercy Kansas City, Kansas City, MO, 64108, United States.
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16
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Variability in DNA methylation at the serotonin transporter gene promoter: epigenetic mechanism or cell-type artifact? Mol Psychiatry 2020; 25:1906-1909. [PMID: 30082839 PMCID: PMC7473835 DOI: 10.1038/s41380-018-0121-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 04/10/2018] [Accepted: 05/25/2018] [Indexed: 11/09/2022]
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17
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Hicks SC, Irizarry RA. methylCC: technology-independent estimation of cell type composition using differentially methylated regions. Genome Biol 2019; 20:261. [PMID: 31783894 PMCID: PMC6883691 DOI: 10.1186/s13059-019-1827-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 09/19/2019] [Indexed: 01/01/2023] Open
Abstract
A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC.
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Affiliation(s)
- Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St,, Baltimore, USA
| | - Rafael A Irizarry
- Department Data Sciences, Dana-Farber Cancer Institute, 450 Brookline Ave,, Boston, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, USA.
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18
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Li Z, Wu H. TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis. Genome Biol 2019; 20:190. [PMID: 31484546 PMCID: PMC6727351 DOI: 10.1186/s13059-019-1778-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/30/2019] [Indexed: 02/07/2023] Open
Abstract
In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST .
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, 30322, GA, USA
| | - Hao Wu
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, 30322, GA, USA.
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19
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Luo X, Yang C, Wei Y. Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies. Nat Commun 2019; 10:3113. [PMID: 31308366 PMCID: PMC6629651 DOI: 10.1038/s41467-019-10864-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 06/06/2019] [Indexed: 02/06/2023] Open
Abstract
In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. Current approaches to the association detection claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not at aggregate level and can suffer from low statistical power. Here, we propose a statistical method, HIgh REsolution (HIRE), which not only improves the power of association detection at aggregate level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. Cellular heterogeneity is one of the major confounding factors in EWAS studies. Here the authors present a statistical method, HIgh REsolution (HIRE), which enables the detection of risk-CpG sites for individual cell types.
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Affiliation(s)
- Xiangyu Luo
- Institute of Statistics and Big Data, Renmin University of China, 100872, Beijing, China.,Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
| | - Yingying Wei
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China.
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20
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Thompson M, Chen ZJ, Rahmani E, Halperin E. CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets. Genome Biol 2019; 20:138. [PMID: 31300005 PMCID: PMC6624895 DOI: 10.1186/s13059-019-1743-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 06/21/2019] [Indexed: 12/11/2022] Open
Abstract
Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED.
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Affiliation(s)
- Mike Thompson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Johnson Chen
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Elior Rahmani
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Biomathematics, University of California Los Angeles, Los Angeles, CA, USA.
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21
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Ladd-Acosta C, Feinberg JI, Brown SC, Lurmann FW, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Feinberg AP, Fallin MD, Volk HE. Epigenetic marks of prenatal air pollution exposure found in multiple tissues relevant for child health. ENVIRONMENT INTERNATIONAL 2019; 126:363-376. [PMID: 30826615 PMCID: PMC6446941 DOI: 10.1016/j.envint.2019.02.028] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/05/2019] [Accepted: 02/10/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND Prenatal air pollution exposure has been linked to many adverse health conditions in the offspring. However, little is known about the mechanisms underlying these associations. Epigenetics may be one plausible biologic link. Here, we sought to identify site-specific and global DNA methylation (DNAm) changes, in developmentally relevant tissues, associated with prenatal exposure to nitrogen dioxide (NO2) and ozone (O3). Additionally, we assessed whether sex-specific changes in methylation exist and whether DNAm changes are consistently observed across tissues. METHODS Genome-scale DNAm measurements were obtained using the Infinium HumanMethylation450k platform for 133 placenta and 175 cord blood specimens from Early Autism Risk Longitudinal Investigation (EARLI) neonates. Ambient NO2 and O3 exposure levels were based on prenatal address locations of EARLI mothers and the Environmental Protection Agency's AirNOW monitoring network using inverse distance weighting. We computed sample-level aggregate methylation measures for each of 5 types of genomic regions including genome-wide, open sea, shelf, shore, and island regions. Linear regression was performed for each genomic region; per-sample aggregate methylation measures were modeled as a function of quantitative exposure level with covariate adjustment. In addition, bumphunting was performed to identify differentially methylated regions (DMRs) associated with prenatal O3 and NO2 exposures in each tissue and by sex, with adjustment for technical and biological sources of variation. RESULTS We identified global and locus-specific changes in DNA methylation related to prenatal exposure to NO2 and O3 in 2 developmentally relevant tissues. Neonates with increased prenatal O3 exposure had lower aggregate levels of DNAm at CpGs located in open sea and shelf regions of the genome. We identified 6 DMRs associated with prenatal NO2 exposure, including 3 sex-specific. An additional 3 sex-specific DMRs were associated with prenatal O3 exposure levels. DMRs initially detected in cord blood samples (n = 4) showed consistent exposure-related changes in DNAm in placenta. However, the DMRs initially detected in placenta (n = 5) did not show DNAm differences in cord blood and, thus, they appear to be tissue-specific. CONCLUSIONS We observed global, locus, and sex-specific methylation changes associated with prenatal NO2 and O3 exposures. Our findings support DNAm is a biologic target of prenatal air pollutant exposures and highlight epigenetic involvement in sex-specific differential susceptibility to environmental exposure effects in 2 developmentally relevant tissues.
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Affiliation(s)
- Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Jason I Feinberg
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shannon C Brown
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Lisa A Croen
- Autism Research Program, Division of Research, Kaiser Permanente, Oakland, CA, USA
| | - Irva Hertz-Picciotto
- Department of Public Health Sciences, MIND (Medical Investigations of Neurodevelopmental Disorders) Institute, University of California, Davis, CA, USA
| | - Craig J Newschaffer
- A.J. Drexel Autism Institute and Department of Epidemiology and Biostatistics, Drexel University School of Public Health, Philadelphia, PA, USA
| | - Andrew P Feinberg
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - M Daniele Fallin
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather E Volk
- Wendy Klag Center for Autism and Developmental Disabilities, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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22
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Chen Y, Armstrong DA, Salas LA, Hazlett HF, Nymon AB, Dessaint JA, Aridgides DS, Mellinger DL, Liu X, Christensen BC, Ashare A. Genome-wide DNA methylation profiling shows a distinct epigenetic signature associated with lung macrophages in cystic fibrosis. Clin Epigenetics 2018; 10:152. [PMID: 30526669 PMCID: PMC6288922 DOI: 10.1186/s13148-018-0580-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 11/06/2018] [Indexed: 11/10/2022] Open
Abstract
Background Lung macrophages are major participants in the pulmonary innate immune response. In the cystic fibrosis (CF) lung, the inability of lung macrophages to successfully regulate the exaggerated inflammatory response suggests dysfunctional innate immune cell function. In this study, we aim to gain insight into innate immune cell dysfunction in CF by investigating alterations in DNA methylation in bronchoalveolar lavage (BAL) cells, composed primarily of lung macrophages of CF subjects compared with healthy controls. All analyses were performed using primary alveolar macrophages from human subjects collected via bronchoalveolar lavage. Epigenome-wide DNA methylation was examined via Illumina MethylationEPIC (850 K) array. Targeted next-generation bisulfite sequencing was used to validate selected differentially methylated CpGs. Methylation-based sample classification was performed using the recursively partitioned mixture model (RPMM) and was tested against sample case-control status. Differentially methylated loci were identified by fitting linear models with adjustment of age, sex, estimated cell type proportions, and repeat measurement. Results RPMM class membership was significantly associated with the CF disease status (P = 0.026). One hundred nine CpG loci were differentially methylated in CF BAL cells (all FDR ≤ 0.1). The majority of differentially methylated loci in CF were hypo-methylated and found within non-promoter CpG islands as well as in putative enhancer regions and DNase hyper-sensitive regions. Conclusions These results support a hypothesis that epigenetic changes, specifically DNA methylation at a multitude of gene loci in lung macrophages, may participate, at least in part, in driving dysfunctional innate immune cells in the CF lung. Electronic supplementary material The online version of this article (10.1186/s13148-018-0580-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Youdinghuan Chen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - David A Armstrong
- Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Haley F Hazlett
- Program in Experimental and Molecular Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Amanda B Nymon
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - John A Dessaint
- Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Daniel S Aridgides
- Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Diane L Mellinger
- Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Xiaoying Liu
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.,Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Alix Ashare
- Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.,Program in Experimental and Molecular Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.,Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
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Identification of differentially methylated cell types in epigenome-wide association studies. Nat Methods 2018; 15:1059-1066. [PMID: 30504870 PMCID: PMC6277016 DOI: 10.1038/s41592-018-0213-x] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 10/09/2018] [Indexed: 12/22/2022]
Abstract
An outstanding challenge of epigenome-wide association studies (EWASs) performed in complex tissues is the identification of the specific cell type(s) responsible for the observed differential DNA methylation. Here we present a statistical algorithm called CellDMC ( https://github.com/sjczheng/EpiDISH ), which can identify differentially methylated positions and the specific cell type(s) driving the differential methylation. We validated CellDMC on in silico mixtures of DNA methylation data generated with different technologies, as well as on real mixtures from epigenome-wide association and cancer epigenome studies. CellDMC achieved over 90% sensitivity and specificity in scenarios where current state-of-the-art methods did not identify differential methylation. By applying CellDMC to an EWAS performed in buccal swabs, we identified smoking-associated differentially methylated positions occurring in the epithelial compartment, which we validated in smoking-related lung cancer. CellDMC may be useful in the identification of causal DNA-methylation alterations in disease.
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Brunst KJ, Tignor N, Just A, Liu Z, Lin X, Hacker MR, Bosquet Enlow M, Wright RO, Wang P, Baccarelli AA, Wright RJ. Cumulative lifetime maternal stress and epigenome-wide placental DNA methylation in the PRISM cohort. Epigenetics 2018; 13:665-681. [PMID: 30001177 DOI: 10.1080/15592294.2018.1497387] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Evolving evidence links maternal stress exposure to changes in placental DNA methylation of specific genes regulating placental function that may have implications for the programming of a host of chronic disorders. Few studies have implemented an epigenome-wide approach. Using the Infinium HumanMethylation450 BeadChip (450K), we investigated epigenome-wide placental DNA methylation in relation to maternal experiences of traumatic and non-traumatic stressors over her lifetime assessed using the Life Stressor Checklist-Revised (LSC-R) survey (n = 207). We found differential DNA methylation at epigenome-wide statistical significance (FDR = 0.05) for 112 CpGs. Additionally, we observed three clusters that exhibited differential methylation in response to high maternal lifetime stress. Enrichment analyses, conducted at an FDR = 0.20, revealed lysine degradation to be the most significant pathway associated with maternal lifetimes stress exposure. Targeted enrichment analyses of the three largest clusters of probes, identified using the gap statistic, were enriched for genes associated with endocytosis (i.e., SMAP1, ANKFY1), tight junctions (i.e., EPB41L4B), and metabolic pathways (i.e., INPP5E, EEF1B2). These pathways, also identified in the top 10 KEGG pathways associated with maternal lifetime stress exposure, play important roles in multiple physiological functions necessary for proper fetal development. Further, two genes were identified to exhibit multiple probes associated with maternal lifetime stress (i.e., ANKFY1, TM6SF1). The methylation status of the probes belonging to each cluster and/or genes exhibiting multiple hits, may play a role in the pathogenesis of adverse health outcomes in children born to mothers with increased lifetime stress exposure.
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Affiliation(s)
- Kelly J Brunst
- a Department of Environmental Health , University of Cincinnati College of Medicine , Cincinnati , OH , USA
| | - Nicole Tignor
- b Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences , Icahn School of Medicine at Mount Sinai One Gustave L. Levy Place , New York , NY , USA
| | - Allan Just
- c Department of Environmental Medicine and Public Health , Icahn School of Medicine at Mount Sinai , New York , NY , USA
| | - Zhonghua Liu
- d Department of Biostatistics , Harvard T.H. Chan School of Public Health , Boston , MA , USA
| | - Xihong Lin
- d Department of Biostatistics , Harvard T.H. Chan School of Public Health , Boston , MA , USA
| | - Michele R Hacker
- e Department of Obstetrics and Gynecology , Beth Israel Deaconess Medical Center , Boston , MA , USA.,f Department of Obstetrics , Gynecology and Reproductive Biology, Harvard Medical School , Boston , MA , USA
| | - Michelle Bosquet Enlow
- g Department of Psychiatry, Program for Behavioral Science, Boston Children's Hospital and Department of Psychiatry , Harvard Medical School , Boston , MA , USA
| | - Robert O Wright
- c Department of Environmental Medicine and Public Health , Icahn School of Medicine at Mount Sinai , New York , NY , USA
| | - Pei Wang
- b Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences , Icahn School of Medicine at Mount Sinai One Gustave L. Levy Place , New York , NY , USA
| | - Andrea A Baccarelli
- h Department of Environmental Health Sciences , Mailman School of Public Health, Columbia University , New York , NY , USA
| | - Rosalind J Wright
- c Department of Environmental Medicine and Public Health , Icahn School of Medicine at Mount Sinai , New York , NY , USA.,i Department of Pediatrics , Kravis Children's Hospital, Icahn School of Medicine at Mount Sinai , New York , NY , USA
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25
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DNA methylation analysis on purified neurons and glia dissects age and Alzheimer's disease-specific changes in the human cortex. Epigenetics Chromatin 2018; 11:41. [PMID: 30045751 PMCID: PMC6058387 DOI: 10.1186/s13072-018-0211-3] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 07/17/2018] [Indexed: 12/30/2022] Open
Abstract
Background Epigenome-wide association studies (EWAS) based on human brain samples allow a deep and direct understanding of epigenetic dysregulation in Alzheimer’s disease (AD). However, strong variation of cell-type proportions across brain tissue samples represents a significant source of data noise. Here, we report the first EWAS based on sorted neuronal and non-neuronal (mostly glia) nuclei from postmortem human brain tissues. Results We show that cell sorting strongly enhances the robust detection of disease-related DNA methylation changes even in a relatively small cohort. We identify numerous genes with cell-type-specific methylation signatures and document differential methylation dynamics associated with aging specifically in neurons such as CLU, SYNJ2 and NCOR2 or in glia RAI1,CXXC5 and INPP5A. Further, we found neuron or glia-specific associations with AD Braak stage progression at genes such as MCF2L, ANK1, MAP2, LRRC8B, STK32C and S100B. A comparison of our study with previous tissue-based EWAS validates multiple AD-associated DNA methylation signals and additionally specifies their origin to neuron, e.g., HOXA3 or glia (ANK1). In a meta-analysis, we reveal two novel previously unrecognized methylation changes at the key AD risk genes APP and ADAM17. Conclusions Our data highlight the complex interplay between disease, age and cell-type-specific methylation changes in AD risk genes thus offering new perspectives for the validation and interpretation of large EWAS results. Electronic supplementary material The online version of this article (10.1186/s13072-018-0211-3) contains supplementary material, which is available to authorized users.
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26
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Zheng SC, Webster AP, Dong D, Feber A, Graham DG, Sullivan R, Jevons S, Lovat LB, Beck S, Widschwendter M, Teschendorff AE. A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix. Epigenomics 2018; 10:925-940. [DOI: 10.2217/epi-2018-0037] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Aim: An outstanding challenge in epigenome studies is the estimation of cell-type proportions in complex epithelial tissues. Materials & methods: Here, we construct and validate a DNA methylation reference and algorithm for complex tissues that contain epithelial, immune and nonimmune stromal cells. Results: Using this reference, we show that easily accessible tissues such as saliva, buccal and cervix exhibit substantial variation in immune cell (IC) contamination. We further validate our reference in the context of oral cancer, where it correctly predicts an increased IC infiltration in cancer but suppressed in patients with highest smoking exposure. Finally, our method can improve the specificity of differentially methylated CpG calls in epithelial cancer. Conclusion: The degree and variation of IC contamination in complex epithelial tissues is substantial. We provide a valuable resource and tool for assessing the epithelial purity and IC contamination of samples and for identifying differential methylation in such complex tissues.
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Affiliation(s)
- Shijie C Zheng
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, PR China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, PR China
| | - Amy P Webster
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Danyue Dong
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, PR China
- University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, PR China
| | - Andy Feber
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - David G Graham
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Roisin Sullivan
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Sarah Jevons
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Laurence B Lovat
- Division of Surgery & Interventional Science, UCL, London WC1E 6BT, UK
| | - Stephan Beck
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Martin Widschwendter
- Department of Women's Cancer, University College London, 74 Huntley Street, London WC1E 6AU, UK
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, PR China
- Department of Women's Cancer, University College London, 74 Huntley Street, London WC1E 6AU, UK
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27
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Pérez RF, Tejedor JR, Bayón GF, Fernández AF, Fraga MF. Distinct chromatin signatures of DNA hypomethylation in aging and cancer. Aging Cell 2018; 17:e12744. [PMID: 29504244 PMCID: PMC5946083 DOI: 10.1111/acel.12744] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2018] [Indexed: 12/21/2022] Open
Abstract
Cancer is an aging‐associated disease, but the underlying molecular links between these processes are still largely unknown. Gene promoters that become hypermethylated in aging and cancer share a common chromatin signature in ES cells. In addition, there is also global DNA hypomethylation in both processes. However, the similarity of the regions where this loss of DNA methylation occurs is currently not well characterized, and it is unknown if such regions also share a common chromatin signature in aging and cancer. To address this issue, we analyzed TCGA DNA methylation data from a total of 2,311 samples, including control and cancer cases from patients with breast, kidney, thyroid, skin, brain, and lung tumors and healthy blood, and integrated the results with histone, chromatin state, and transcription factor binding site data from the NIH Roadmap Epigenomics and ENCODE projects. We identified 98,857 CpG sites differentially methylated in aging and 286,746 in cancer. Hyper‐ and hypomethylated changes in both processes each had a similar genomic distribution across tissues and displayed tissue‐independent alterations. The identified hypermethylated regions in aging and cancer shared a similar bivalent chromatin signature. In contrast, hypomethylated DNA sequences occurred in very different chromatin contexts. DNA hypomethylated sequences were enriched at genomic regions marked with the activating histone posttranslational modification H3K4me1 in aging, while in cancer, loss of DNA methylation was primarily associated with the repressive H3K9me3 mark. Our results suggest that the role of DNA methylation as a molecular link between aging and cancer is more complex than previously thought.
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Affiliation(s)
- Raúl F. Pérez
- Nanomedicine Group; Nanomaterials and Nanotechnology Research Center (CINN-CSIC); Universidad de Oviedo; El Entrego, Asturias Spain
- Cancer Epigenetics Laboratory; Institute of Oncology of Asturias (IUOPA); Hospital Universitario Central de Asturias (HUCA); Universidad de Oviedo; Oviedo, Asturias Spain
| | - Juan Ramón Tejedor
- Cancer Epigenetics Laboratory; Institute of Oncology of Asturias (IUOPA); Hospital Universitario Central de Asturias (HUCA); Universidad de Oviedo; Oviedo, Asturias Spain
- Cáncer Epigenetics Laboratory; Fundación para la Investigación Biosanitaria de Asturias (FINBA); Instituto de Investigación Sanitaria del Principado de Asturias (ISPA); Oviedo, Asturias Spain
| | - Gustavo F. Bayón
- Cancer Epigenetics Laboratory; Institute of Oncology of Asturias (IUOPA); Hospital Universitario Central de Asturias (HUCA); Universidad de Oviedo; Oviedo, Asturias Spain
| | - Agustín F. Fernández
- Cancer Epigenetics Laboratory; Institute of Oncology of Asturias (IUOPA); Hospital Universitario Central de Asturias (HUCA); Universidad de Oviedo; Oviedo, Asturias Spain
- Cáncer Epigenetics Laboratory; Fundación para la Investigación Biosanitaria de Asturias (FINBA); Instituto de Investigación Sanitaria del Principado de Asturias (ISPA); Oviedo, Asturias Spain
| | - Mario F. Fraga
- Nanomedicine Group; Nanomaterials and Nanotechnology Research Center (CINN-CSIC); Universidad de Oviedo; El Entrego, Asturias Spain
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Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, Christensen BC. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol 2018; 19:64. [PMID: 29843789 PMCID: PMC5975716 DOI: 10.1186/s13059-018-1448-7] [Citation(s) in RCA: 186] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 05/08/2018] [Indexed: 11/23/2022] Open
Abstract
Genome-wide methylation arrays are powerful tools for assessing cell composition of complex mixtures. We compare three approaches to select reference libraries for deconvoluting neutrophil, monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cell fractions based on blood-derived DNA methylation signatures assayed using the Illumina HumanMethylationEPIC array. The IDOL algorithm identifies a library of 450 CpGs, resulting in an average R2 = 99.2 across cell types when applied to EPIC methylation data collected on artificial mixtures constructed from the above cell types. Of the 450 CpGs, 69% are unique to EPIC. This library has the potential to reduce unintended technical differences across array platforms.
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Affiliation(s)
- Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Devin C Koestler
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Rondi A Butler
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Helen M Hansen
- Department of Neurological Surgery, Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - John K Wiencke
- Department of Neurological Surgery, Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Karl T Kelsey
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Departments of Molecular and Systems Biology, and Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
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29
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Carnero-Montoro E, Alarcón-Riquelme ME. Epigenome-wide association studies for systemic autoimmune diseases: The road behind and the road ahead. Clin Immunol 2018; 196:21-33. [PMID: 29605707 DOI: 10.1016/j.clim.2018.03.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 03/26/2018] [Indexed: 12/20/2022]
Abstract
Epigenetics is known to be an important mechanism in the pathogenesis of autoimmune diseases. Epigenetic variations can act as integrators of environmental and genetic exposures and propagate activated states in immune cells. Studying epigenetic alterations by means of genome-wide approaches promises to unravel novel molecular mechanisms related to disease etiology, disease progression, clinical manifestations and treatment responses. This paper reviews what we have learned in the last five years from epigenome-wide studies for three systemic autoimmune diseases, namely systemic lupus erythematosus, primary Sjögren's syndrome, and rheumatoid arthritis. We examine the degree of epigenetic sharing between different diseases and the possible mediating role of epigenetic associations in genetic and environmental risks. Finally, we also shed light into the use of epigenetic markers towards a better precision medicine regarding disease prediction, prevention and personalized treatment in systemic autoimmunity.
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Affiliation(s)
- Elena Carnero-Montoro
- Pfizer - University of Granada - Andalusian Government Center for Genomics and Oncological Research (GENYO), Granada, Spain.
| | - Marta E Alarcón-Riquelme
- Pfizer - University of Granada - Andalusian Government Center for Genomics and Oncological Research (GENYO), Granada, Spain; Unit of Inflammatory Chronic Diseases, Institute of Environmental Medicine, Karolinska Institutet, Solna, Sweden.
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30
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Masser DR, Hadad N, Porter H, Stout MB, Unnikrishnan A, Stanford DR, Freeman WM. Analysis of DNA modifications in aging research. GeroScience 2018; 40:11-29. [PMID: 29327208 PMCID: PMC5832665 DOI: 10.1007/s11357-018-0005-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 01/05/2018] [Indexed: 12/22/2022] Open
Abstract
As geroscience research extends into the role of epigenetics in aging and age-related disease, researchers are being confronted with unfamiliar molecular techniques and data analysis methods that can be difficult to integrate into their work. In this review, we focus on the analysis of DNA modifications, namely cytosine methylation and hydroxymethylation, through next-generation sequencing methods. While older techniques for modification analysis performed relative quantitation across regions of the genome or examined average genome levels, these analyses lack the desired specificity, rigor, and genomic coverage to firmly establish the nature of genomic methylation patterns and their response to aging. With recent methodological advances, such as whole genome bisulfite sequencing (WGBS), bisulfite oligonucleotide capture sequencing (BOCS), and bisulfite amplicon sequencing (BSAS), cytosine modifications can now be readily analyzed with base-specific, absolute quantitation at both cytosine-guanine dinucleotide (CG) and non-CG sites throughout the genome or within specific regions of interest by next-generation sequencing. Additional advances, such as oxidative bisulfite conversion to differentiate methylation from hydroxymethylation and analysis of limited input/single-cells, have great promise for continuing to expand epigenomic capabilities. This review provides a background on DNA modifications, the current state-of-the-art for sequencing methods, bioinformatics tools for converting these large data sets into biological insights, and perspectives on future directions for the field.
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Affiliation(s)
- Dustin R Masser
- Reynolds Oklahoma Center on Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Nathan Shock Center for Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Niran Hadad
- Reynolds Oklahoma Center on Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Nathan Shock Center for Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Hunter Porter
- Reynolds Oklahoma Center on Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Nathan Shock Center for Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Michael B Stout
- Reynolds Oklahoma Center on Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Nutritional Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Archana Unnikrishnan
- Reynolds Oklahoma Center on Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - David R Stanford
- Reynolds Oklahoma Center on Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Willard M Freeman
- Reynolds Oklahoma Center on Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Oklahoma Nathan Shock Center for Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Department of Nutritional Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
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31
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Chen Y, Widschwendter M, Teschendorff AE. Systems-epigenomics inference of transcription factor activity implicates aryl-hydrocarbon-receptor inactivation as a key event in lung cancer development. Genome Biol 2017; 18:236. [PMID: 29262847 PMCID: PMC5738803 DOI: 10.1186/s13059-017-1366-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2017] [Accepted: 11/27/2017] [Indexed: 12/25/2022] Open
Abstract
Background Diverse molecular alterations associated with smoking in normal and precursor lung cancer cells have been reported, yet their role in lung cancer etiology remains unclear. A prominent example is hypomethylation of the aryl hydrocarbon-receptor repressor (AHRR) locus, which is observed in blood and squamous epithelial cells of smokers, but not in lung cancer. Results Using a novel systems-epigenomics algorithm, called SEPIRA, which leverages the power of a large RNA-sequencing expression compendium to infer regulatory activity from messenger RNA expression or DNA methylation (DNAm) profiles, we infer the landscape of binding activity of lung-specific transcription factors (TFs) in lung carcinogenesis. We show that lung-specific TFs become preferentially inactivated in lung cancer and precursor lung cancer lesions and further demonstrate that these results can be derived using only DNAm data. We identify subsets of TFs which become inactivated in precursor cells. Among these regulatory factors, we identify AHR, the aryl hydrocarbon-receptor which controls a healthy immune response in the lung epithelium and whose repressor, AHRR, has recently been implicated in smoking-mediated lung cancer. In addition, we identify FOXJ1, a TF which promotes growth of airway cilia and effective clearance of the lung airway epithelium from carcinogens. Conclusions We identify TFs, such as AHR, which become inactivated in the earliest stages of lung cancer and which, unlike AHRR hypomethylation, are also inactivated in lung cancer itself. The novel systems-epigenomics algorithm SEPIRA will be useful to the wider epigenome-wide association study community as a means of inferring regulatory activity. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1366-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yuting Chen
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, 320 Yue Yang Road, Shanghai, 200031, China
| | - Martin Widschwendter
- Department of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6AU, UK
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, 320 Yue Yang Road, Shanghai, 200031, China. .,Department of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6AU, UK. .,UCL Cancer Institute, University College London, Paul O'Gorman Building, 72 Huntley Street, London, WC1E 6BT, UK.
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32
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Statistical and integrative system-level analysis of DNA methylation data. Nat Rev Genet 2017; 19:129-147. [PMID: 29129922 DOI: 10.1038/nrg.2017.86] [Citation(s) in RCA: 168] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.
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33
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Nordlund J, Syvänen AC. Epigenetics in pediatric acute lymphoblastic leukemia. Semin Cancer Biol 2017; 51:129-138. [PMID: 28887175 DOI: 10.1016/j.semcancer.2017.09.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 08/21/2017] [Accepted: 09/02/2017] [Indexed: 12/11/2022]
Abstract
Acute lymphoblastic leukemia (ALL) is the most common malignancy in children. ALL arises from the malignant transformation of progenitor B- and T-cells in the bone marrow into leukemic cells, but the mechanisms underlying this transformation are not well understood. Recent technical advances and decreasing costs of methods for high-throughput DNA sequencing and SNP genotyping have stimulated systematic studies of the epigenetic changes in leukemic cells from pediatric ALL patients. The results emerging from these studies are increasing our understanding of the epigenetic component of leukemogenesis and have demonstrated the potential of DNA methylation as a biomarker for lineage and subtype classification, prognostication, and disease progression in ALL. In this review, we provide a concise examination of the epigenetic studies in ALL, with a focus on DNA methylation and mutations perturbing genes involved in chromatin modification, and discuss the future role of epigenetic analyses in research and clinical management of ALL.
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Affiliation(s)
- Jessica Nordlund
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Sweden.
| | - Ann-Christine Syvänen
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Sweden
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34
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Lea AJ, Vilgalys TP, Durst PAP, Tung J. Maximizing ecological and evolutionary insight in bisulfite sequencing data sets. Nat Ecol Evol 2017; 1:1074-1083. [PMID: 29046582 PMCID: PMC5656403 DOI: 10.1038/s41559-017-0229-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 05/31/2017] [Indexed: 12/12/2022]
Abstract
Genome-scale bisulfite sequencing approaches have opened the door to ecological and evolutionary studies of DNA methylation in many organisms. These approaches can be powerful. However, they introduce new methodological and statistical considerations, some of which are particularly relevant to non-model systems. Here, we highlight how these considerations influence a study's power to link methylation variation with a predictor variable of interest. Relative to current practice, we argue that sample sizes will need to increase to provide robust insights. We also provide recommendations for overcoming common challenges and an R Shiny app to aid in study design.
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Affiliation(s)
- Amanda J Lea
- Department of Biology, Duke University, Durham, NC, 27708, USA.
- Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Washington Road, Princeton University, Princeton, NJ, 08540, USA.
| | - Tauras P Vilgalys
- Department of Evolutionary Anthropology, Duke University, Durham, NC, 27708, USA
| | - Paul A P Durst
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jenny Tung
- Department of Biology, Duke University, Durham, NC, 27708, USA.
- Department of Evolutionary Anthropology, Duke University, Durham, NC, 27708, USA.
- Institute of Primate Research, National Museums of Kenya, Nairobi, 00502, Kenya.
- Duke University Population Research Institute, Duke University, Durham, NC, 27708, USA.
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Holbrook JD, Huang RC, Barton SJ, Saffery R, Lillycrop KA. Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies? Epigenomics 2017; 9:1143-1150. [DOI: 10.2217/epi-2017-0032] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Excitement about DNA methylation biomarkers has been tempered by a growing appreciation of the complex causal relations with cell fate. Intersample differences in DNA methylation can be partitioned into those that are independent of cellular heterogeneity and those that are caused by differential mixtures of cell types. Generally, the field has assumed that the former are more likely to be causative of disease. The latter has been considered a likely consequence of disease and a confounder to be removed. We argue that the conceptual separation of these signals is artificial and not necessarily informative about causation. DNA methylation is a very sensitive measure of cell fate mix and therefore reveals much about underlying disease etiology including aspects of causation.
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Affiliation(s)
- Joanna D Holbrook
- Human Development & Health Academic Unit, University of Southampton & NIHR Southampton Biomedical Research Centre, University of Southampton & University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK
| | - Rae-Chi Huang
- Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Sheila J Barton
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Richard Saffery
- Cancer & Disease Epigenetics, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia
- Department of Paediatrics, The University of Melbourne, Parkville, VIC, Australia
| | - Karen A Lillycrop
- Human Development & Health Academic Unit, University of Southampton & NIHR Southampton Biomedical Research Centre, University of Southampton & University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK
- Biological Sciences, Faculty of Natural & Environmental Sciences, University of Southampton, Tremona Road, Southampton, SO16 6YD, UK
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Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, Oh S, Burchard EG, Eskin E, Zou J, Halperin E. Correcting for cell-type heterogeneity in DNA methylation: a comprehensive evaluation. Nat Methods 2017; 14:218-219. [PMID: 28245214 PMCID: PMC5548185 DOI: 10.1038/nmeth.4190] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Elior Rahmani
- Blavatnik School of Computer Science, Tel-Aviv University, Tel Aviv, Israel
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel
| | - Noah Zaitlen
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Yael Baran
- Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Joshua Galanter
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Science, University of California, San Francisco, San Francisco, California, USA
- Genentech, South San Francisco, California, USA
| | - Sam Oh
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Esteban G Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Science, University of California, San Francisco, San Francisco, California, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Palo Alto, California, USA
| | - Eran Halperin
- Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA
- Department of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, Los Angeles, California, USA
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