DNA Methylation Changes During Aging and in Age-Associated Diseases
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DNA Methylation Changes During Aging and in Age-Associated Diseases Von der Fakultät für Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades einer Doktorin der Naturwissenschaften genehmigte Dissertation vorgelegt von M.Sc. Monika Eipel aus Jülich Berichter: Univ.-Prof. Dr. Dr. Wolfgang Wagner Univ.-Prof. Dr. Martin Zenke Tag der mündlichen Prüfung: 04.06.2019 Diese Dissertation ist auf den Internetseiten der Universitätsbibliothek verfügbar. II Abstract VI Zusammenfassung VII 1 Introduction 1 1.1 DNA Methylation in the Aging Organism 1 1.1.1 Mechanisms of DNA Methylation and Demethylation 1 1.1.2 DNA Methylation Changes During Aging 3 1.1.3 DNA Methylation as a Biomarker for Aging 6 1.2 Clonal Development During Aging and Age-Associated Diseases 7 1.2.1 Age-Related Clonal Hematopoiesis 7 1.2.2 DNA Methylation Changes During Malignant Transformation 11 1.2.3 Inherited Age-Associated Diseases and Telomeropathies 12 1.2.4 The PRDM Family 17 1.3 Stem Cells 19 1.3.1 Induced Pluripotent Stem Cells 19 1.3.2 Induced Pluripotent Stem Cells as a Model System in Clinical Research 20 1.4 Objectives 23 2 Materials and Methods 24 2.1 Molecular Biology 24 2.1.1 DNA Isolation 24 2.1.2 Polymerase Chain Reaction 24 2.1.3 Agarose Gel Electrophoresis 25 2.1.4 Bisulfite Conversion 25 2.1.5 Pyrosequencing 26 2.1.6 Barcoded Bisulfite Amplicon Next Generation Sequencing 29 2.1.7 RNA Isolation 31 2.1.8 cDNA Synthesis and Semi-qPCR 31 2.2 Age Prediction Models 33 2.3 Cloning 34 III 2.3.1 Using CRISPR/Cas9n to Generate Gene Knockouts 34 2.3.2 Generation of PRDM8 Overexpression Vector 37 2.3.3 Transformation of Escherichia Coli 38 2.3.4 Plasmid Isolation and Colony PCR 38 2.3.5 Transfection of Induced Pluripotent Stem Cells via Electroporation 39 2.3.6 Virus Production 39 2.3.7 Transduction of Induced Pluripotent Stem Cells 40 2.4 Cell Culture 41 2.4.1 Culture of Induced Pluripotent Stem Cells 41 2.4.2 Embryoid Body Assay 42 2.4.3 Neural differentiation 43 2.4.4 HEK 293T Cell Culture 45 2.4.5 Cell Sorting 45 2.5 Proteochemistry 45 2.5.1 H and E staining of Buccal Swab Smears 45 2.5.2 Immunofluorescence Staining 45 2.6 Statistics and Data Analyses 46 3 Results 47 3.1 Epigenetic Age Predictions of Buccal Swab Samples 47 3.1.1 Comparative Analyses of Age-Associated DNA Methylation Changes in Blood and Mouth Swab Samples 47 3.1.2 Development of a Predictor to Determine Cell Compositions in Buccal Swabs 50 3.1.3 Combination of Age-Associated and Cell Type Specific CG Dinucleotides to Improve Age Predictions 55 3.2 Analysis of DNA Methylation Variability of Neighboring CG Dinucleotides in Clonal Diseases 57 3.2.1 Identifying Myeloid Malignancies by Analyses of DNA Methylation Variability at Neighboring CG Dinucleotides 58 IV 3.2.2 Using Barcoded Bisulfite Amplicon Next Generation Sequencing to Classify DNA Methylation Patterns of AML Samples 65 3.3 The Functional Role of PRDM8 on Stem Cell Differentiation 69 3.3.1 PRDM8 is Differentially Methylated in Dyskeratosis Congenita Patients 69 3.3.2 Using CRISPR/Cas9n to Modulate PRDM8 Expression in Induced Pluripotent Stem Cells 70 3.3.3 Influence of PRDM8 on Neural Differentiation 74 3.3.4 Genome-Wide Analyses of PRDM8-Dependent DNA Methylation Changes During Neural Differentiation 79 4 Discussion 85 4.1 Tissue Specific DNA Methylation Enhances Epigenetic Age Predictions 85 4.2 Malignant Clonal Development can be Identified by DNA Methylation Variability of Successive CG Dinucleotides 88 4.3 Classification of DNA Methylation Patterns of Individual DNA Strands Provides a New Perspective to Track Clonal Hematopoiesis 91 4.4 Lack of PRDM8 Expression Leads to Impaired Neural Differentiation and is Associated with Differential Promoter Methylation 93 5 Conclusion and Future Perspective 97 6 Bibliography 98 7 Appendix 116 7.1 Abbreviations 116 7.2 List of Figures 120 7.3 List of Tables 122 7.4 Publications 123 7.5 Acknowledgements 125 7.6 Declaration of Authorship 126 V Abstract Abstract Highly reproducible changes in DNA methylation (DNAm) can be used for epigenetic age predictions. In forensic science, the specimens of choice for age predictions are buccal swabs containing leukocytes and epithelial cells. However, it is well known that DNAm varies between cell types. To take these differences into account, we developed a model to predict the proportion of cell types in buccal swabs based on DNAm levels at only two CpG sites. The combination of these CpG sites with three age-associated CpG sites generated a new model for age predictions of buccal swab samples which allows age prediction with high accuracy. Of note, epigenetic age predictors are not applicable to blood samples from patients with hematological malignancies, indicating that the DNAm landscape is disturbed during malignant transformation. To further elucidate how methylation levels in age-associated CpG sites change during malignant transformation, we focused on the age-associated region in PDE4C. We demonstrate that the variability of DNAm patterns of successive CpG sites in PDE4C is indicative for clonal hematopoiesis. Barcoded Bisulfite Amplicon Next Generation Sequencing was used to derive patient-specific methylation patterns of AML samples with single strand resolution. These patterns could be classified as healthy or AML-derived by machine learning algorithms. We hypothesize that the quantification of AML derived DNAm patterns provides a new perspective to track clonal hematopoiesis. Furthermore, we have recently described reduced expression and hypermethylation of the PRDM8 gene in patients with the premature aging disease dyskeratosis congenita. To study the physiological role of this epigenetically controlled histone methyltransferase, we modulated its expression in iPSCs using the CRISPR/Cas9n technology. Spontaneous and directed differentiation experiments revealed impaired neural differentiation of PRDM8-/- and PRDM8+/- clones. Genome-wide methylation analyses indicate that PRDM8 positively regulates neurogenesis via the promotion of specific DNAm changes in promoter regions of genes associated to neural functions. In conclusion, this thesis enhances our understanding of DNAm changes and variability at specific CpG sites during aging and in age-associated diseases and demonstrates how these changes can be used for forensic and clinical applications. VI Zusammenfassung Zusammenfassung Reproduzierbare Veränderungen in der DNA-Methylierung (DNAm) können für epigenetische Altersvorhersagen genutzt werden. Für die forensische Bestimmung des epigenetischen Alters werden Mundschleimhautabstriche (MSA), bestehend aus Leukozyten und Epithelzellen, bevorzugt. Jedoch variiert die DNAm zwischen verschiedenen Zelltypen. Daher haben wir ein Modell zur Bestimmung der zellulären Komposition von MSA entwickelt, welches auf der DNAm von zwei CpGs basiert. Die Kombination dieser CpGs mit drei altersassoziierten CpGs generierte ein neues Modell zur Altersbestimmung von MSA mit einer hohen Genauigkeit. Epigenetische Altersabschätzungen sind jedoch nicht für Blutproben von Patienten mit hämatologischen Erkrankungen anwendbar, was auf eine Veränderung der DNAm- Landschaft während der malignen Transformation hinweist. Um zu untersuchen wie sich die DNAm in altersassoziierten Regionen während der malignen Transformation verändert, fokussierten wir uns auf die altersassoziierte Region in PDE4C. Wir konnten zeigen, dass die Variabilität von DNAm-Mustern von benachbarten CpGs in PDE4C indikativ für klonale Hämatopoese ist. BBA-Seq wurde angewandt um patientenspezifische DNAm-Muster von AML-Patienten mit Einzelstrangauflösung zu generieren. Diese Muster wurden mittels maschinellen Lernens als gesund oder AML-abgeleitet klassifiziert. Wir nehmen an, dass die Quantifizierung der AML-abgeleiteten DNAm-Muster eine neue Perspektive zur Verfolgung der klonalen Hämatopoese bietet. Kürzlich beschrieben wir die Hypermethylierung und reduzierte Expression von PRDM8 in Patienten mit der altersassoziierten Krankheit Dyskeratosis Congenita. Um die funktionelle Rolle dieser epigenetisch regulierten Histon-Methyltransferase zu studieren, wurde die Genexpression in iPSCs mittels CRISPR/Cas9n moduliert. Unter spontanen und gerichteten Differenzierungsbedingungen konnte eine geminderte neuronale Differenzierung der PRDM8+/- und PRDM8-/- Zellen nachgewiesen werden. Genomweite Methylierungsanalysen legen nahe, dass PRDM8 die Neuronalentwicklung über DNA-Methylierungsveränderungen in Promotoren von Genen reguliert, welche mit neuronalen Funktionen assoziiert sind. Zusammenfassend vertieft diese Thesis unser Verständnis von spezifischen DNAm- Veränderungen während des Alterns als auch in altersassoziierten Krankheiten und demonstriert wie diese Veränderungen für forensische und klinische Zwecke genutzt werden können. VII Introduction 1 Introduction 1.1 DNA Methylation in the Aging Organism Aging is caused by a variety of hallmarks, such as genomic instability, telomere attrition, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion and, importantly, epigenetic abnormalities including changes in DNA methylation (DNAm). Epigenetic systems control gene activity and thus - either directly or indirectly - affect all other hallmarks (Ashapkin et al. 2017). Therefore, current research focuses on the nature and mechanisms of epigenetic variability. 1.1.1 Mechanisms of DNA Methylation and Demethylation Epigenetics can be referred to as the interface between the genome