Science, Faculty of
Medical Genetics, Department of
DSpace
UBCV
Fu, Maggie Po Yuan
Doctor of Philosophy - PhD
University of British Columbia
Epigenetic marks are essential for maintaining cellular identity and functions, through regulation of chromatin structure and transcriptional activation. This process is especially relevant in early development during cell differentiation, where chromatin organization and DNA methylation (DNAme) patterns serve as âepigenetic barriersâ to preserve cellular identity. Disruptions in epigenetic regulation are therefore commonly associated with atypical development. Yet, the direct mechanisms underlying the role of epigenetic dysregulation in pathology remain an area of active research. One challenge in the field of research originates from the plasticity of DNAme; specifically, as DNAme can be modified by factors including cellular identity, biological sex, age, and environmental factors, it can be difficult to tease apart the signals driving its variability. The overarching objective of my thesis is to develop bioinformatic tools to better dissect the sources of DNAme variability, with a focus on rare diseases of epigenetic origin. Chapter 2 describes a patient with compound heterozygous variants in ASXL1, combined immunodeficiency, and EBV-associated B-cell lymphoma. I reported an altered DNAme landscape and associated immune cell dysfunction associated with the patientâs clinical phenotypes. In Chapter 3, I curated population-specific reference DNAme datasets to enable detection of disease-associated DNAme signatures, comparing multiple statistical methods. Chapter 4 details the optimization of a cell type prediction pipeline for pediatric samples using machine learning, alongside the development of a user-friendly R package that empowers future research in pediatric and longitudinal immune dynamics. Finally, in Chapter 5, I synthesized current strategies for addressing cellular heterogeneity in DNAme analyses and proposed a set of best-practice guidelines. Together, this body of work supports more accurate identification of DNAme patterns associated with rare diseases and immune landscape alterations.
UNTANGLING SOURCES OF DNA METHYLATION VARIABILITY IN THE CONTEXT OF DEVELOPMENT AND RARE DISEASES by Maggie Po Yuan Fu B.Sc, The University of British Columbia, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Genome Science and Technology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2025 © Maggie Po Yuan Fu, 2025ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: UNTANGLING SOURCES OF DNA METHYLATION VARIABILITY IN THE CONTEXT OF DEVELOPMENT AND RARE DISEASES submitted by Maggie Po Yuan Fu in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Genome Science and Technology Examining Committee: Dr. Michael S. Kobor, Professor, Medical Genetics, UBC Supervisor Dr. Eldon Emberly, Professor, Physics, Simon Fraser University Supervisory Committee Member Dr. Wan L. Lam, Professor, Pathology and Laboratory Medicine, UBC University Examiner Dr. Christian G. Steidl, Professor, Pathology and Laboratory Medicine, UBC University Examiner Additional Supervisory Committee Members: Dr. Keegan Korthsauer, Assistant Professor, Statistics, UBC Supervisory Committee Member Dr. William T. Gibson, Professor, Medical Genetics, UBC Supervisory Committee Member iii Abstract Epigenetic marks are essential for maintaining cellular identity and functions, through regulation of chromatin structure and transcriptional activation. This process is especially relevant in early development during cell differentiation, where chromatin organization and DNA methylation (DNAme) patterns serve as âepigenetic barriersâ to preserve cellular identity. Disruptions in epigenetic regulation are therefore commonly associated with atypical development. Yet, the direct mechanisms underlying the role of epigenetic dysregulation in pathology remain an area of active research. One challenge in the field of research originates from the plasticity of DNAme; specifically, as DNAme can be modified by factors including cellular identity, biological sex, age, and environmental factors, it can be difficult to tease apart the signals driving its variability. The overarching objective of my thesis is to develop bioinformatic tools to better dissect the sources of DNAme variability, with a focus on rare diseases of epigenetic origin. Chapter 2 describes a patient with compound heterozygous variants in ASXL1, combined immunodeficiency, and EBV-associated B-cell lymphoma. I reported an altered DNAme landscape and associated immune cell dysfunction associated with the patientâs clinical phenotypes. In Chapter 3, I curated population-specific reference DNAme datasets to enable detection of disease-associated DNAme signatures, comparing multiple statistical methods. Chapter 4 details the optimization of a cell type prediction pipeline for pediatric samples using machine learning, alongside the development of a user-friendly R package that empowers future research in pediatric and longitudinal immune dynamics. Finally, in Chapter 5, I synthesized current strategies for addressing cellular heterogeneity in DNAme analyses and proposed a set of best-practice guidelines. Together, this body of work supports more accurate identification of DNAme patterns associated with rare diseases and immune landscape alterations. iv Lay Summary Our bodies are made up of many different types of cells. The unique properties of each type of cell are partially controlled by epigeneticsâchemical changes that help turn genes on or off without altering the DNA sequence. These epigenetic marks are especially important during early development. When this system goes wrong, it can lead to serious developmental diseases. One of the key epigenetic marks is called DNA methylation (DNAme), which can be influenced by factors such as age, sex, or the environment. Given its variability, it can be challenging to delineate whether DNAme changes are attributable to diseases or natural variation in the population. The goal of my research is to create computational and statistical tools to better understand and interpret these complex epigenetic patterns, especially in people with rare genetic disorders. Overall, my research contributes to our understanding of hidden DNAme signals that may be linked to diseases. v Preface All work presented in this thesis was conducted at BC Childrenâs Hospital Research Institute and The University of British Columbia in collaboration with scientists and clinicians. Research study protocols were approved by The University of British Columbia Clinical Research Ethics Board. All study participants, including patients and controls and/or their parent or legal guardian gave signed informed consent. Generative artificial intelligence (GenAI) was not used for any aspects of the work. Some of the work presented in this thesis is adapted from publication, specifically chapters 1, 2, 4, 5, and 6. This is discussed in more detail below. The contribution of various collaborators and my specific role in each of the projects are also detailed below. Chapter 1 and Chapter 6: Some of the information contained in chapter 1 and 6 was published as a review paper on a peer-reviewed journal (Frontiers in Genetics)1. This was done under full supervision from Dr. Michael Kobor. I was principally responsible for the literature review and manuscript preparation. Dr. Sarah Merrill, Dr. Mehul Sharma, Dr. William Gibson, and Dr. Stuart Turvey assisted with reviewing and editing the manuscript. Chapter 2: All of the data in chapter 2 was recently accepted by a peer reviewed journal (Journal of Experimental Medicine). The research protocols were approved by the University of British Columbia Clinical Research Ethics Board (#H15-00641). I share principal responsibilities for this study with the co-first author of the manuscript, Dr. Mehul Sharma. This involves all aspects of the project, including study design, experiment execution, data analysis and manuscript preparation. This work is a collaboration of Dr. Michael Kobor with Dr. Stuart Turvey and Catherine Biggs, all at BC Childrenâs Hospital Research Institute. However, there was significant contribution from collaborators without whom this was not possible. vi Pariya Yousefi, Ryan Tan, Simran Samra, Liam Golding, and Susan Lin assisted in laboratory experiments. Dr. Sarah Merrill helped with statistical analysis. Kate Del Bel, Rebecca Deyell, Jacob Rozmus, Wingfield Rehmus, Kyla Hildebrand, Elliot James, Géraldine Blanchard-Rohner, Kevin Shopsowitz, Jefferson Terry, Anna F Lee, Laura Sauvé, Hana Mitchell, Julie Prendiville, Colin Ross, Simon R M Dobson, Suzanne Vercauteren, and Dr. Clara van Karnebeek were involved with patient care, patient enrollment, and analysis of clinical data. Dr. Bhavi Modi, Dr. Britt I Drögemöller, Dr. Allison Matthews, Dr. Maja Tarailo-Graovac, Dr. Wyeth W. Wasserman, and Margaret L McKinnon conducted genetic screening and identified variants of significance. DNA extraction and methylation microarray were performed by Dr. Julie MacIsaac, Kristy Dever, and Dr. David Lin. Clinical flow cytometry was done by Dr. Audi Setiadi and her team at BC Childrenâs Hospital. Dr. Anna F. Lee and her team, histology labs at BC Childrenâs Hospital and BC Cancer, performed in histological and immunohistochemistry experiments. Dr. Mandy Meijer contributed to reviewing and editing the manuscript. Chapter 3: The data in chapter 3 is currently not published or available as a pre-print, the manuscript is under preparation The project utilizes publicly available samples on Gene Expression Omnibus databased and the samples described in Chapter 2, with research protocols approved by the University of British Columbia Clinical Research Ethics Board (#H15-00641). This was done under full supervision from Dr. Michael Kobor, who advised on all aspects of the study. I was principally responsible for all aspects of this study including study design, experiment execution, and data analysis. Chapter 4: The data in chapter 4 is currently not published but available as a preprint on BioRxiv (DOI: 10.1101/2025.04.22.649907)2. The Research Ethics Board (#H07-03120) examined and authorized the processing of the Canadian Healthy Infant Longitudinal Development (CHILD) study in accordance with the Tri-Council Policy Statement: Ethical Conduct for vii Human Research (TCPS2, 2018). Data transfer agreement (UBC ref: M21-00880) was set up for the analysis of the Isle of Wight cohort DNA methylation data. The project was done under full supervision from Dr. Michael Kobor, who advised on all aspects of the study. I was principally responsible for all aspects of this study including study design, experiment execution, and data analysis. Dr. Karlie Edwards, Erick Navarro-Delgado, Dr. Sarah Merrill, and Dr. Chaini Konwar contributed to DNA methylation data preprocessing and advice on statistical analysis. Dr. Negusse Kitaba and Dr. John Holloway facilitated access to DNA methylation data from the Isle of Wight (IOW) cohort. Dr. Stuart Turvey was responsible for the establishment of DNA methylation profiling for the CHILD cohort. Chapter 5: Chapter 5 is adapted from a published primer article on a peer-reviewed journal (STAR Protocols)3. This was done under full supervision from Dr. Michael Kobor. I was principally responsible for the literature review, code generation, and manuscript preparation. Dr. Sarah Merrill and Dr. Keegan Korthauer provided statistical support and facilitated with editing of the paper. viii Table of Contents Abstract ...................................................................................................................... iii Lay Summary ............................................................................................................... iv Preface ........................................................................................................................ v Table of Contents ........................................................................................................viii List of Tables .............................................................................................................. xiii List of Figures ............................................................................................................. xiv Abbreviations ........................................................................................................... xviii Acknowledgements .................................................................................................. xxiii Dedication................................................................................................................. xxv 1 Chapter 1: Introduction ............................................................................................ 1 1.1 The molecular basis of epigenetic modifications .................................................. 1 1.1.1 Functions of DNA methylation â the knowns and the unknowns ........................ 2 1.1.2 DNA methylation â writers and erasers ............................................................ 5 1.1.3 Histone modification and chromatin remodelers ............................................. 6 1.1.4 Interplay amongst DNA methylation and other epigenetic modifications ........... 7 1.2 Roles of DNA methylation in development and cell function .................................. 8 1.2.1 DNA methylation in early development ........................................................... 9 1.2.2 DNA methylation and cell identity ................................................................. 10 1.2.3 Developmental Origins of Health and Disease framework .............................. 11 1.2.4 Aging, epigenetic clocks, and cellular senescence......................................... 11 1.3 Human immune cells in peripheral blood ........................................................... 13 1.4 Rare diseases of epigenetic origin (RDEOs)......................................................... 14 1.4.1 Examples of RDEOs ..................................................................................... 26 1.4.1.1 DNMT3A-associated RDEOs ................................................................... 26 1.4.1.2 TET2-associated RDEOs ......................................................................... 26 1.4.1.3 PRC2-related overgrowth syndromes ...................................................... 27 1.4.1.4 Bohring-Opitz syndrome ......................................................................... 28 1.4.1.5 Immunodeficiency with centromeric instability and facial anomalies syndrome (ICF) ........................................................................................................... 28 1.4.2 Inborn errors of immunity, cancer, and clonal hematopoiesis......................... 29 ix 1.5 High dimensional analysis of DNA methylation ................................................... 30 1.5.1 Biological variables contributing to DNAme variability in human population .... 31 1.5.1.1 Cellular heterogeneity ............................................................................ 32 1.5.1.2 Age ........................................................................................................ 32 1.5.2 Strategies to account for DNAme-associated variability ................................. 33 1.6 Thesis rationale and objective ........................................................................... 33 2 Chapter 2: Human ASXL1 Deficiency Causes Epigenetic Dysfunction, Combined Immunodeficiency and EBVâAssociated Hodgkin Lymphoma ........................................ 35 2.1 Rationale and Introduction ................................................................................ 35 2.2 Materials and Methods ..................................................................................... 36 2.2.1 Participants and consent ............................................................................. 36 2.2.2 Identification of candidate variants ............................................................... 36 2.2.3 Sanger sequencing ...................................................................................... 37 2.2.4 Generation and maintenance of primary T cells and fibroblasts ...................... 37 2.2.5 Generation and expression of ASXL1 variant plasmids ................................... 38 2.2.6 Stable WT ASXL1 expression in expanded patient-derived T cells .................... 39 2.2.7 DNA extraction for DNA methylation array analysis ........................................ 39 2.2.8 Data preprocessing and differential DNA methylation analysis ....................... 40 2.2.9 Epigenetic age analysis ................................................................................ 41 2.2.10 Enrichment analysis .................................................................................... 41 2.2.11 RNA sequencing of expanded T cells ............................................................. 41 2.2.12 Enrichment analysis for differential expression.............................................. 42 2.2.13 Intracellular flow cytometry in PBMCs, T cells, and primary fibroblasts ........... 42 2.2.14 Clinical-grade flow cytometry ....................................................................... 45 2.2.15 Histology .................................................................................................... 46 2.3 Results ............................................................................................................ 47 2.3.1 Case description ......................................................................................... 47 2.3.2 Identification and characterization of novel germline compound heterozygous ASXL1 variants ............................................................................................................ 51 2.3.3 Differential DNAme in PRC-regulated genomic elements and advanced epigenetic aging in the patientâs lymphocytes ............................................................... 56 x 2.3.4 Wide-spread DNAme loss and advanced epigenetic aging in patientâs T cells were rescued by WT ASXL1 transduction ...................................................................... 62 2.3.5 Patient CD4 T cells displayed elevated PD1 and contraction of the T follicular helper cell compartment ............................................................................................. 67 2.3.6 ASXL1 variants impaired CD4 and CD8 T-cell maintenance and activation in patient cells ............................................................................................................... 74 2.3.7 Reduced NKT and NK cell cytotoxicity was rescued in vitro with WT ASXL1 and recovered post-HSCT .................................................................................................. 79 2.4 Discussion ....................................................................................................... 81 3 Chapter 3: Identifying Rare Disease Epigenetic Signature with Population-Specific Reference DNAme Datasets ........................................................................................ 87 3.1 Rationale and Introduction ................................................................................ 87 3.2 Material and Methods ....................................................................................... 88 3.2.1 Sample selection for pediatric population reference set ................................. 88 3.2.2 Normalization, QC, and imputation of DNA methylation data ......................... 89 3.2.3 Cell type proportion prediction ..................................................................... 90 3.2.4 Correcting for variances associated with inter-sample cellular heterogeneity .. 90 3.2.5 Batch correction ......................................................................................... 91 3.2.6 Statistical approaches for differential DNA methylation analysis .................... 91 3.3 Results ............................................................................................................ 93 3.3.1 Curation of pediatric population DNAme reference dataset ........................... 93 3.3.2 Comparable trends observed across three statistical approaches .................. 93 3.3.3 One-sample t-test yielded more unique results than the other two methods . 100 3.4 Discussion ..................................................................................................... 102 4 Chapter 4: Advancing Pediatric and Longitudinal DNA Methylation Studies with CellsPickMe, an Integrated Blood Cell Deconvolution Method ..................................... 105 4.1 Rationale and Introduction .............................................................................. 105 4.2 Materials and Methods ................................................................................... 106 4.2.1 Cohort description .................................................................................... 106 4.2.2 DNA sample collection and methylation array for the CHILD cohort .............. 107 4.2.3 CHILD cohort DNA methylation data processing.......................................... 107 4.2.4 Pan-age blood reference-based cell deconvolution ..................................... 108 4.2.5 Model Performance Assessment ................................................................ 112 xi 4.2.6 Code Availability ....................................................................................... 114 4.3 Results .......................................................................................................... 114 4.3.1 Cellular maturity and developmental time point are major contributors to DNA methylation variability ............................................................................................... 114 4.3.2 Training procedure of reference-based cell type deconvolution .................... 116 4.3.3 Normalization impacted prediction performance across reference datasets . 118 4.3.4 UniBlood19 reference unveiled epigenetic signatures associated with developmental immune cell transition ....................................................................... 125 4.3.5 Elastic net and random forest consistently performed well amongst feature selection methods .................................................................................................... 134 4.3.6 The trained pipeline outperformed benchmark methods in test datasets ...... 138 4.4 Discussion ..................................................................................................... 143 5 Chapter 5: Examining Cellular Heterogeneity in Human DNA Methylation Studies: Overview and Recommendations ............................................................................... 148 5.1 Rationale and Introduction .............................................................................. 148 5.2 Main .............................................................................................................. 150 5.2.1 Obtaining ISCH information ....................................................................... 151 5.2.1.1 Reference-based ISCH prediction ......................................................... 152 5.2.1.2 Reference-free ISCH prediction ............................................................. 160 5.2.2 Accounting for intersample differences in cellular composition .................... 165 5.2.2.1 Use robust linear regression and include ISCH information as covariates 166 5.2.2.2 Adjusting for ISCH by residualization ..................................................... 167 5.2.2.3 Accounting for non-compositional estimates ......................................... 168 5.2.3 Investigating cell type-specific associations of variables of interest and DNA methylation .............................................................................................................. 168 5.3 Conclusion .................................................................................................... 171 6 Chapter 6: Conclusion and Future Directions ........................................................ 172 6.1 Summary of findings ....................................................................................... 172 6.2 Considerations for epigenetic studies: limitations and future directions ............. 174 6.2.1 Understanding the mechanisms driving RDEOs ........................................... 174 6.2.1.1 Interpretability of EWAS findings ........................................................... 174 6.2.1.2 Mechanisms: why and how ................................................................... 174 xii 6.2.2 Cell type and tissue specificity ................................................................... 176 6.2.2.1 Challenges with studying the cell types of relevance ............................... 176 6.2.2.2 Differences in in vivo and ex vivo experimental findings ........................... 177 6.2.2.3 How to study the ârightâ cell populations moving forward? ...................... 178 6.2.3 Epigenetics as a key marker of cell type in future immunology research ........ 179 6.3 Concluding remarks ....................................................................................... 180 REFERENCES ........................................................................................................... 182 xiii List of Tables Table 1.1 Known RDEOs and corresponding genetic origins and functional targets. .... 17 Table 2.1 List of antibodies used in two different flow cytometry panels. .................... 43 Table 2.2 Immunophenotyping of Index patient lymphocytes at age 11 years. ............. 69 Table 3.1 Cohorts and demographics information of HC whole blood samples in epigenome-wide association study. ............................................................................. 88 Table 3.2 Cohorts and demographics information of HC PBMC samples in epigenome-wide association study. ............................................................................................... 89 Table 3.3 Cohorts and demographics information of HC buccal swab samples in epigenome-wide association study. ............................................................................. 89 Table 3.4 Number of DMs identified with three statistical approaches. ...................... 96 Table 4.1 Median clinical interval of cell populations (10th to 90th percentile) in 5-year-old healthy individuals. .............................................................................................. 113 Table 4.2 Cellular composition and dataset included for the three reference datasets we compiled in this study, UniBlood7, UniBlood13, and UniBlood19. ........................... 119 Table 4.3 Pair-wise comparison of prediction error pooled over all references. ......... 124 Table 4.4 Percentage of median predicted cell populations that fell within the clinical intervals, out of the categories predicted. ................................................................... 129 Table 5.1 Available Reference-Based ISCH Prediction Algorithms and Reference Datasets with Corresponding Packages, Compatible Tissues, and Required Input Types152 Table 5.2 Reference-Free ISCH Prediction Algorithms ............................................. 162 xiv List of Figures Figure 1.1 A tortoiseshell catâan example of epigenetic regulation. ............................. 1 Figure 1.2 Schematic of Epigenetic Regulatory Mechanisms Associated with RDEOs: Chromatin Remodelers, Histone Modifications, and DNA Methylation. ........................... 16 Figure 2.1 Clinical timeline of a patient with biallelic ASXL1 variants. .......................... 47 Figure 2.2 The index patientâs hematological parameters over time. ........................... 48 Figure 2.3 Extended immune phenotypes of the index patient. ................................... 48 Figure 2.4 Image of healing cutaneous granuloma on left knee taken at 14 years old. ... 49 Figure 2.5 Skin biopsy performed at 9 years old from the left thigh. ............................. 49 Figure 2.6 Bone marrow biopsy from the index patient. .............................................. 50 Figure 2.7 Micrograph of lymph node from the index patient. ...................................... 51 Figure 2.8 Characterization of germline biallelic ASXL1 variants in the patient. ............ 52 Figure 2.9 Predicted CADD score (v1.6) and allelic frequency of ASXL1 variants reported in the gnomAD database (v3). ...................................................................................... 52 Figure 2.10 A schematic of major functional domains in ASXL1 and the positions of patient and other reported germline variants................................................................. 53 Figure 2.11 ASXL1 protein abundance in expanded CD4 and CD8 T cells. .................. 54 Figure 2.12 ASXL1 protein abundance in cultured fibroblasts. ................................... 54 Figure 2.13 H2AK119ub abundance in expanded CD4 and CD8 T cells. ...................... 55 Figure 2.14 A schematic of the experimental setup to assess the effect of each of the patientâs ASXL1 variants on ASXL1 protein abundance. ................................................. 55 Figure 2.15 ASXL1 protein abundance in transfected HEK293 cells. ........................... 56 Figure 2.16 A volcano plot of differential DNAme between the index patient and HCs in whole blood, PBMCs, and buccal swabs. ..................................................................... 57 Figure 2.17 Venn diagram showing the overlap of differentially methylated sites with Padj < 0.05 in whole blood and PBMCs. ............................................................................... 58 xv Figure 2.18 Venn diagram of differentially methylated loci with Bohring Opitz syndrome-associated DNAme signature....................................................................................... 58 Figure 2.19 Chromatin state enrichment analysis in whole blood and PBMCs. ........... 59 Figure 2.20 Predicted PedBE epigenetic age in the index patient and HCs. ................. 60 Figure 2.21 Horvath pan-tissue EA and predicted telomere length of the index patient and HCs in whole blood and PBMCs. ............................................................................ 61 Figure 2.22 Telomere length of patient lymphocytes and granulocytes. ...................... 61 Figure 2.23 GFP and ASXL1 abundance in HC, patient, and rescued expanded T cells. 62 Figure 2.24 PCA of DNAme landscape in HC, patient, or rescued expanded T cells. .... 63 Figure 2.25 Heatmaps of DMs between HC and patient expanded T cells. .................. 64 Figure 2.26 Venn diagram of the DMs identified in expanded T cells. .......................... 64 Figure 2.27 Chromatin state enrichment analysis in expanded T cells. ....................... 65 Figure 2.28 Heatmaps of enriched pathways based on DE in cultured T cells. ............ 66 Figure 2.29 Higher EA in expanded patient-derived T cells was rescued by WT ASXL1. . 67 Figure 2.30 T cell phenotyping showing altered T cell compartments in the patient. .... 68 Figure 2.31 Quantification of T cell compartments in the index patient and HC based on clinical flow cytometry. ............................................................................................... 69 Figure 2.32 PD1+ cell populations in primary CD4 and CD8 memory T cells of HCs and the patient based on clinical flow cytometry. ................................................................ 70 Figure 2.33 Quantification of PD1+ cell populations in the naïve, TCM, TEM, and TEMRA compartments for CD4 and CD8 T cells based on clinical flow cytometry. ...................... 71 Figure 2.34 B cell phenotyping of the patient and HCsâ PBMC.................................... 71 Figure 2.35 Circulating Tfh cell profiling in the patient and HCsâ PBMC....................... 72 Figure 2.36 Characterization of CD4+ memory T cells in the patient and HCsâ PBMC. . 73 Figure 2.37 Further profiling of Tfh cell function in terms of PD1 expression................ 74 Figure 2.38 CD57+ cell populations in primary CD4 and CD8 memory T cells of HCs and the patient based on clinical flow cytometry. ................................................................ 75 xvi Figure 2.39 Quantification of CD57+ cell populations in the naïve, TCM, TEM, and TEMRA compartments. ................................................................................................ 76 Figure 2.40 Relative proliferative capacity of expanded CD4 and CD8 T cells. ............. 76 Figure 2.41 Profiling of pSTAT5 abundance in expanded T cells. ................................. 77 Figure 2.42 ROS profiling of the patient and HCsâ expanded T cells. ........................... 78 Figure 2.43 CD3+CD56+ cell populations in the patient and HCsâ PBMC. ................... 80 Figure 2.44 DNAme profiles of NCAM1 gene............................................................. 80 Figure 2.45 Expression level of NCAM1. ................................................................... 80 Figure 2.46 NK cell phenotyping of the patient and HCsâ PBMC. ................................ 81 Figure 3.1 Volcano plots of DM signature detected in whole blood. ............................. 97 Figure 3.2 Volcano plots of DM signature detected in PBMC. ...................................... 98 Figure 3.3 Volcano plots of DM signature detected in buccal swabs. ......................... 100 Figure 3.4 Distribution of the number of significant DMs (with 5% delta beta cutoff) yielded by three statistical tests in whole blood, PBMC, and buccal swabs. .................. 101 Figure 3.5 Venn diagram showing the numbers of DMs (with 5% delta beta cutoff) identified in the patientâs whole blood, PBMC, and buccal swabs. ................................ 102 Figure 4.1 Schematic diagram of the standard reference-based DNAme cell type prediction pipeline and options evaluated in the optimization procedure. ..................... 111 Figure 4.2 Principal component analysis showing the main patterns of DNAme variation in sorted cord and adult peripheral blood cell populations. .......................................... 115 Figure 4.3 Principal component analysis showing the main patterns of DNAme variation in sorted blood cells and whole blood samples. .......................................................... 117 Figure 4.4 Violin plot of absolute prediction error of cell type proportions across normalization methods and reference panels in CHILD age 5 samples. ........................ 122 Figure 4.5 Violin plot of CEYGO score across normalization methods and reference panels in CHILD age 5 samples. ................................................................................. 123 Figure 4.6 Correlation of absolute prediction error of cell type proportions and CETYGO score for age 5 CHILD samples. ................................................................................. 125 xvii Figure 4.7 Age-specific clinical interval of hematological fractions and predicted cell type proportions of CHILD age 5 blood samples (quantile normalized). ........................ 127 Figure 4.9 Estimated blood cell proportions in age 5 CHILD samples across Extended, UniBlood13, and UniBlood19 reference panels. .......................................................... 131 Figure 4.10 Estimated blood cell proportions with the UniBlood19 reference panel for samples across age groups. ...................................................................................... 132 Figure 4.11 DNAme variance explained by cell type PCs in age 5 CHILD samples using quantile normalization. ............................................................................................. 133 Figure 4.12 Absolute prediction error of cell type proportions of age 5 CHILD samples across machine-learning-based feature selection methods based on IDOL reference. .. 135 Figure 4.13 Absolute prediction error of cell type proportions of age 5 CHILD samples across feature selection methods based on UniBlood19 reference. ............................. 136 Figure 4.14 Mean absolute error of predicted cell type proportions of age 5 CHILD samples across varying number of available features (k) for each feature selection method based on IDOL reference. .......................................................................................... 137 Figure 4.15 Mean absolute error of predicted cell type proportions of age 5 CHILD samples across varying number of available features (k) for each feature selection method based on UniBlood19 reference. ................................................................................ 137 Figure 4.16 Absolute prediction error of cell type proportions across regression methods in age 5 CHILD samples. .............................................................................. 138 Figure 4.17 Absolute prediction error of cell type proportions in reserved CHILD age 1 samples across deconvolution pipelines. ................................................................... 139 Figure 4.18 Absolute prediction error of cell type proportions in reserved CHILD cord blood samples. ......................................................................................................... 140 Figure 4.19 Deconvolution prediction results for CHILD cord blood across methods. 141 Figure 4.20 Absolute prediction error of cell type proportions in an external validation adult whole blood dataset (GSE112618). .................................................................... 142 Figure 5.1 Decision scheme for examining ISCH in DNAme studies. .......................... 149 Figure 5.2 Flowchart of reference-based cell type (CT) proportion prediction for estimating ISCH in heterogeneous tissues. ................................................................. 155 xviii Abbreviations 5-aza 5-azacytidine 5hmC 5-hydroxymethylcytosine 5mC 5-methylcytosine ADD ATRX-DNMT3-DNMT3L domain AML Acute myeloid leukemia ASXL1 Additional sex combs like 1 BLR Boosted logistic regression BMIQ Beta-mixture quantile BOS Bohring-Opitz syndrome CADD Combined annotation-dependent depletion CART Classification and regression tree CBC Complete blood count CeDAR Cell-type-specific Differential Analysis using Regression CellDMC Cell type-specific Differential Methylation Calling CETYGO Cell type deconvolution goodness CGI CpG island CHILD cohort Canadian Healthy Infant Longitudinal Development cohort CH Clonal hematopoiesis CMML Chronic myelomonocytic leukemia CP Constraint projection CpG Cytosine-guanine dinucleotide csDM Cell-type-specific differential DNA methylation CTCF CCCTC-binding factor CyTOF Cytometry by time of flight DE Differential expression xix DM Differential DNA methylation DMV DNA methylation valley DNAme DNA methylation DNMT DNA methyltransferase DHS DNase hypersensitive sites DTA mutations DNMT3A, TET2, and ASXL1 DOHaD Developmental Origins of Health and Disease DSB Double-strand break EA Epigenetic age EBV EpsteinâBarr virus EN Elastic net EPIC array Illumina Infinium MethylationEPIC BeadChip array ES Effect size ESC Embryonic stem cell EV Empty vector EWAS Epigenome-wide association study FACS Fluorescence-activated cell sorting FDR False discovery rate GBM Gradient boosted machine GEO Gene Expression Omnibus gnomAD Genome Aggregation Database GoF / LoF Gain of function / Loss of function GVHD Graft Versus Host disease GxE Gene-by-environment HDAC Histone deacetylase HM450K array HumanMethylation450 BeadChip array H2AK119ub Monoubiquitination of lysine 119 on histone H2A xx H3K27me3 Tri-methylation of lysine 27 on histone H3 H3K36me2/3 Di-/Tri-methylation of lysine 36 on histone H3 H3K4me3 Tri-methylation of lysine 4 on histone H3 HC Healthy control HSC Hematopoietic stem cells HSCT Hematopoietic stem cell transplantation ICF Immunodeficiency, centromeric instability, and facial anomalies syndrome IDOL Identifying Optimal Libraries IEI Inborn error of immunity IL Interleukin IOW Isle of Wight iPSC Induced pluripotent stem cell IRES Independent ribosomal entry site ISCH Inter-sample cellular heterogeneity IVIG Intravenous immunoglobulin KO Knock out LDN Low density neutrophils LOOCV Leave-one-out cross validation MAE Mean absolute error meQTL Methylation quantitative trait locus MBD Methyl-CpG-binding domain MDS Myelodysplastic syndrome MeCP2 Methyl CpG binding protein 2 MFI Mean fluorescence intensity MHC Major histocompatibility complex MMR Measles, mumps, and rubella xxi NHEJ Nonhomologous end-joining NK cell Natural Killer cell OSKM Oct3/4, Sox2, Klf4, and cMyc PAMP Pathogen-associated molecular pattern PBMC Peripheral blood mononuclear cells PC(A) Principal component (analysis) PD1 Programmed cell death protein 1 PedBE clock Pediatric buccal epigenetic clock PRC1/2 Polycomb repressive complex 1 and 2 PR-DUB Polycomb repressive deubiquitinase complex PWWP Pro-Trp-Trp-Pro domain (p)STAT5 (Phosphorylated) Signal transducer and activator of transcription 5 QC Quality control RD(EO) Rare disease (of epigenetic origin) RF Random forest (sc)RNA-seq (Single-cell) RNA sequencing ROS Reactive oxygen species RPC Robust partial correlation SLEA Sample level enrichment analysis SVR Support vector regression TAD Topologically associating domain TCA Tensor Composition Analysis TET Ten-eleven translocation methylcytosine dioxygenases TCM/TEM/TEMRA Central/effector/effector memory T cells (T cell subsets) TF Transcription factor Tfh T follicular helper cell TLR Toll-like receptor xxii Treg Regulatory T cell UHRF1 Ubiquitin-like, containing PHD and RING Finger domains 1 UDR Ubiquitin-dependent recruitment domain V(D)J Variableâdiversityâjoining WES Whole exome sequencing WGS Whole genome sequencing WT Wild type XCI X-chromosome inactivation xxiii Acknowledgements I am fortunate to be able to work and live in the beautiful Vancouver of British Columbia, Canada, at the unceded territory of the Coast Salish Peoples, including the territories of the xÊ·mÉÎ¸kÊ·ÉyÌÉm (Musqueam), SkwxwúÊmesh (Squamish), Stó:lÅ and SÉlÌílwÉtaÊ (Tsleil-Waututh) Nations. I would like to express my deep gratitude to Dr. Michael S. Kobor for his supervision of my graduate study. Mike provided me the intellectual freedom to explore my interest in science and dictate the direction of my PhD projects. He has also provided incredible support in facilitating collaborations and securing data access, so that I can enhance my projects. This experience has strengthened my critical thinking and communication skills, and some of the credits must be given to Mike for fostering the positive learning environment that is the Kobor Lab. I would also like to thank all the people who have provided me with intellectual feedback on my work, and helped me grow as a scientist. This includes my thesis committee members, Dr. Keegan Korthauer, Dr. William T. Gibson, and Dr. Eldon Emberly. For my wonderful collaboration experience, I would like to thank Catherine Biggs and Dr. Stuart Turvey. I never could have understood immunology without this incredible research opportunity. I am also truly grateful for everyone in the Kobor Lab, so many of whom mentored me on everything from epigenetics, bioinformatics, to scientific writing. This includes Dr. Sarah Merrill, who supported me in every way, and I never could have done my PhD without her. This also included Dr. Nicole Gladish, Dr. Samantha Schaffner, and Dr. David Lin, who guided me in my early graduate study years. I would be remiss not to acknowledge the incredible team who constantly inspire me scientifically and are simply the most wonderful friends, Dr. Hilary Brewis, Paola Arguello Pascualli, Hannah-Ruth Engelbrecht, Dr. Karlie Edwards, Erick Navarro-Delgado, Dr. Meingold Chan and everyone else in the Kobor Lab. Thank you all. Last but not least, I would like to thank the patient whose case I described in Chapter 2 of the thesis. They are an incredible and creative person. I am grateful to them and their xxiv family for consenting to the research, making my PhD thesis possible. I hope this work is helpful not just for the patient and their family, but to the scientific community at large and other patients who might benefit from this research as well. xxv Dedication To my mom, who worked tirelessly her whole life to give Angela, Brian and I the opportunities to receive the best education. To Angela and Tobin, thank you for being there for me and being amazing friends. Your brilliance, diligence, and compassion inspire me daily. To Brian, my bright young brother, my hope to be a good role model for you propels me forward. To Lithium (a.k.a. Lily) and Potassium (a.k.a. Poppy), for always being there for me as moral support and for constantly reminding me that life and science are fascinating. Finally, to Mehul, my partner, thank you for supporting me through my long PhD journey with incredible patience. You motivated me to be a better scientist, as well as a healthier and kinder person. I am glad to have you all in my life. 1 1 Chapter 1: Introduction 1.1 The molecular basis of epigenetic modifications Each cell in the human body, for the most part, has an identical primary DNA sequence. Yet, distinct cell populations serve unique functions that contribute to the full operation of the organism in aggregate. The regulation of the gene expression pattern and the synchronization of it across cells, tissues, and developmental milestones are essential to healthy development4. One can theoretically regard typical development as the coordinated generation, organization, and death of specific cell populations. Epigenetic processes play a crucial role in orchestrating this coordination5â9. One infamous visual example of epigenetic regulation is the random fur coloring of a tortoiseshell or calico cat (Figure 1.1). Figure 1.1 A tortoiseshell catâan example of epigenetic regulation. A picture of my catâPotassium a.k.a. Poppy. 2 In 1957, Waddington analogized the cell fate decision to a ball rolling down a landscape with multiple valleys10,11. The ball starts with infinite potential. However, once down a certain path, it experiences lateral inhibition and is unable to de-differentiate. The image served as a foundation for understanding the role of epigenetics in establishing and maintaining cellular identity11,12. While a wealth of research observed the co-occurrence of differentiation and altered epigenetics5,6,13â16, whether the marks are driving the cell fate decision and the underlying mechanisms remain rather opaque. Today, epigenetics is typically defined by its role in maintaining cellular memory; it encompasses an array of mitotically heritable modifications to chromatin that can perpetuate distinct gene activity states12,17. In either case, epigenetic regulation is undeniably important for human development. During the cell differentiation process in early development, epigenetic patterns serve as âepigenetic barriersâ to preserve cellular identity and maintain gene expression profiles18â21. Disruption of normal epigenetic regulation reshapes the differentiation landscape22. Furthermore, both knockout models and germline variants in human patients have demonstrated that dysfunctions in key epigenetic regulators or regulatory components can lead to disorders and cancers1 (section 1.4). In the following sections, I will introduce the functions of some key epigenetic regulatory components â DNA, histone, and chromatin modifications. Specifically, I will focus on DNAme methylation (DNAme) and its roles in early development and cell functions, as well as the consequences of dysregulation of the epigenetic machinery. 1.1.1 Functions of DNA methylation â the knowns and the unknowns DNAme is one of the most well characterized epigenetic markers. It is the covalently bound methyl group to the 5â carbon of cytosine (5mC), primarily occurring in the context of cytosine-guanine dinucleotides (CpGs)23. The modification can be directly measured and quantified with microarray or sequencing-based techniques at base-pair resolution, thereby making DNAme an attractive target for epigenetic profiling24,25. 3 Intriguingly, DNAme as a feature is absent in many model organisms, including S. cerevisiae, C. elegans, and D. melanogaster26. While the lack of conservation indicates that the modification is not essential for life, its pervasiveness throughout evolution, all the way from E. coli to H. sapiens, suggests that DNAme confers a unique evolutionary advantage26. Further supporting this hypothesis are the mutagenic properties of DNAme. Upon spontaneous deamination, 5mC is converted to uracil and passed down as thymine27,28. Indeed, the deamination process has also been linked to missense and nonsense mutation-driven human inherited diseases29. The fact that many species evolved along with this genetic risk further suggests the utility of DNAme. DNAme has been proposed as a mechanism of transcriptional regulation, but its role is nuanced30. While earlier studies identified an anticorrelation of promoter DNAme with gene expression, recent work suggests the relationship is complex and context-dependent31â36. CpG islands (CGI), genomic regions characterized by high GC content, high CpG density, and low DNAme levels, show around 50% overlap with gene promoters and 25% overlap with gene bodies6,37,38. In differentiated cells, CGIs are commonly found in the promoters of constitutively expressed housekeeping genes39. Fewer than 3% of variable CpGs are reported in promoters, suggesting these promotersâ DNAme levels are highly invariable, in line with the stable expression of these genes40. Developmental and transcription factor-related genes show high overlap with DNAme valleys (DMVs), which are multi-kilobase regions containing one to several CGIs13,41. DMVs are also largely unmethylated, despite the active regulation of corresponding genes under bivalent chromatin states6,13. Overall, highly methylated promoters are associated with transcriptional repression, whereas unmethylated promoters can exhibit a range of transcriptional states, demonstrating that promoter DNAme is often sufficient but not necessary in transcriptional silencing30,42â44. Outside of promoters, the literature proposed that DNAme in other genomic contexts is also relevant to transcriptional regulation35,45â48. For example, DNAme in the first intron has been reported to negatively associate with expression level46. In contrast, DNAme in non-CGI-containing gene bodies tends to positively correlate with expression34,45,49. Distal 4 regulatory elements, such as enhancers, also demonstrate dynamic DNAme commonly ranging from 10% to 50%50. Enhancers tend to be CpG-poor and show high overlap with lineage-specific differentially methylated regions6,51,52. With the aid of cohesion and CCCTC-binding factor (CTCF), distal regulatory components are arranged into topologically associating domains (TADs) that facilitate promoter-enhancer interaction53â55. DNAme within the CTCF-binding motif actively modulates CTCF affinity, establishing another link between transcriptional regulation and DNAme56,57. Several mechanisms of action have been proposed for DNAmeâs role in the perpetuation of distinct transcriptomic states35. The literature revealed that gene body DNAme modulates alternative splicing-mediated transcriptional regulation58â61,49. Moreover, the transcriptional repression at highly methylated CGIs has been attributed to the mediatory effect of methyl-binding domain (MBD)-containing proteins, which recognize and bind specifically to 5mC. These 5mC-binding MBD proteins show high affinity to CpG-rich and highly methylated genomic regions, and recruit histone deacetylases (HDACs) to reinforce transcriptional repression (section 1.1.4)62,63. Alternatively, DNAme can influence gene expression through mediating transcription factor (TF) binding. Systemic in vitro screening demonstrated that the majority of TFs show differential affinity to their binding motifs depending on the DNAme status, with some leading to decreased and others to increased affinity64â68. Furthermore, recent studies suggest that the link between DNAme and TF binding, heavily influenced by genetic variants, underlies DNAmeâs role in transcriptional repression69,70. Through a range of direct and indirect means, DNAmeâs involvement in transcriptional control leads to its vital function in several key regulatory mechanisms, including X-chromosome inactivation (XCI)71â73, genomic imprinting74â77, suppression of spurious transcription from cryptid promoters78â82, transposable elements81,83,84, and satellite repeats69,85,86. As these regulatory processes are precisely controlled, especially in early development, DNAme, in concert with other epigenetic modifications, plays a pivotal role in human development and disease (further explored in section 1.2)5,6,13. 5 1.1.2 DNA methylation â writers and erasers Delineating the mechanisms underlying DNAme regulators and effectors facilitates our understanding of DNAme dynamics in typical development and disease conditions. While DNAme levels can be affected by a wide array of proteins, they are directly modulated by their writers â DNA methylation transferase (DNMT) â and erasers â ten-eleven translocation (TET) methylcytosine dioxygenases87,88. The 5mCs are recognized by their readers, including MBD-binding proteins and TFs35,64,69. The mirroring of the CG pairs allows for maintenance of methylation state across cell cycles. Specifically, DNMT1 is largely attributed as the protein responsible for DNA methylation on the nascent strand post replication89,90. E3 ubiquitin-protein ligase UHRF1 facilitates this process via the binding to hemi-methylated CpG at the replication fork and ubiquitinating either histone 3 (H3) or PAF15, a co-factor associated with the DNA polymerase91â94. The ubiquitination products then mediate the recruitment and activation of DNMT1 to maintain DNAme level92,95,96. In contrast to the CpG dinucleotide, non-CpG DNAme is mainly regulated by DNMT3A and 3B, which are also responsible for de novo DNAme during development25,97â99. DNMT3A/B and the catalytically inactive DNMT3-like protein (DNMT3L) all share the conserved methyltransferase (MTase) domain, and the chromatin reading PWWP and ATRX-DNMT3-DNMT3L (ADD) domains6,87. Two copies of DNMT3A or 3B form a tetrameric complex with two DNMT3Ls to methylate cytosines on both DNA strands100â102. The DNMT3s are responsible for establishing the pattern of DNAme during germ cell development and early embryogenesis21,103â105. However, DNMT3A and 3B have been reported to function in different contexts, with DNMT3A as the main de novo DNMT in somatic cells and DNMT3B isoforms as the main de novo DNMTs for re-methylation post-implantation6,106,107. In addition to spontaneous deamination, DNAme can be actively removed with TET proteins. The TET protein family is responsible for the iterative oxidation of 5mC to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine, and then 5-carboxylcytosine, which can eventually be removed by thymine DNA glycosylase-mediated base excision repair108,109. 6 TET proteinsâ C-terminus stabilizes the interaction with modified cytosines, with preferential affinity for the 5mCs88. TET proteins are responsible for the rapid demethylation after fertilization and during gastrulation110,111. Furthermore, they have unique roles in neurons, leading to a higher proportion of 5hmC in the brain compared to other tissues, with enrichments in gene body and enhancers112,52,113. 1.1.3 Histone modification and chromatin remodelers DNA nucleotides are wrapped around an octamer of histone proteins to form the basic unit of chromatin, the nucleosome114. Posttranslational modifications, such as acetylation, methylation, phosphorylation, SUMOylation, and ubiquitination, are often found on the N- or C-terminal tails of histones, thereby altering their charge and structure115. This can affect how histones interact with each other, DNA, and various other chromatin-binding proteins. For example, the negatively charged acetyl group neutralizes the electrostatic interaction between the lysine-rich histone tail and the negatively charged DNA backbone, which can lead to looser nucleosome packaging and facilitate increased accessibility of DNA to the transcriptional machinery116. Histone modifications are highly correlated with chromatin states117, nucleosome spacing and positioning118, and DNAme patterns114,119â121. Together, these epigenetic marks maintain transcriptional control throughout the cell cycle and are responsive to environmental stimuli114. One example of a histone modifier that is further explored in Chapter 2 is ASXL1. ASXL1 comprises part of the Polycomb repressive deubiquitinase (PR-DUB) complex, which along with Polycomb repressor complexes 1 and 2 (PRC1/2) represses target genes by histone and downstream chromatin modifications122. In embryonic development, the establishment of histone H2A lysine 119 monoubiquitination (H2AK119ub) by PRC1 is essential for PRC2 recruitment, which is responsible for H3K27me3 modifications, thereby bolstering transcriptional repression123â126. The process is also crucial for the establishment of chromatin bivalency, whereby H3K4me3 and H3K27me3, histone modifications associated with transcriptional activation and repression, are present simultaneously, marking genomic regions poised to switch between active and repressed 7 transcriptional states upon stimulation127â129,48. The PR-DUB complex removes H2AK119ub at later developmental stages to maintain transcriptional regulation48,124,127,130. ASXL1 is critical for the function of PR-DUB131â133. Therefore, impaired ASXL1, and consequently altered PR-DUB activity, lead to perturbations in H2AK119ub deposition, PRC1 localization, developmental genesâ expression, and lineage differentiation127,133â136. ATP-dependent chromatin remodeling complexes comprise another major class of epigenetic regulators. There are four main families of chromatin remodelers in eukaryotes: SWI/SNF, ISWI, CHD, and INO80137. Complexes in all families share the ability to bind to nucleosomes and break the DNAâhistone interaction138. Some remodeler families exhibit more specialized functions, such as the SWI/SNF family complexes that are responsible for nucleosome sliding and increasing DNA accessibility139. Other remodeler families have dynamic functions, such as the INO80 family remodelers, which are involved in histone variant deposition, transcriptional activation, and DNA repair137. Through their modulation of nucleosome organization and chromatin assembly, chromatin remodeling complexes work in tandem with other epigenetic regulators to reinforce the transcriptional state of a cell140. As the chromatin remodelers influence chromatin accessibility, they also play important roles in development, and genetic variants of the members of these families often lead to developmental disorders and malignancies138,139,141,142. 1.1.4 Interplay amongst DNA methylation and other epigenetic modifications The epigenomic modifications and regulators dynamically interact and influence one another119,54,140. For example, DNMT1 activity is contingent on multiple histone marks. UHRF1 binds to H3K9me3 and unmodified H3K4, marks for heterochromatin143â145. Upon recognition of these marks, UHRF1 catalyzes the ubiquitination of H3K18 in mammals, which is necessary for DNMT1 recruitment143,95,93. Similarly, DNMT3A/B recruitments are also closely linked to histone modifications. DNMT3A/Bâs PWWP domains both recognize but demonstrate differential affinities for H3K36me2 and H3K36me3, marks for intergenic and active gene bodies, respectively121,146â151. Alternatively, DNMT3A1 can be recruited to CGI through its recognition of H2AK119ub with its N-terminus ubiquitin-dependent 8 recruitment region (UDR)148. As such, genetic variants driving impaired lysine methyltransferase 3B (KMT3B / NSD1) or PRC1 activities, which are responsible for H3K36me2 and H2AK119Ub respectively, lead to altered DNAme landscape in addition to the aberrations of histone modifications148,149. In contrast, DNMT3A/B targeting is hindered by H3K4 methylation121,140. In the absence of H3K4 methylation, DNMT3Lâs ADD domain binds to the H3 tail, stabilizing its tetrameric complex with DNMT3A/B during de novo methylation101,152. H3K4me2/3 abrogates the interaction and reduces DNAme at these marked genomic regions152â154. As H3K4me3 marks actively transcribed promoters, this mutually exclusive binding pattern contributes to the anticorrelation of promoter DNAme and gene expression151,30,155. Intriguingly, the directionality is not one-way exclusively. Deletion or defective DNMT3A/B drives an expansion of H3K4me3-marked regions, demonstrating that DNAme regulates histone modifications in reverse as well156,157. Similarly, H3K27me3, a mark of facultative heterochromatin, is both inhibited by and an indirect negative regulator of DNAme151,158â160,140. DMVs, which are highly enriched for development and cell-lineage-specific genes, are an example of the combinatory regulation of histone and DNA modifications. DMVs show high overlap to bivalent chromatin and are characterized by unmethylated DNA, H3K4me3, H3K27me3, and H2AK119ub. As an additional layer, chromatin remodelerâ activities also affect the access of DNMT, TET, and histone-modifying proteins, indirectly regulating DNAme and histone modifications114. For example, deposition of the histone variant H2A.Z by the chromatin remodeler SRCAP is negatively correlated with DNAme level across plants and animals161â163. Similarly, DNAme is recognized by MeCP2, which binds to a range of epigenetic modifiers including HDACs and chromatin remodelers to facilitate chromatin condensation and transcriptional repression164â166. This highly interconnected network of epigenetic regulation potentially reflects a built-in biological redundancy to prevent genetic mutations from causing serious health conditions54. 1.2 Roles of DNA methylation in development and cell function 9 1.2.1 DNA methylation in early development Epigenetics serves a crucial role in defining and maintaining cellular state and cell functions in development5,30. Two rounds of epigenetic reprogramming occur after zygote formation, one preimplantation, and another in the primordial germ cell, where DNAme is actively removed by TETs and re-introduced by DNMTs167,168,110,6. In mice, loss-of-function (LoF) mutations of Dnmt3a and Dnmt3b lead to postnatal and embryonic lethality, respectively169. For Dnmt1, both overexpression and LoF mutations also drive embryonic lethality, suggesting the exact control over DNAme dosage and distribution is crucial for embryonic development170,171. Interestingly, these LoF mutations of Dnmt1/3a/3b, while catastrophic when prompted to differentiate, yield minimal effect on cell morphology or proliferation in murine embryonic stem cells (ESCs), despite significantly reduced DNAme170,172,173. ESCs are pluripotent cells capable of developing into embryo proper and a full organism174,175. Dnmt mutant embryos also demonstrate loss of DNAme in cell lineage-committed genes and intragenic cryptic promoters, leading to overexpression from these promoters81. The findings suggest that DNAme plays a unique and essential role in cellular differentiation, in line with the intricate DNAme dynamics from parental gametes, zygotes, blastocysts, epiblasts, to post-implantation embryo6,167. Multiple studies demonstrated that humans are more sensitive to DNAme disturbance in comparison to mice176,86,5. In mice, only homozygous deletion or LoF mutations of Dnmt1 and Dnmt3 drive developmental delay and lethality, whereas heterozygous animals develop in a similar trajectory to their wild-type (WT) counterparts169,170,177. In contrast, heterozygous LoF variants of the DNMTs lead to significant developmental disturbance and often lethality in humans178,86,5,1. Similar findings are demonstrated in vitro. Whereas murine ESCs with homozygous LoF mutations or even triple knock-out (KO) of Dnmt1, Dnmt3a, and Dnmt3b display self-renewing capacity â the ability of a cell to proliferate in the same state â human ESCs with DNMT1 deletion result in rapid cell death170,172,173,176. The discrepancy suggests that human DNAme regulation is potentially more fine-tuned, or there is less built-in redundancy for its control over cellular states. 10 1.2.2 DNA methylation and cell identity In humans, DNAme has been shown to be crucial for the maintenance of somatic progenitor cellsâ self-renewing capacity179. Functional DNMT1 has been shown to be essential for epidermal progenitor cells, which have the capacity to differentiate into more specialized cell populations180. Epigenomic profiling of human ESCs further confirmed that early-stage differentiation of ESCs into lineage-specific progenitor cells is characterized by differential DNAme at CG-rich promoters, with gene expression predominately regulated by PRC-dependent repression6,13. In contrast, later-stage differentiation into somatic-tissue-specific cell types is under dynamic DNAme regulation at CG-poor enhancers13. These findings implicate DNAmeâs role beyond the differentiation process, but also in the maintenance of cellular identity. Treatment of mouse fibroblasts in vitro with 5-Azacytidine (5-aza), a cytosine analog that blocks DNMT activity, leads to erosion of their cellular identity and subsequent differentiation into muscle cells30,181. The expanding recipes for reprogramming of differentiated cell types (e.g. fibroblasts) to pluripotent cells (e.g. induced pluripotent stem cells [iPSCs]) also demonstrate the role of epigenetic control in cell identity182. OSKM (Oct3/4, Sox2, Klf4, and cMyc), the first set of transcription factors demonstrated to have reprogramming capacity, works in concert with PRC2 to repress lineage-specific genes183â185. More recent studies also use modified CRISPR to demethylate master transcription factors such as OSKM or Sox1, thereby achieving increased reprogramming efficiency186,187,182. Overall, DNAme and other epigenetic modifications serve as a reprogramming barrier that facilitates chromatin stability and limits changes in cellular identity once established182,188. The epigenomic signature of distinct cell types and tissues has been well characterized in the literature34,189,75,190,52,191. They facilitate the maintenance of gene expression states within cells and across replications119,114,17,12. These expression profiles constitute a cellular phenotype that is inseparable from its identity, given that cells are not static entities, but are defined by their function. For example, in CD8+ T cell differentiation, as naive cells differentiate into effector cells, the cells exhibit activation of functional effector 11 genes via multiple levels of DNAme and histone modification regulation192. Many studies have highlighted the cell type-specific nature of epigenetic regulation, and it is therefore unsurprising that not just tissues but even their constituent cell types are the most relevant factors driving epigenetic variability in healthy human populations193,194. In fact, DNAme has served a vital function for human immunity195,196. Unmethylated CpG motif is a classic pathogen-associated molecular pattern (PAMP) that activates human Toll-like receptor 9 (TLR9) and downstream immune responses such as cytokine production197,198. This unmethylated CpG motif can be found in bacterial, yeast, and nematode DNA197. The utility of DNAme as a differentiator of host and foreign antigen is another example of DNAmeâs function in defining and informing the source of DNA, either the cell type, the tissue, or even the species. 1.2.3 Developmental Origins of Health and Disease framework After the establishment of DNAme landscape at the epiblast stage, the epigenetic mark remains relatively stable except for the lineage-specific genes6. However, as DNAme is pliable to environmental modifications yet stable over time, it has been proposed as a molecular process that mediates the biological embedding of early life experiences199â204. The Developmental Origins of Health and Disease (DOHaD) hypothesis posits that exposures during sensitive developmental windows, especially in the prenatal environment or within the first years of life, can influence long-term physiological functioning and the health trajectory of a person205,206. DNAme is thus often examined under the DOHaD framework to understand the effect of early-life exposure on human health, usually in the context of gene by environment interactions, as both elements are known to modulate DNAme patterns199,207â211. 1.2.4 Aging, epigenetic clocks, and cellular senescence On the flip side of early development, there is aging. Aging is an ill-defined biological process that involves the accumulation of damage and loss of function over time212,213. Certainly, at a population level, individuals age at varying trajectories with an array of 12 health outcomes. As an objective and quantifiable measure, chronological age is used in this thesis as an estimator of aging. Genome-wide DNAme is at its highest level in mammalian embryos, with a non-linear gradual loss over time and an increase in both intra- and inter-individual variability with age214â218. This increase in variability has been linked to the phenomenon of epigenetic drift, where the epigenetic profile becomes increasingly variable with age as the regulatory machinery fails to faithfully maintain regulation through mitosis219,194,220â222,209. In contrast, some age-associated DNAme signatures are highly consistent across individuals, suggesting some regulatory process underlies the DNAme changes216,223â225. Even though the exact mechanisms driving these age-associated epigenetic changes remain to be investigated, the phenomenon has been harnessed to develop epigenetic clocks â DNAme-based bioinformatics predictors of the biological aging process223,225â230. The clocks have been shown to be useful for exploring drivers of varying aging trajectories and health outcomes in population studies224,225,231. However, as epigenetic age is often confounded by biological sex, genetic ancestry, and immune profile changes, its application and generalizability remain areas of active research225,232â234. In in vitro settings, aging is typically studied in the context of senescence. Cellular senescence is a key feature of aging, defined as the loss of replicative capacity after extended proliferation, and is characterized by telomere shortening, oxidative stress, mutation accumulation, and morphological alterations235â237. Drawing parallels to the aging signatures in human populations, senescent cells exhibit a global loss of DNAme, especially at late replicating domains, and a focal gain of DNAme in CGI-containing promoters along with repression of these corresponding genes238,239. Population-based aging-associated DNAme loci were found predominately in genomic regions that are poorly maintained by DNMT1 and UHRF1 in senescent cells, leading to a preferential DNAme loss in these regions240. Growing evidence implicates this age-associated DNAme loss as a potential protective mechanism. DNAme loss has been shown to drive de-repression of transposable elements and induce viral mimicry, where the immune system recognizes the cell as potentially virally infected241â243. This can trigger innate immune response and 13 apoptosis, thereby restricting excessive replication of senescent and pre-cancer cells243,244. 1.3 Human immune cells in peripheral blood Given the connection between DNAme and cell identity, DNAme of blood cells is often examined to understand immune system dysregulation195,245â247. A brief overview of hematopoietic immune cells and their functions is thus warranted. All immune cells originate from hematopoietic stem cells (HSCs) in the bone marrow, which give rise to two major lineages: the common lymphoid progenitors and the common myeloid progenitors248. Monocytes and granulocytes originate from the common myeloid progenitor249. These cells undergo maturation primarily in the bone marrow before entering the bloodstream249,250. As the most abundant immune cells in peripheral blood, granulocytes are multi-nucleated and the main contributor to innate immunity248,251,252. Granulocytes are composed of neutrophils, eosinophils, and basophils. Neutrophils typically constitute the majority of all white blood cells (WBCs); they are also the rapid responders to bacterial infections253,254. Eosinophils and basophils, in contrast, have relatively low abundance252. Whereas eosinophils defend against parasites and mediate allergic responses, basophils release histamine during hypersensitivity reactions to trigger inflammation and facilitate allergen clearance255,256. Monocytes circulate in the blood but readily migrate into tissues, where they differentiate into macrophages or dendritic cells, contributing to phagocytosis and inflammation257. Dendritic cells also serve as key antigen-presenting cells that present endogenous and exogenous peptides on the surface of major histocompatibility complex (MHC) class I and class II, respectively258,259. Dendritic cells can migrate to lymph nodes and initiate targeted lymphocyte responses, thereby acting as bridges between innate and adaptive immunity257,260. Lymphocytes, T, B, and NK cells arise from the common lymphoid progenitor248. Natural killer (NK) cells act as part of the innate immune system despite being derived from the lymphoid lineage, providing rapid cytolytic activity without antigen-specific priming261. In contrast, B cells mature in the bone marrow, where they undergo two crucial selection 14 processesâ variableâdiversityâjoining (V[D]J) recombination and central toleranceâbefore entering circulation as naïve B cells262. V(D)J recombination refers to the somatic rearrangement of the V, J, and sometimes D gene segments that correspond to the antigen-binding regions of immunoglobulins262. The process facilitates antigen recognition against a wide range of pathogens263. Central tolerance is the process of eliminating autoreactive lymphocytes to prevent the immune systems from targeting self-antigens262. Similar to their B-cell counterparts, T cell precursors migrate and mature in the thymus by undergoing positive and negative selection264. Successful maturation of T cells leads to two subsets: CD4+ T cells recognizing antigens presented on MHC class II, and CD8+ T cells recognizing antigens on MHC class I264,258,259. Mature naïve T cells then enter circulation similarly to naïve B cells264. Naïve B and T cells circulate through secondary lymphoid organs, such as lymph nodes and spleen262,265. Here, as a part of the adaptive immune system, antigen exposure can trigger their activation and differentiation into memory cells, allowing swift and targeted response to the next exposure266. B cells can differentiate into antibody secreting plasma cells or memory B cells266. Memory B cells persist long-term and respond to re-exposure by generating high-affinity antibodies266,267. T cells differentiate into memory CD4+ T cells and memory CD8+ T cells266. Memory CD4+ T cells primarily function as helpers, expressing specific cytokines needed for antigen clearance by other immune cells, whereas memory CD8+ T cells are cytotoxic, targeting infected or malignant cells directly266,268. Together, these diverse cell types form the foundation of hematopoietic immune cell functions. 1.4 Rare diseases of epigenetic origin (RDEOs) Epigenetics is vital for the establishment and maintenance of gene expression profiles specific to the functioning of defined cell populations, and therefore critical for embryonic development6,176. Genetic variants that alter the functions of this regulatory process can thus drive global disruption of epigenetic and transcriptomic profiles, leading to impairments in differentiation and developmental trajectories1,22. These conditions are 15 often rare and characterized as Mendelian disorders, where mutations in a single gene can explain the clinical phenotype269,270. Rare diseases (RDs) are typically defined by a prevalence threshold of 5â76 cases per 100,000 in the population, with a global average incidence for each disease of 4 per 10,00084. Although individually rare, RDs are common in aggregate, with more than 10,000 RDs reported to date together affecting roughly 350 million people worldwide (about 4.4% of the population)85. Historically, however, research and development regarding RDs have been underfunded because of the difficulty of advocacy for small numbers of patients affected by any specific disease269. Some RDs of genetic origin are driven by altered epigenetic regulation and are referred to as RDs of epigenetic origin (RDEOs). Typically, these genetic variants have been identified in the coding regions of epigenetic regulators, altering their protein function and the downstream epigenetic patterns that they establish. RDEOs typically affect patients from an early age and drive significantly altered cellular functions across multiple systems140,271,272. RDEOs therefore often present as a combination of immune, neurological, and physical developmental disorders, perhaps because of the underlying genetic complexity of these aspects of human physiology. While many genes contribute to epigenetic regulation in a broad sense, in this section, I broadly categorize the contributors to RDEOs based on the key epigenetic regulatory elements: DNA methylation, histone modifications, and the activities of chromatin remodelers (Figure 1.2). A list of known RDEOs that have been registered to the OMIM database is included in Table 1.1. They have generally been studied by multiple approaches, including clinical reports of patient phenotypes, diagnostic studies through which candidate genes are selected based on promising variants, bioinformatics to extract biomarkers of epigenome-wide dysregulation, and functional examination of the underlying mechanisms in cell and animal models. These multipronged approaches have led to significant advances in RDEO research. 16 Figure 1.2 Schematic of Epigenetic Regulatory Mechanisms Associated with RDEOs: Chromatin Remodelers, Histone Modifications, and DNA Methylation. Abbreviations: ac, acetylation; me, methylation. 17 Table 1.1 Known RDEOs and corresponding genetic origins and functional targets. The information is curated based on information available on the OMIM database Epigenetic regulation Functional Group Gene Target RDEO OMIM DNA methylation DNA methyltransferases DNMT1 cytosine Hereditary sensory neuropathy type IE (HSANIE) # 614116 Autosomal dominant cerebellar ataxia, deafness, and narcolepsy (ADCADN) # 604121 DNMT3A cytosine Tatton-Brown-Rahman syndrome (TBRS) # 615879 Heyn-Sproul-Jackson syndrome (HESJAS) # 618724 DNMT3B cytosine Immunodeficiency-centromeric instability-facial anomalies syndrome 1 (ICF1) # 242860 methyl-CpG-binding proteins MBD5 modified cytosine Intellectual developmental disorder, autosomal dominant 1 # 156200 MECP2 modified cytosine Rett syndrome (RTT) # 312750 Rett syndrome-associated severe neonatal encephalopathy # 300673 18 X-linked syndromic intellectual developmental disorder-13 (MRXS13) # 300055 X-linked Lubs-type syndromic intellectual developmental disorder (MRXSL) # 300260 methylcytosine dioxygenase TET2 modified cytosine Immunodeficiency-75 (IMD75) # 619126 TET3 modified cytosine Beck-Fahrner syndrome (BEFAHRS) # 618798 CGG repeats FMR1 Fragile X syndrome (FXS) # 300624 Histone modification Lysine-specific methyltransferases (KMT) KMT2A / MLL1 H3K4 Wiedemann-Steiner syndrome (WDSTS) # 605130 KMT2B / MLL2 H3K4 Dystonia 28, childhood-onset (DYT28) # 617284 KMT2C / MLL3 H3K4 Kleefstra syndrome 2 # 617768 KMT2D / MLL4 H3K4 Kabuki syndrome 1 KMT2E / MLL5 H3K4 O'Donnell-Luria-Rodan syndrome (ODLURO) # 618512 KMT2F / SET1A H3K4 Early-onset epilepsy with or without developmental delay (EPEDD) # 618832 19 Neurodevelopmental disorder with speech impairment and dysmorphic facies (NEDSID) # 619056 KMT2G / SET1B H3K4 Intellectual developmental disorder with seizures and language delay (IDDSELD) # 611055 KMT2H / ASH1L H3K36 Autosomal dominant intellectual developmental disorder-52 (MRD52) # 617796 KMT1D / EHMT1 / GLP H3K9 Kleefstra syndrome 1 (KLEFS1) # 610253 EED H3K27 Cohen-Gibson syndrome (COGIS) # 617561 EZH2 H3K27 Weaver syndrome (WVS) # 277590 SUZ12 H3K27 Imagawa-Matsumoto syndrome (IMMAS) / SUV12-related overgrowth # 618786 KMT3A / SETD2 H3K36 Luscan-Lumish syndrome (LLS) # 616831 KMT3B / NSD1 H3K36 Sotos syndrome (SOTOS) # 117550 KMT3G / NSD2 H3K36 Rauch-Steindl syndrome (RAUST) # 619695 SETD5 H3K36 Autosomal dominant intellectual developmental disorder-23 (MRD23) # 615761 20 KMT5B / SUV420H1 H4K20 Autosomal dominant intellectual developmental disorder-51 (MRD51) # 617788 Lysine-specific demethylases (KDM) KDM1A / LSD1 H3K4me1/2, H3K9me1/2 Cleft palate, psychomotor retardation, and distinctive facial features (CPRF) # 616728 KDM3B / JHDM2b H3K9me Diets-Jongmans syndrome # 618846 KDM4B / JMJD2B H3K9/ H3K36me2/3 Autosomal dominant intellectual developmental disorder-65 (MRD65) # 619320 KDM5B / JARID1B H3K4me1/2/3 Autosomal recessive intellectual developmental disorder-65 (MRT65) # 618109 KDM5C / JARID1C/SMCX H3K4me2/3 Claes-Jensen type of X-linked syndromic intellectual developmental disorder (MRXSCJ) # 300534 KDM6A / UTX H3K27me2/3 Kabuki syndrome 2 # 300867 KDM6B / JMJD3 H3K27me2/3 Neurodevelopmental disorder with coarse facies and mild distal skeletal abnormalities (NEDCFSA) # 618505 21 KDM7B / PHF8 H3K9 Siderius-type X-linked syndromic intellectual developmental disorder (MRXSSD) # 300263 histone acetyltransferases (HAT) KAT3A / CREBBP H2A; H2B; H3 Rubinstein-Taybi syndrome (RSTS1) # 180849 Menke-Hennekam syndrome-1 (MKHK1) # 618332 KAT3B / EP300 H2A; H2B; H3 Rubinstein-Taybi syndrome 2 (RSTS2) # 613684 Menke-Hennekam syndrome-1 (MKHK2) # 618333 KAT5 / TIP60 H4; H2A Neurodevelopmental disorder with dysmorphic facies, sleep disturbance, and brain abnormalities (NEDFASB) # 619103 KAT6A / MYST3 / MOZ H3K9 Arboleda-Tham syndrome (ARTHS) # 616268 KAT6B / MYST4 / MORF H3K9 Genitopatellar syndrome (GTPTS) # 606170 SBBYS variant of Ohdo syndrome (SBBYSS) # 603736 KANSL1 H4K16 Koolen-de Vries syndrome (KDVS) # 610443 KAT8 / MYST1 / HMOF H4K16 Li-Ghorgani-Weisz-Hubshman syndrome (LIGOWS) # 618974 22 Histone deacetylase (HDAC) HDAC4 neurodevelopmental disorder with central hypotonia and dysmorphic facies (NEDCHF) # 619797 HDAC6 X-linked dominant chondrodysplasia # 300863 HDAC8 Cornelia de Lange syndrome-5 (CDLS5) # 300882 Ubiquitination UBE2A H2B Intellectual developmental disorder, X-linked syndromic, Nascimento type # 300860 Deubiquitination ASXL1 H2AK119ub Bohring-Opitz syndrome # 605039 ASXL2 H2AK119ub Shashi-Pena syndrome # 617190 BAP1 H2AK119ub Kury-Isidor syndrome # 619762 Chromatin remodeler SWI/SNF family ATRX H3.3 X-linked alpha-thalassemia/mental retardation syndrome (ATRX) # 301040 X-linked intellectual disability-hypotonic facies syndrome-1 (MRXFH1) # 309580 SWI/SNF family - BAF complex and associated factors ARID1B / BAF250B Coffin-Siris syndrome-1 (CSS1) # 135900 ARID1A / BAF250A Coffin-Siris syndrome-2 (CSS2) # 614607 23 SMARCB1 / BAF47 Coffin-Siris syndrome-3 (CSS3) # 614608 SMARCA4 / BRG1 Coffin-Siris syndrome-4 (CSS4) # 614609 SMARCE1 / BAF57 Coffin-Siris syndrome-5 (CSS5) # 616938 ARID2 / BAF200 Coffin-Siris syndrome-6 (CSS6) # 617808 DPF2 / BAF45D Coffin-Siris syndrome-7 (CSS7) # 618027 SMARCC2 / BAF170 Coffin-Siris syndrome-8 (CSS8) # 618362 SOX11 Coffin-Siris syndrome-9 (CSS9) # 615866 SMARCA2 / BRM Nicolaides-Baraitser syndrome (NCBRS) # 601358 Blepharophimosis-impaired intellectual development syndrome (BIS) # 619293 ADNP Helsmoortel-van der Aa syndrome (HVDAS) # 615873 CHD family CHD1 Pilarowski-Bjornsson syndrome (PILBOS) # 617682 24 CHD2 Developmental and epileptic encephalopathy-94 (DEE94) # 615369 CHD5 Parenti-Mignot neurodevelopmental syndrome (PMNDS) # 619873 CHD7 CHARGE syndrome # 214800 CHD8 Intellectual developmental disorder with autism and macrocephaly (IDDAM) # 615032 CHD family - NuRD/Mi-2 complex and associated proteins CHD3 Snijders Blok-Campeau syndrome (SNIBCPS) # 618205 CHD4 Sifrim-Hitz-Weiss syndrome (SIHIWES) # 617159 PHF6 Bo rÌjeson-Forssman-Lehmann syndrome (BFLS) # 301900 ISWI family BPTF / NURF301 Neurodevelopmental disorder with dysmorphic facies and distal limb anomalies (NEDDFL) # 617755 Other HELLS / SMARCA6 Immunodeficiency-centromeric instability-facial anomalies syndromes 4 (ICF4) # 616911 25 CDCA7 Immunodeficiency-centromeric instability-facial anomalies syndromes 3 (ICF3) # 616910 Transcription factor of CDCA7 ZBTB24 Immunodeficiency-centromeric instability-facial anomalies syndromes 3 (ICF2) # 614069 human INO80 complex SRCAP H2A.Z Floating Harbor syndrome (FLHS) # 136140 Developmental delay, hypotonia, musculoskeletal defects, and behavioral abnormalities (DEHMBA) / non-FLHS SRCAP-related neurodevelopmental disorders # 619595 Other epigenetic regulation Cohesin NIPBL Cornelia de Lange syndrome-1 (CDLS1) # 122470 SMC1A Cornelia de Lange syndrome-2 (CDLS2) # 300590 SMC3 Cornelia de Lange syndrome-3 (CDLS3) # 610759 RAD21 Cornelia de Lange syndrome-4 (CDLS4) # 614701 Histone variants HIST1H1E Rahman syndrome (RMNS) # 617537 26 1.4.1 Examples of RDEOs 1.4.1.1 DNMT3A-associated RDEOs DNMT3A is the most commonly mutated gene in patients with hematopoietic malignancies273,274. In humans, germline variants of DNMT3A have been linked to changes in both neurological and physical development. For example, loss-of-function (LoF) variants of DNMT3A lead to decreased global DNAme and altered hematopoiesis in a condition referred to as Tatton-Brown-Rahman syndrome (TBRS; OMIM #615879)275. TBRS is characterized by generalized overgrowth, intellectual disability, a range of neurodivergent phenotypes, distinctive facial features, and an altered hematopoietic landscape178,276. In contrast, gain-of-function (GoF) variants of DNMT3A were shown to cause Heyn-Sproul-Jackson syndrome (HESJAS; OMIM #618724), a very rare condition reported in only three patients to date, all of whom presented with microcephalic primordial dwarfism157. Functional characterization of the disease-associated variant showed altered DNMT3A function preventing binding to di- and trimethylated histone H3 lysine 36 (H3K36me2/3) and driving increased DNAme in regions marked by H3K27me3 and H3K4me3157. The dichotomy of TBRS and HESJAS, driven by DNMT3A LoF and GoF, respectively, represents an intriguing example of RDEO-associated phenotypic heterogeneity. 1.4.1.2 TET2-associated RDEOs TET proteins mediate the active DNA demethylation process in mammals88. Similar to DNMT3A, TET2 mutations have been linked to a wide range of hematopoietic malignancies277,278. In addition to cancer, the role of TET2 dysregulation in the development of RDEOs has been explored. Germline TET2 variants have been reported to be associated with a number of diseases with various levels of severity in humans. In one study, a patient with a heterozygous truncation variant of TET2 presented with delayed developmental milestones in early childhood. Elderly germline carriers of the same allele also exhibited CD8+ T cell exhaustion, skewing toward terminally differentiated effector memory cells re-expressing CD45RA (TEMRA)279. In another study, patients homozygous for LoF TET2 27 variants showed severe immunodeficiency, lymphadenopathy, hepatosplenomegaly, developmental delay, autoimmunity, and B-cell or T-cell lymphoma280. Class-switch recombination defects were also detected in TET2-deficient B cells280. Elevated levels of DNAme were observed along with decreased DNA hydroxymethylation in both heterozygous and homozygous TET2 LoF cases279,280. Furthermore, a rare variant burden analysis showed that rare heterozygous TET2 variants were strongly enriched in individuals with neurodegenerative diseases281. These observations link heterozygous TET2 variants to long-term epigenetic dysregulation, leading to altered immune profiles and neurodegeneration later in life. In contrast, homozygous LoF variants of TET2 result in drastic alterations in early life that lead to extreme immunodeficiency. Taken together, these observations highlight the role of TET2, as well as the effects of the timing and dosage of its expression, in the regulation of hematopoiesis and neuronal specification. 1.4.1.3 PRC2-related overgrowth syndromes PRC2 is one of the best-studied epigenetic writers and is responsible for H3K27me1/2/3125,282â284. The PRC2 core complex is composed of four core subunits, i.e., EZH1/2, EED, SUZ12, and RBBP4/7 284, and nonconservative variants in these complexes have been shown to cause PRC2-related overgrowth syndromes: Weaver syndrome is linked to EZH2 variants285 (OMIM #277590), CohenâGibson syndrome is related to EED variants286 (OMIM #617561), and SUZ12-related overgrowth is linked to SUZ12 variants287 (OMIM #618786). Each of these PRC2-related overgrowth syndromes presents with tall stature, macrocephaly, advanced bone age, and intellectual disability, and Weaver syndrome and SUZ12-related overgrowth have been linked to increased risk of hematological malignancies and other cancers 287â289. The importance of PRC2 in establishing transcriptional silencing in key developmental genes, such as the Hox gene clusters, can at least in part explain the correlation between LoF variants in these genes and overgrowth phenotypes290. These phenomena highlight the nonredundancy of PRC2 subunits in facilitating histone modifications. 28 1.4.1.4 Bohring-Opitz syndrome As ASXL1 comprises part of the PR-DUB complex122, it plays a critical role in development. De novo heterozygous nonsense or frameshift ASXL1 variants leading to a loss of the C-terminus PHD finger domain cause Bohring-Opitz syndrome (BOS). BOS is a neurodevelopmental disorder characterized by severe developmental delay, microcephaly, failure to thrive, and distinct facial features291â293. While Asxl1-null mice recapitulate BOS phenotypes, heterozygous germline deletion mice exhibit immune dysfunction without any notable developmental impediment134,294. Despite the well-characterized role of ASXL1 in H2AK119 deubiquitination and the consequential dysregulation of bivalent histone marks127,124,295â297, the existing literature on BOS patient samples failed to observe significant differences in bulk H2AK119ub, H3K4me3, or H3K27me3 levels136. The study also did not observe a decrease in ASXL1 abundance in the BOS patients, suggesting that the BOS-associated ASXL1 variants might act in a dominant-negative fashion. Interestingly, differential DNAme patterns and increased epigenetic age has been observed in these BOS patients, highlighting the contribution of altered epigenetic landscape in BOS phenotypes136,298. 1.4.1.5 Immunodeficiency with centromeric instability and facial anomalies syndrome (ICF) Immunodeficiency with centromeric instability and facial anomalies syndrome (ICF) exemplifies the heterogeneity of genetic causes and clinical presentation of RDEOs. ICF is characterized by chromosomal instability, global developmental delay, and humoral immune deficiency that results in recurrent and often fatal infections299,300. The molecular hallmarks of ICF include chromosomal deletions or duplications, heterochromatin decondensation, and centromeric breakage301. ICF has been linked to hypomorphic variants in four genes: DNMT3B (ICF1; OMIM #242860), ZBTB24 (ICF2; OMIM #614069), CDCA7 (ICF3; OMIM #616910), and HELLS (ICF4; OMIM #616911)302,303. Whereas DNMT3B directly modifies DNA methylation status, CDCA7 and HELLS are chromatin remodelers. Despite the common etiology of low DNAme at pericentromeric satellite 2 and 3 repeats304, ICF2, 3, and 4 are driven by defects in chromatin remodelers or their expression; CDCA7 29 and HELLS form an ATP-dependent nucleosome remodeling complex that catalyzes nucleosome sliding305, while ZBTB24 is a transcriptional regulator that binds directly to the CDCA7 promoter and activates its transcription305,306. Mechanistically, the functions of these three proteins are intertwined, thus explaining the similar clinical phenotypes associated with mutations in their genes. ATP-dependent nucleosome remodeling complexes and their roles in maintaining nucleosome accessibility have been implicated in DNA double-strand break (DSB) repair307,308. One type of DSB repair, nonhomologous end-joining (NHEJ), is crucial for B-cell maturation and class-switch recombination. Hypo- or agammaglobulinemia in patients with ICF2â4 can be driven by the NHEJ defect. In contrast, while DNMT3B has not been directly implicated in NHEJ, it has been shown to be critical in restricting DNA damage-induced DSB309. These observations suggest that the mechanism underlying DNAme dysregulation in ICF2â4 may be different from that in ICF1310. The dimorphism in regulatory processes between ICF1 and ICF2â4 despite the shared clinical phenotypes presents a challenge in the study of RDEOs. 1.4.2 Inborn errors of immunity, cancer, and clonal hematopoiesis RDEOs can also manifest as monogenic inborn errors of immunity (IEIs), which are a group of more than 550 rare diseases caused by deleterious germline variants driving immune dysfunction311. ICF, for example, is an RDEO that is also characterized as an IEI. The overlapping spectrum is often driven by epigenetic dysregulation-induced disruption in hematopoiesis and cellular differentiation pathways22,312. The perturbation in immune function can give rise to inflammation, lymphoproliferation, and increased susceptibility to infections and cancer280,313â315. The same mechanisms underlying immunodeficiency in certain IEIs may also promote oncogenesis in a cell-intrinsic manner, such as DNA repair or epigenetic defects, causing genomic instability314. Hematologic malignancies account for the majority of cancers in IEIs and are mainly of lymphoid origin, however, myeloid leukemias can also occur313. Alternatively, myelodysplastic syndromes (MDS) are sometimes observed in association 30 with IEIs as well, which are a subgroup of chronic hematological malignancies characterized by ineffective hematopoiesis leading to cytopenia (i.e. a reduction of certain populations of mature blood cells), with roughly one third of the cases progressing to acute myeloid leukemia (AML)316,317. Somatic variants in IEI-associated and key epigenetic genes are also observed in hematological malignancies, further supporting their oncogenic or tumor suppression role5,6,318. Irrespective of the cancerâs origins, an altered epigenetic landscape is a hallmark of cancer319. Similar to the aging trajectory, this typically presents as global loss of DNAme, predominately in repeat elements and late replicating domains320â323,220. Another feature of cancer is the focal gain of DNAme in typically lowly methylated regions, such as CGIs and DMVs321,324. Aberrant activities of epigenetic regulators potentially contribute to the cancer epigenome. Indeed, acquired variants in the epigenetic regulatory genes DNMT3A, TET2, and ASXL1 (collectively known as DTA mutations) are common in myeloid malignancies such as MDS, AML, and chronic myelomonocytic leukemia (CMML)273,325â328. Furthermore, despite not being directly connected to cancers, DTA mutations have been implicated in age-related clonal hematopoiesis (CH)329â333. CH is the dominance of certain hematopoietic stem cell clones that carry genetic variants that confer expansion advantage331. Age-related CH is common in older populations but is associated with an increased risk of hematological cancers as well as cardiovascular diseases330,334,333. The link between CH and hematologic malignancies remains to be elucidated, with the current evidence alluding to the role of DTA genes in potentiating immune cell stemness and proliferative capacity335â338. 1.5 High dimensional analysis of DNA methylation DNAme is by far the most studied epigenetic mark, often in the context of epigenome-wide association studies (EWAS). EWAS refer to association tests conducted between variables of interest and DNAme differences across the epigenome at a population level339â342. More than 1172 published EWAS have been documented on the EWAS Atlas database alone, 31 reporting DNAme associations on traits including from smoking, body mass index, diseases, to environmental exposures involving 247 tissues or cell lines340,343. The DNAme states of nearby CpGs are highly correlated, and repetitive elements are often difficult to profile344â346. As such, despite the existence of 28 million CpGs in the human genome, only a subset is typically evaluated in EWAS. Roughly 95% of these studies were conducted with high-density microarrays on bisulfite-converted DNA. Amongst those, the Illumina Infinium HumanMethylation450K BeadChip (HM450K) array represents the majority of the published studies, profiling more than 450 thousand DNAme loci on the human genome340,347. In recent years, the Infinium MethylationEPIC BeadChip (EPIC) array has shown an increased adoption rate with its coverage of more than 850 thousand DNAme loci340,348â350. This high-dimensional interrogation of DNAme demands thoughtful experimental design to yield informative findings. For one, multiple test correction of statistical significance is necessary to prevent inflated false positive results351. Additionally, covariates that are known to drive DNAme variability need to be accounted for to accurately assess the association between DNAme and the main variables of interest351. Given the number of tests conducted in an EWAS and the number of covariates in typical models, large sample sizes are often required to yield sufficient statistical power352,353. 1.5.1 Biological variables contributing to DNAme variability in human population As DNAme can be simultaneously altered by multiple biological factors, this plasticity sometimes poses a specific set of challenges in research; namely, it can be difficult to tease apart the source of a specific signal of interest12,353,354. For example, accounting for cellular heterogeneity is one major challenge in EWAS193,353. Aside from cell type heterogeneity, other factors, such as age, sex, and genetic ancestry, are also associated with genome-wide DNAme variation216,355â358. 32 1.5.1.1 Cellular heterogeneity As EWAS studies matured over the past decade, the link between cell type and DNAme patterns is now well characterized3,17,193,359. As cellular composition of samples may vary in heterogeneous tissues, such as blood, differences in DNAme measured between two groups may either reflect average DNAme changes across one or more cell types or simply represent differences in cell type composition between groups193. This cell type composition difference across samples, or intersample cellular heterogeneity (ISCH), can be measured directly or predicted with bioinformatic methods (further explored in Chapter 5). Sometimes, ISCH is a concomitant variable epigeneticists want to control for in their studies, akin to other contributors of interindividual DNAme variability194,360â362. As ISCH can be a potential confounder in the relation between DNAme and the variable of interest, failure to account for ISCH in EWAS might lead to inflated false-positive and false-negative rates193,361. Other times, ISCH can be the outcome of interest, as changes in cellular composition can inform a range of biological signals from physiological stress to disease risks194,363â365. In the cases of RDEOs, many of them present atypical cell differentiation and maturation patterns as well as altered epigenetic states in each cell population280,299,366,367. These phenomena pose unique problems not encountered in genomic research. In heterogeneous tissues, the sources of DNAme variability can be driven by changes in either cell type composition or the epigenetic regulation within a given cell type, or a combination thereof368. Special considerations for study design are necessary to untangle these two effects, such as supplementing bulk tissue analysis with experiments in sorted cells. 1.5.1.2 Age The association between the aging process and DNAme changes has been touched on in section 1.2.4. In the context of EWAS, chronological age has been shown to be highly correlative to DNAme changes, and thus a variable that is routinely included as a covariate in the regression model351,369. The reported age associations extend beyond the elderly 33 populations, but can be observed throughout the human life span219,229,370â374. The age-associated DNAme changes are particularly wide-spread in neonates and infancy, particularly in immune-associated genes370,371. As cellular composition changes with age, the observation raises the possibility that immune cell heterogeneity may be driving the associations375,234,376. Accurate accounting of both the chronological age and cell type proportions in EWAS is currently the best practice to mitigate this confounding effect3. 1.5.2 Strategies to account for DNAme-associated variability The concomitant variables, or variables that are associated with DNAme variability yet not the direct variables of interest of EWAS, are generally accounted for as covariates in the EWAS linear models. This regression model outputs the coefficient estimates and standard error of each fitted variable, informing their relative contribution to DNAme variability. Alternatively, variance associated with a set of variables can be regressed out prior to EWAS to âremove their effectâ from the DNAme data, as has been commonly implemented for batch correction377. Stratification is another common approach to not account for, but rather remove variability of a given associated variable by separating samples based on their group identity. This strategy is commonly employed in genetic studies to separate individuals from distinct genetic ancestries378â380. For DNAme data analysis, many studies have taken the same approach to reduce undesirable variance for improved signal detection233,381. 1.6 Thesis rationale and objective The cumulative literature suggests that DNAme is a crucial epigenetic mark that is not only important in shaping the developmental trajectory, but also reflects the influence of a variety of factors on human health and diseases. The thesis set out to profile and account for biological factors that drive DNAme variability, such as age, sex, and cellular composition. Two contrasting cases are presented in this thesis, one of a case of a single patient with a RDEO driving immunodeficiency and lymphoma, and one of a normative human birth cohort â the Canadian Healthy Infant Longitudinal Development (CHILD) 34 study. In the case of the single patient, I aimed to investigate the role of ASXL1 in patient DNAme regulation and immune landscape, as described in Chapter 2. Further, to characterize the extent of DNAme dysregulation, I developed strategies to assess and account for the effect of key biological variables, such as cellular heterogeneity and age, on DNA methylation variability: outlined in Chapter 3. Chapter 4 sets out to describe and improve the cell deconvolution procedure for longitudinal and pediatric blood DNAme samples, whereas current literature on estimating and accounting for ISCH is included as part of Chapter 5. Overall, the thesis proposes methods to dissect the sources of DNAme variability to improve the interpretability of DNAme-based association testing. 35 2 Chapter 2: Human ASXL1 Deficiency Causes Epigenetic Dysfunction, Combined Immunodeficiency and EBVâAssociated Hodgkin Lymphoma 2.1 Rationale and Introduction Monogenic IEIs are a group of more than 550 rare diseases caused by deleterious germline variants in immune-related genes311. Some IEI disease-causing variants occur in genes encoding epigenetic regulators that modify steady-state gene expression and cellular phenotype, such as DNMT3B and TET21,382. These alterations in epigenetic regulation disrupt hematopoiesis and cellular differentiation pathways that are important for immune function, driving an increased risk of infections and malignancies22,312. This is exemplified by the connection between DTA mutations (DNMT3A, TET2, and ASXL1) with age-related CH and myeloid neoplasms329,332. ASXL1 (OMIM #612990) is the least characterized of the three DTA genes and has not been previously linked with IEI in humans. ASXL1 and BAP1 together form the main subunit of the PR-DUB complex122. PR-DUB is crucial in embryonic development for its role in removals of H2AK119ub1 and transcriptional repression, and thus ASXL1 variants has been linked a developmental disorder, BOS (described in section 1.4.1.4)124,130. While Asxl1-null mice recapitulate BOS phenotypes, heterozygous germline deletion or hematopoietic-specific Asxl1 deletion leads to MDS-like features134,294. Further, conditional knock-in of an Asxl1 variant associated with CH revealed a phenotype of impaired T-cell development, function, and exhaustion that drove tumor progression in mice135,383,384. In this chapter, we report the first patient with an IEI due to biallelic germline variants in ASXL1 causing ASXL1 deficiency presenting with combined immunodeficiency, complicated by vaccine-strain rubella virus granulomas and EpsteinâBarr virus (EBV)-associated Hodgkin lymphoma. Along with the clinical phenotype, we compared the patient's DNAme landscape and immunological profiles with those of the healthy control (HC) population to understand the molecular impact of ASXL1 deficiency. 36 2.2 Materials and Methods 2.2.1 Participants and consent Peripheral blood samples were collected from the index patient, as well as from the patientâs father, mother, and sister, along with three unrelated healthy individuals at the BC Childrenâs Hospital Research Institute. Similarly, skin biopsies were collected from the patient and six healthy individuals. All participants from whom we collected biological samples and/or their parents/guardians provided written informed consent for sample collection, sequencing, data analysis, and the publication of findings and photographs. Data from additional samples were obtained from publicly available databases, as outlined below. The research protocols were approved by the University of British Columbia Clinical Research Ethics Board (H15-00641). 2.2.2 Identification of candidate variants Genomic DNA was isolated from peripheral blood using standard procedures and exome/genome sequencing was performed on the Illumina HiSeq platform (Macrogen, Seoul, South Korea). Sequencing data were analyzed using an updated version of our in-house, open-source, semi-automated bioinformatics pipeline as described previously385,386. Trio whole-exome sequencing (trio-WES) was performed on genomic DNA from the patient and both parents. Compound heterozygous ASXL1 variants were considered the top candidates of interest. Based on family history and pedigree, once a candidate variant list was generated by the pipeline, variants identified through the de novo, autosomal recessive, and compound heterozygous inheritance models were first assessed. All high-impact, putative LoF variants were reviewed. Next, the candidate variant list was screened for variants in genes associated with IEI, especially those previously linked with vaccine-strain rubella-associated granulomas. Genes associated with DNA repair deficiency disorders as well as telomeropathies were also investigated. In addition, a phenotype-driven approach was also employed to identify relevant variants in genes that may be associated with clinical features (using associated MeSH and HPO 37 terms) presented by the proband, such as multiple skin lesions, necrotizing granulomatous inflammation (postvaccine rubella strain identification), idiopathic chronic macrocytosis, low T-cell count, high B-cell count, mild megablastoid changes, mild dysplastic features, infections, pneumonia, failure to thrive, learning disability, and subtle dysmorphic features. The p.C111Y and p.1237F variants were further considered as potentially pathogenic based on in-silico tools. The pathogenicity predictors included combined annotation-dependent depletion (CADD) (p.C111Y: 26.2; p.S1237F: 23.0), Polyphen-2 (p.C111Y: 1.0; p.S1237F: 0.883), and MutationTaster (p.C111Y: 1.0; p.S1237F: 0.778)387â389. Furthermore, the p.S1237F variant is present in the COSMIC database (catalogue of somatic mutations in cancer) for its association with basal cell carcinoma390,391. Whole-genome sequencing (WGS) was then performed to scan for potential somatic mosaic variants and to exclude variants in noncoding regions. Compound heterozygous ASXL1 variants still emerged as the variants of highest significance and were further verified by Sanger sequencing. 2.2.3 Sanger sequencing Briefly, standard sequencing of genomic DNA from all individuals of the family was performed to confirm genotype segregation with the disease in the family using the following primers: Var1 p.C111Y: Forward 5â²-GAATGACTGCCTTGCTTTAAGA-3â², Reverse 5â²-AAAGGTTAGTCCCTAGTGCC-3â²; Var2 p.1237F: Forward 5â²-TGCCAGATAGCTGTGAAACA-3â², Reverse 5â²-CACATTTGGAGATGAGAAACGA-3â². 2.2.4 Generation and maintenance of primary T cells and fibroblasts For generation of T-cell blasts, T cells were isolated from peripheral blood mononuclear cells (PBMCs) (EasySepâ¢ Human T-Cell Isolation Kit, Cat # 17951; Stemcell, Vancouver, BC, Canada) and treated with exogenous IL-2 (10 U/mL) and ImmunoCultâ¢ Human CD3/CD28 T Cell Activator (Cat # 10971; Stemcell, Vancouver, BC, Canada) for 12 days, 38 maintained at a concentration of 106 cells/mL every 2 days. After 12 days, exogenous IL-2 concentration was increased to 1000 U/mL. To generate fibroblasts, a skin biopsy was obtained, manually cut into smaller pieces, and placed in separate wells of a 6-well plate in complete DMEM (GE Healthcare, Chicago, IL, USA) composed of 10% heat-inactivated FBS (Gibco-BRL Life Technologies, Grand Island, NY, USA; Life Technologies, Gaithersburg, MD, USA), 2mM l-glutamine (HyClone, Thermo Fisher Scientific, Waltham, MA, USA), 1mM sodium pyruvate (Gibco-BRL Life Technologies), and 1Ã Antibiotic-Antimycotic (Gibco-BRL Life Technologies, Thermo Fisher Scientific) for 3 weeks at 37°C or until a confluent monolayer of fibroblasts had formed. Primary fibroblasts were then lifted and frozen at â80°C for future assays. 2.2.5 Generation and expression of ASXL1 variant plasmids Full-length ASXL1 cDNA was cloned into a pCMV6-AC-IRES-GFP vector (Cat# PS100027; OriGene Technologies, Rockville, MD, USA) using the following restriction enzymes AsiSI (Cat# R0630S; New England Biolabs, Ipswich, MA, USA) and MluI-HF (Cat# R3198S; New England Biolabs). The p.C111Y and p.S1237F variants reported in this study were generated in the cloned vector through site-directed mutagenesis. The following primers were used for site-directed mutagenesis (lowercase = mutated site): Var1 p.C111Y: Forward 5â²- TGTGGAGAGCtatGGGTCTAATG -3â², Reverse 5â²- CATTAGACCCataGCTCTCCACA -3â²; Var2 p.1237F: Forward 5â²- GCAGTTCTCTtttTTTAGTTGTGAAG -3â², Reverse 5â²- CTTCACAACTAAAaaaAGAGAACTGC -3â². Expression of ASXL1 variant plasmids were induced transiently in HEK293 cells using a Lipofectamine 3000 kit (Cat# L3000150; Thermo Fisher Scientific) according to manufacturerâs recommendations. Briefly, HEK293 cells were seeded at 2.0 Ã 105 cells/well in a 24-well plate in 0.5 mL of Dulbecco modified Eagle medium (DMEM) with 10% FBS (Gibco, Life Technologies; Rockville, MD, USA) and incubated for 24 hours at 37°C. Cells were transfected with 250 ng of plasmid DNA using the P3000 and Lipofectamine 3000 reagents. 39 After 24 hours, cells were harvested and stained with a live dead stain (Fixable Viability Stain 780, Cat#565388, BD Biosciences, Franklin Lakes, NJ, USA) for 5 mins at 37°C. Expression of the live dead stain and GFP was then acquire on the BD LSRFortessa (BD Biosciences) and analysed using the FlowJo software (BD Biosciences). Cells were then fixed with Cytofix (Cat# 554655; BD Biosciences) for 10 minutes at room temperature and permeabilized using Perm III for 20 minutes on ice (Cat# 558050; BD Biosciences). The cells were then stained with either primary antibody for myc tag (1:1000 dilution, Cat# 2276; Cell Signaling Technology) or mouse IgG1 isotype control (1:500 dilution) for 1 hour at room temperature in 1Ã Perm/Wash (Cat# 554723; BD Biosciences), followed by staining in secondary goat anti-mouse secondary antibody (1:200 dilution Alexa Fluorâ¢ 405, Cat#: A-31553; Thermo Fisher Scientific) for 30 mins at room temperature, also in 1Ã Perm/Wash. 2.2.6 Stable WT ASXL1 expression in expanded patient-derived T cells Stable wild-type (WT) ASXL1 expression was achieved by infection of expanded patient-derived T-cells with lentiviral particles as described previously392,393, and expanded cells were used for downstream DNAme and intracellular flow cytometric analyses. To generate lentivirus vectors, wild-type ASXL1 (NM_015338) plasmid (Cat#: RG219354; OriGene Technologies, Rockville, MD, USA) was cloned into a GFP-tagged Lenti vector (Cat#: PS100071; OriGene Technologies) using AscI (Cat#: R0558L) and MluI-HF (Cat#: R3198L) (both from New England Biolabs, Ipswich, MA, USA). The plasmid was packaged using third-generation packaging plasmids and transfected into HEK293T cells. Culture medium was collected, centrifuged, filtered, concentrated, and stored at â80°C until use. Expanded T cells were infected with lentiviral particles and expanded in complete RPMI-1640 (GE Healthcare, Chicago, IL, USA) supplemented with 10% FBS for 9 days. 2.2.7 DNA extraction for DNA methylation array analysis Blood and buccal swabs were collected from the patient, and genomic DNA was extracted from whole blood, PBMCs, buccal cells, and cultured T cells. Whole blood was collected in 40 Vacutainer® CPTâ¢ Cell Preparation Tubes (Becton Dickinson, Franklin Lakes, NJ, USA). PBMCs were then isolated by standard Ficoll-Plaque (GE Healthcare) density centrifugation. Buccal swabs were collected with Isohelix Buccal Swabs (Cell Projects Ltd., Kent, UK) and Oragene DNA sample collection kits (DNA Genotek, Kanata, ON, Canada), respectively. Genomic DNA was extracted from whole blood, PBMCs, and expanded T cells with DNeasy kits (Qiagen, Germantown, MD, USA). Buccal DNA was extracted with Isohelix Buccal DNA Isolation kits (Cell Projects Ltd.). Extracted DNA (750 ng) was subjected to bisulfite conversion with an EZ-96 DNA Methylation kit (Zymo Research, Irvine, CA, USA), and aliquots of 160 ng were assayed with DNAme arrays. DNA extracted from primary tissues and T cells was assayed using Infinium HumanMethylationEPIC BeadChip (EPIC) and HumanMethylationEPICv2 BeadChip arrays (Illumina, San Diego, CA, USA), and scanned with the Illumina iScan 2000 to obtain IDAT files that capture raw DNAme intensities. Two patient PBMC samples were prepared and assayed as technical replicates on the EPIC arrays. 2.2.8 Data preprocessing and differential DNA methylation analysis The preprocessing and analysis detail for the DNAme data is described in detail in Chapter 3. Briefly, we downloaded Illumina DNAme microarray data (Infinium Human Methylation 450K BeadChip, GPL13534 and GPL16304, and Infinium MethylationEPIC v1.0 BeadChip, GPL21145 and GPL23976) of whole blood, PBMC, and buccal swab tissues from Gene Expression Omnibus (GEO) database using the R package GEOquery394,395. We then filtered the samples for age-matched HCs, i.e. samples collected from individuals with no notable medical history or disease status and between 6 to 18 years of age. The ASXL1 patientâs DNAme data were harmonized with the HC DNAme data, underwent quality controlled (QCâed), normalized, batch corrected, and cell type corrected together, separated by tissues. EWAS on disease status was conducted separately for the three tissues with one-sample t-tests, controlling for chronological age and biological sex in the model. Significantly differentially methylated sites (DMs) were identified as those with Benjamini-Hochberg adjusted P-value below or equal to 0.05 (Padj â¤ 0.05). For cultured T cells, the P-41 value threshold for DMs were lowered because the low sample size (patient samples: n = 3, rescued samples: n = 3, HC samples: n = 6), and DMs were defined as those with Padj â¤ 0.2 and delta-beta â¥ 0.1, with the effect size threshold added to ensure biological relevance of the detected DMs. 2.2.9 Epigenetic age analysis The epigenetic ages of the patient, family control, and unrelated HC blood and T-cell samples were estimated using the Horvath pan-tissue clock223, Hannum clock373, and Horvath skin and blood clock227. The buccal samplesâ epigenetic age was estimated with the pediatric buccal (PedBE) clock229. These estimations were performed using the methylclock R package396. 2.2.10 Enrichment analysis Overrepresentation analyses were conducted using the gometh function in the missMethyl package to identify enriched ChromHMM chromatin states. The ChromHMM states were exported from the Roadmap Epigenomics database and overlapped with the Illumina annotation file for the EPIC arrays189. The method accounts for the background probes on the DNAme arrays to minimize coverage biases. The probes were annotated to the corresponding genes and the enriched pathways with Padj < 0.05 were identified. 2.2.11 RNA sequencing of expanded T cells Expanded patient T cells, patient T cells rescued with WT ASXL1 and healthy control T cells stably transduced with GFP empty vector had their transcriptome measured. Briefly, RNA was extracted as previously described393 using a RNeasy Mini Plus Kit (Qiagen) according to manufacturerâs recommendations. A ribosomal depletion step was then carried out on the RNA before library construction and sequencing on the Illumina platform. 150 base pair paired end sequencing was conducted for a targeted total of 50 million reads per sample. De-multiplexed read sequences were aligned to a reference sequence using RNA-Seq Alignment app (v1.1.1) on Illumina Basespace, using Spliced Transcripts Alignment to a 42 Reference (STAR) aligner and Cufflinks 2 for assembly and estimation of gene expression. Expression data were normalized to reads between samples using the edgeR package397. Normalized counts were filtered to remove low counts using the filterByExpr function in edgeR398. 2.2.12 Enrichment analysis for differential expression Differential expression (DE) was accomplished using Limma399. We performed two sets of comparisons: HC (n = 3) versus patient (n = 3) samples, and patient versus rescued (n = 3). Gene set enrichment analysis was conducted on the GSEA software, using 1000 permutations with weighted (p = 1) enrichment score for preranked analysis based on t statistics from DE analysis400. Two MSigDB gene sets, Hallmark and Reactome, were used in the analysis, where default gene set exclusion criteria were set, with gene sets above the size of 500 or below 15 being filtered out in the analysis401,402. The sample level enrichment analysis (SLEA) scores were then determined by subsetting for the core enrichment genes in the significantly enriched pathways, followed by a z-score calculation of these genesâ expression in 1000 bootstraps. 2.2.13 Intracellular flow cytometry in PBMCs, T cells, and primary fibroblasts Intracellular flow cytometry was performed to examine the abundance of ASXL1, H2AK119ub1, and IL-2-induced phosopho-STAT5 (pSTAT5) in expanded patient-derived primary T cells, pSTAT5 in PBMCs, and ASXL1 in primary fibroblasts. Expanded T cells were first washed and stained with CD4 BUV563 (Clone: SK3, Cat# 612912; BD Biosciences, Franklin Lakes, NJ, USA), CD8 BUV395 (Clone: RPA-T8, Cat# 563796; BD Biosciences), and Fixable Viability Stain 780 (Cat#565388; BD Biosciences) for 15 minutes. Cells were then fixed (fibroblasts were lifted, spun, and fixed) using BD Cytofix (Cat# 554655; BD Biosciences) for 20 minutes at room temperature and permeabilized using Perm III for 30 minutes on ice (Cat# 558050; BD Biosciences). The cells were then stained with either primary antibody for ASXL1 (Cat# MA5-36238; Thermo Fisher Scientific) or mouse IgG1 isotype control for 1 hour at room temperature in 43 1Ã Perm/Wash (Cat# 554723; BD Biosciences). Intracellular staining was performed separately for H2AK119ub (Cat# 8240S; Cell Signaling Technology, Danvers, MA, USA) in expanded T cells. Subsequently, the cells were washed and stained with goat anti-mouse secondary antibody (Alexa Fluorâ¢ 405, Cat#: A-31553; Thermo Fisher Scientific) for 30 minutes at room temperature. ASXL1 expression was then measured using a BD FACSymphony flow cytometer (BD Biosciences) and analyzed using FlowJo software (BD Biosciences). Detection of pSTAT5 was performed similar to the protocol outlined above in expanded T cells. After permeabilization, cells were stained with mouse anti-human pSTAT5 (pY694) PE-Cy7 (Cat# 560117; BD Biosciences). The following antibodies were used for extracellular staining of PBMCs: CD3 FITC (Clone: UCHT1, Cat# 555332; BD Biosciences), CD4 BUV563 (Clone: SK3, Cat# 612912; BD Biosciences), CD8 BUV395 (Clone: RPA-T8, Cat# 563796; BD Biosciences), CD27 BV605 (Clone: L128, Cat#: 562655; BD Biosciences), CD45RA BV421 (Clone: HI100, Cat#: 562885; BD Biosciences). Immunophenotyping on T, B, and NK cells with intracellular cytokine detection was also conducted with PBMCs from the patient, family members, and HCs. The PBMCs were stimulated with a cell stimulation cocktail (PMA/Ionomycin, Cat# 00-4970-93, eBioscience Cell Stimulation Cocktail, ThermoFisher Scientific, Waltham, MA, USA) for 4h at 37ËC in the presence of protein transport inhibitors (Brefeldin A and Monensin, Cat# 00-4980-93, eBioscience Protein Transport Inhibitor Cocktail, ThermoFisher Scientific). Cells were stained with two different antibody panels (listed in Table 2.1) using the eBioscience Foxp3 Transcription Factor Staining Buffer Set (Cat# 00-5523-00, Invitrogen, Thermo Fisher Scientific). Samples were acquired on the BD FACSymphony flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA). Data was analyzed using FlowJo (BD Biosciences). Table 2.1 List of antibodies used in two different flow cytometry panels. Marker Fluorophore Clone Catalogue #: T-cell cytokine panel 44 Cell surface markers Anti-CD3 APC-H7 SK7 641406 (BD) Anti-CD8 BUV805 SK1 612889 (BD) Anti-CD4 BUV563 SK3 612912 (BD) Anti-CD27 BV605 L128 562655 (BD) Anti-CD45RA PerCP-Cy5.5 HI100 563429 (BD) Anti-PD-1 PeCy7 EH12.1 561272 (BD) Anti-CXCR5 BV421 2G8 562889 (BD) Anti-CD154 BUV737 24-31 752856 (BD) Intracellular markers Anti-TNF BUV395 MAb11 563996 (BD) Anti-IFNγ BV786 4S.B3 564793 (BD) Anti-IL-4 PeCF594 MP4-25D2 565161 (BD) Anti-IL-17A AF488 N49-653 560488 (BD) Anti-IL-21 AF647 3A3-N2.1 560493 (BD) Fixable viability stain AF700 564997 (BD) T- B- NK- panel Cell surface markers 45 Anti-CD45RA PerCP-Cy5.5 HI100 563429 (BD) Anti-CD19 APC HIB19 561742 (BD) Anti-CD3 APC-H7 SK7 641406 (BD) Anti-IgM BV421 G20-127 562618 (BD) Anti-CD8 BV570 SK1 301037 (BioLegend, San Diego, CA, USA) Anti-CD27 BV605 L128 562655 (BD) Anti-CD38 BV786 HIT2 563964 (BD) Anti-CD16 BUV496 3G8 612944 (BD) Anti-CD4 BUV563 SK3 612912 (BD) Anti-IgD BUV615 IA6-2 613008 (BD) Anti-CD56 BUV661 NCAM16.2 750478 (BD) Anti-CD25 BUV805 2A3 742070 (BD) Anti-CD57 PE NK1 560844 (BD) Intracellular markers Anti-Perforin FITC δG9 567721 (BD) Anti-Granzyme BV510 GB11 563388 (BD) Fixable viability stain AF700 564997 (BD) 2.2.14 Clinical-grade flow cytometry 46 Patient clinical immunophenotyping was carried out on whole blood at an accredited clinical flow cytometry laboratory according to standard protocols. Briefly, whole blood was collected and stained using dried antibody reagents (DURAClone tubes; Beckman Coulter, Brea, CA, USA) according to the manufacturerâs recommendations. Panels focused on the analysis of T-cell subsets (DURAClone IM T Cell Subsets, Cat#: B53328; Beckman Coulter) and B-cell subsets (DURAClone IM B Cell, Cat#: B53318; Beckman Coulter). Samples used for the B-cell subsets were washed three times prior to staining. Aliquots of 100 μL of fresh blood were added to the dried reagent tubes and incubated in the dark at room temperature for 15 minutes prior to lysis with ImmunoPrep Reagent System (Beckman Coulter). Samples were then fixed with 1.0 mL of 1% paraformaldehyde prior to flow cytometry. All flow data were acquired on a Navios flow cytometer and then analyzed using Kaluza software (Beckman Coulter). 2.2.15 Histology Formalin-fixed, paraffin-embedded lymph node tissue was cut into sections 3 µm thick for routine hematoxylin and eosin (H&E) staining or 5 µm thick for immunohistochemistry. Bone marrow trephine biopsies were processed in B-plus solution followed by decalcification and formalin fixation. Paraffin-embedded marrow tissue was then cut into sections 3 µm thick for routine hematoxylin and eosin (H&E) staining or 4 µm thick for immunohistochemistry. 47 2.3 Results 2.3.1 Case description The index patient is a now 16-year-old female (pronouns: they/them) born to nonconsanguineous parents. Their mother has a history of rheumatoid arthritis, while their father and older sister have no notable clinical history. The index patient was born full-term and received all routine immunizations, including live attenuated measles, mumps, rubella (MMR) vaccine (Figure 2.1). At 3 years of age, the patient developed chronic ulcerated skin granulomas on the right arm and left leg. They were hospitalized twice for pneumonia at ages 3 months and 6 years, with the latter in the context of primary EBV infection. Laboratory tests over time showed chronic macrocytosis, T-cell lymphopenia, progressive loss of B-cells, and hypogammaglobulinemia (Figure 2.2, Figure 2.3). Figure 2.1 Clinical timeline of a patient with biallelic ASXL1 variants. The timeline depicts the patientâs infections requiring hospitalization (pneumonia at 3 months, primary EBV infection and bacterial pneumonia at 6 years), the timing of MMR vaccines, the onset of cutaneous granulomas and EBV+ Hodgkin lymphoma, and definitive treatment with hematopoietic stem cell transplant (HSCT). 48 Figure 2.2 The index patientâs hematological parameters over time. The charts demonstrate normal/elevated hemoglobin, declining platelet and absolute neutrophil counts, chronic macrocytosis, low/normal T-cell counts, and progressive loss of B cells. Figure 2.3 Extended immune phenotypes of the index patient. White blood cell count assessment over time showed normal white blood cell count. Immunoglobulin assessment over time showed low IgG until initiation of intravenous immunoglobulin (IVIG), low/normal IgM levels and low/normal IgA levels. 49 Despite prolonged systemic courses of antimicrobials, topical corticosteroids and wound care, the skin granulomas persisted for more than a decade, with new lesions developing in the left inguinal region. Repeated biopsies showed necrotizing granulomatous inflammation with no organisms identified on special stains (Figure 2.4, Figure 2.5). Serial bone marrow biopsies every 1â2 years showed mild hypocellularity, megaloblastoid changes, and eventually the presence of non-necrotizing granulomas (Figure 2.6). Infectious studies revealed intermittent EBV viremia (ranging from 0 to 1000s copies/mL), absent antibody titers to measles, mumps, varicella, and hepatitis B, but high titers to rubella. After vaccine-strain rubella granulomas were reported in other IEI patients403, a biopsy from one of the cutaneous granulomas was tested for vaccine-strain rubella, RA 27/3, and was found to be positive by PCR. Figure 2.4 Image of healing cutaneous granuloma on left knee taken at 14 years old. Figure 2.5 Skin biopsy performed at 9 years old from the left thigh. A tissue section stained with hematoxylin and eosin showing subcutaneous necrotizing (*) and 50 nonnecrotizing granulomas (arrowhead). Specific staining for organisms was negative (not shown). Scale bar: 200 μm. Figure 2.6 Bone marrow biopsy from the index patient. The image showed mildly hypocellular marrow and granuloma (arrowhead). At 14.5 years of age, the patient was diagnosed with EBV-associated stage IVA classical Hodgkin lymphoma, mixed cellularity subtype with prominent granulomatous inflammation (Figure 2.7). They underwent four cycles of chemotherapy, at which time a repeat Positron Emission Topography (PET)-CT showed progressive disease, and were transitioned to nivolumab, an anti-programmed cell death protein 1 (PD1) antibody, immunotherapy. This was complicated by the development of autoimmune thyroiditis treated with thyroxine. Six weeks following discontinuation of nivolumab, conditioning began for a 9/10 matched unrelated donor hematopoietic stem cell transplant (HSCT) with cyclophosphamide, fludarabine and 200cGy total body irradiation. The patient received mycophenolate mofetil and tacrolimus prophylaxis for graft versus host disease (GVHD) and developed grade 1 GVHD of the upper gut and skin which was treated with corticosteroids. She was discharged on day +34 with full donor chimerism, and remains well now 18 months posttransplant with healing of their chronic skin lesions. 51 Figure 2.7 Micrograph of lymph node from the index patient. The image indicates juxtaposed granulomatous inflammation (*) and classical Hodgkin lymphoma with ReedâSternberg cells (arrowheads). Scale bar: 200 μm. 2.3.2 Identification and characterization of novel germline compound heterozygous ASXL1 variants Given the unique and severe clinical presentation, trio-WES and subsequent WGS were performed. Sequencing identified compound heterozygous missense variants in ASXL1: a maternally inherited variant in exon 5 (Var1_pC111Y: NM_015338.6:c.332G>A; NP_056153.2:p.Cys111Tyr) and a paternally inherited variant in exon 13 (Var2_p1237F: NM_015338.6:c.3710C>T; NP_056153.2:p.Ser1237Phe), as candidates. The ASXL1 variants segregated with disease in the family (Figure 2.8). These are missense variants predicted to be deleterious, and were considered to be strong candidates for further functional evaluation based on their changes in amino acid polarities, high combined annotation-dependent depletion (CADD) scores (p.C111Y: 26.2; p.S1237F: 23.0), and their low allelic frequency in the Genome Aggregation Database (gnomAD) v4 (p.C111Y: 3.10eâ06; p.S1237F: 4.90e-05, with no homozygotes for either variant; Figure 2.9). Both of these variants fell outside the mutational hotspot region associated with typical truncating BOS variants in ASXL1 exon 13 (Figure 2.10). 52 Figure 2.8 Characterization of germline biallelic ASXL1 variants in the patient. (A) Family pedigree of ASXL1 variant inheritance pattern. (B) Index patient and their familyâs ASXL1 genotype as verified by Sanger sequencing. The red arrows highlight the base pairs changed in the ASXL1 missense variants. Figure 2.9 Predicted CADD score (v1.6) and allelic frequency of ASXL1 variants reported in the gnomAD database (v3). All missense (gray), stop-gained (navy blue), or frameshift (magenta) variants are plotted. Patient variants are indicated with a triangle. VEP, variant effect predictor. 53 Figure 2.10 A schematic of major functional domains in ASXL1 and the positions of patient and other reported germline variants. The patientâs ASXL1 variants (p.C111Y and p.S1237F, labeled red) are outside the mutational hotspot and major functional domains. The variant linked to non-Hodgkin lymphoma (p.R402Q) is labeled blue, while those linked to Bohring-Opitz syndrome are labeled gray. Molecular consequences of the patientâs ASXL1 variants were next defined. Expanded patient primary T cells (both CD4 and CD8) expressed approximately half the amount of total ASXL1 protein when compared to both HCs and family members (p < 0.05, Figure 2.11). ASXL1 deficiency was also demonstrated in patient cultured fibroblasts obtained from a skin biopsy (Figure 2.12). Given the well-described role of ASXL1 in H2AK119 deubiquitination404, we quantified H2AK119ub1 in expanded T cells but did not find notable differences in bulk enrichment levels between the patient and controls (Figure 2.13). While surprising, recent work has shown that BOS-causing germline ASXL1 variants do not alter specific histone modifications, including H2AK119ub1, but cause DNAme dysregulation136. To define the molecular impact of each of the patientâs ASXL1 alleles, we transfected HEK293 cells with GFP-tagged plasmids containing either myc-tagged WT or the patient's variant ASXL1 (Figure 2.14). Both ASXL1 variants show significantly reduced myc mean fluorescence intensity (MFI) compared to WT ASXL1 in the transfected (GFP+) cells (p < 0.05, Figure 2.15). Together, these studies confirmed that that both of the patientâs variants result in a minor yet significant reduction in ASXL1 protein abundance, and in the compound heterozygous state result in human ASXL1 deficiency. 54 Figure 2.11 ASXL1 protein abundance in expanded CD4 and CD8 T cells. (Left) A representative density plot of patient and HC ASXL1 protein abundance, measured by flow cytometry, and (Right) their respective quantification (n = 4). *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance and Dunnettâs multiple comparison test. Figure 2.12 ASXL1 protein abundance in cultured fibroblasts. (Left) A representative density plot of patient and HC ASXL1 protein abundance, measured by flow cytometry, and (right) their respective quantification (n = 3). *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance and Dunnettâs multiple comparison test. 55 Figure 2.13 H2AK119ub abundance in expanded CD4 and CD8 T cells. (Left) A representative density plot of patient and HC H2AK119ub abundance, measured by flow cytometry, and (Right) their respective quantification (n = 3). *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance and Dunnettâs multiple comparison test. Figure 2.14 A schematic of the experimental setup to assess the effect of each of the patientâs ASXL1 variants on ASXL1 protein abundance. C-terminally myc-tagged WT, p.C111Y, or p.S1237F ASXL1 where cloned into a vector containing an independent ribosomal entry site (IRES) followed by a GFP reporter gene. The resulting plasmid leads to independent translation of ASXL1-myc and GFP. The plasmids were transfected into HEK293 cells and the relative ASXL1 abundance was measured with flow cytometry. 56 Figure 2.15 ASXL1 protein abundance in transfected HEK293 cells. (Left) A representative density plot of WT, p.S1237F, and p.C111Y ASXL1âs protein abundance, detected by flow cytometry based on the MFI of C-terminus myc-tag in successfully transfected cells (GFP+ population). (Right) The respective quantification of each ASXL1 variant (n = 8). *P < 0.05, **P < 0.01, one-way analysis of variance and Dunnettâs multiple comparison test. 2.3.3 Differential DNAme in PRC-regulated genomic elements and advanced epigenetic aging in the patientâs lymphocytes We conducted epigenome-wide association studies to compare the DNAme patterns of primary patient samples to age-matched HCs compiled from publicly available data sets in three tissue types: buccal swabs (n = 725), whole blood (n = 452), and isolated PBMCs (n = 285). The details of the methods are described in Chapter 3. Briefly, age-matched HC samples are curated from the GEO database based on age (6 to 18 years) and health conditions (with no notable medical history or disease status)394. One-sample t-test was conducted probe-wise to detect deviation of HC sample means from the patient DNAme levels. We observed wide-spread differential DNAme between the patient and HCs in whole blood and PBMCs but not in buccal swabs (Figure 2.16). There were 50,885 DMs in whole blood, 18,581 DMs in PBMCs, but only 38 DMs in buccal swabs that passed the threshold of Padj â¤ 0.05, 15.7% of which (9,438 DMs) overlapped between whole blood and PBMCs (Figure 2.17). Emphasizing that this patient does not have BOS, we found that BOS-associated DMs were not enriched in our analysis (Figure 2.18). Pathway enrichment 57 analysis with 18-state ChromHMM chromatin state annotation showed that DMs in both whole blood and PBMCs were significantly enriched in regulatory elements, such as enhancers, bivalent transcription start sites, and repressed Polycomb regions, as well as some quiescent gene and heterochromatin regions in PBMCs (Figure 2.19). Figure 2.16 A volcano plot of differential DNAme between the index patient and HCs in whole blood, PBMCs, and buccal swabs. Delta beta represents the effect size of the patient status variable after accounting for age at sample collection and sex. The turquoise points represent sites with significantly increased DNAme in the patient, while the magenta points indicate sites with significantly decreased DNAme. The vertical dark gray line represents an effect size threshold of 0.1, and the horizontal line corresponds to the statistical threshold of Padj = 0.05. 58 Figure 2.17 Venn diagram showing the overlap of differentially methylated sites with Padj < 0.05 in whole blood and PBMCs. Figure 2.18 Venn diagram of differentially methylated loci with Bohring Opitz syndrome-associated DNAme signature. The diagram shows the overlap of patient-variant-associated differential DNAme signatures identified in our whole blood analysis and those associated with BOS in a previous study298. 59 Figure 2.19 Chromatin state enrichment analysis in whole blood and PBMCs. Overrepresentation analysis was used to extrapolate the enrichment of the 18-state ChromHMM model using differential DNAme in whole blood and PBMCs. The gray dashed line corresponds to Padj = 0.05. To gain further information about the patientâs DNA methylome, we assessed the patientâs tissue-specific epigenetic age. We focused on the first-generation epigenetic clocks as they specifically estimate chronological age based on DNAme patterns405. In analysis of buccal swabs, the patient was estimated to have a PedBE clock age of 10.8 years, matching their chronological age of 11 years at sample collection (Figure 2.20). In contrast, the Horvath pan-tissue age of blood samples was higher compared to their chronological age (Figure 2.21). In alignment with the EA findings, the predicted telomere length based on DNAme data was lower in blood (Figure 2.21). The presence of short telomeres in lymphocytes was confirmed by direct measurement (Figure 2.22). Overall, we identified advanced epigenetic aging (as estimated by the Horvath pan-tissue clock) and short telomeres in ASXL1-deficient patient blood samples compared to HCs, which was especially prominent in lymphocyte subsets. 60 Figure 2.20 Predicted PedBE epigenetic age in the index patient and HCs. HC samples are shown in blue, while patient samples are shown in red. The black line indicates the best fit line using linear regression of all the sample points. The gray area corresponds to the 95% confidence interval. 61 Figure 2.21 Horvath pan-tissue EA and predicted telomere length of the index patient and HCs in whole blood and PBMCs. At the time point sample collection, the patientâs chronological age is 11 years. The EA and telomere length prediction were performed with the methylclock package in R. HC samples are indicated in blue, while patient samples are indicated in red. The black line represents the linear model best-fit line. The gray area surrounding the best-fit line corresponds to the 95% confidence interval. Figure 2.22 Telomere length of patient lymphocytes and granulocytes. Telomere length was measured with Flow-FISH assay (red dot)406. The index patientâs telomere lengths in lymphocytes and granulocytes were compared with that in the reference range of HC population. 62 2.3.4 Wide-spread DNAme loss and advanced epigenetic aging in patientâs T cells were rescued by WT ASXL1 transduction To examine causation, we transduced expanded patient-derived T cells with WT ASXL1 to delineate the relationship between the patientâs variants and the alterations in epigenetic regulation in lymphocytes. Whereas empty vector (EV)-transduced patient cells (patient condition) maintained a lower ASXL1 protein level compared to the EV-transduced HC cells (HC condition), WT ASXL1-transduced patient cells (rescued condition) showed increased ASXL1 protein comparable to the HC condition (Figure 2.23). Figure 2.23 GFP and ASXL1 abundance in HC, patient, and rescued expanded T cells. Protein abundance was detected by flow cytometry. The vectors contained a GFP tag, so the transduction efficiency was measured using the GFP signal. Expanded T cells from unrelated HCs were either not transduced (Untransduced condition) or transduced with an EV (HC condition), while expanded T cells from the patient were transduced with either an EV (patient condition) or WT ASXL1 (rescue condition). Principal component analysis (PCA) of the T cellsâ DNAme showed that the patient clustered away from the HC (including family member) cells along PC1, with the rescued cells in the middle (Figure 2.24). In the T cells, EWAS comparisons resulted in zero DMs after multiple test correction due to the low sample size. Therefore, we adjusted the detection threshold to Padj < 0.2 and delta-beta > 0.1, with the intention of exploring potentially dysregulated elements with a stricter delta-beta threshold to preserve some 63 robustness. Under these new thresholds, analysis of the patient vs. HC comparison yielded 141,113 DMs, of which 129,104 (91.5%) showed decreased DNAme. The pattern observed in the PCA plot was replicated in the DMsâpatient samples were notable for their decreased DNAme, while the rescued condition showed an intermediate DNAme level (Figure 2.25). Furthermore, loss of DNAme in the patient condition occurred mainly in loci that were highly methylated in HC samples; of the DMs with decreased DNAme, 80.1% were > 50% methylated in the HCs (skewness test P < 2.2eâ16, Figure 2.25). In the rescued vs. HC condition, 35,457 DMs were identified, and 90,345 of the patient vs. HC DMs were not significantly different in this comparison, indicating that the majority of the DMs in the patientâs cells were rescued by WT ASXL1 transduction (Figure 2.26). Similar to our findings in blood, the genomic locations of DMs were highly enriched for repressed Polycomb and bivalent chromatin states, with the rescued cells showing higher P-values than that of patient cells (Figure 2.27). When performing differential expression (DE) analysis on RNA-seq data of the same expanded T-cell samples, we observed enriched downregulation of DNAme and other epigenetic-related pathways in the patient compared to the rescued samples (Figure 2.28). Figure 2.24 PCA of DNAme landscape in HC, patient, or rescued expanded T cells. Biplot of adjusted PC1 and PC2 generated by PCA of DNAme measured at all loci on the methylation microarray. Unrelated HC samples are labeled blue, family HCs are labeled orange, patient samples are labeled purple, and rescued samples are labeled green. 64 Figure 2.25 Heatmaps of DMs between HC and patient expanded T cells. DMs were identified with linear regression analysis comparing the index patient with unrelated and family HC samples. The heatmap on the left is row normalized, and that on the right shows the beta value, with the DMs in rows (orders differ between the left and right heatmap) and samples in columns. Figure 2.26 Venn diagram of the DMs identified in expanded T cells. Of the DMs identified in the patient vs. HC (purple) comparison, 82.0% were not differentially methylated in the rescued vs. HC (green) comparisons. 65 Figure 2.27 Chromatin state enrichment analysis in expanded T cells. Overrepresentation analysis was used to extrapolate the enrichment of the 18-state ChromHMM model using differential DNAme in the patient vs. HC (purple) and rescued vs. HC (green) comparisons. The gray dashed line corresponds to Padj = 0.05. We also tested whether WT ASXL1 could normalize the EA acceleration we had observed in the patient. While the Horvath pan-tissue clock slightly underpredicted chronological age in controls, the clock assigned the patient an EA on average 27.4 years older than their chronological age. Notably, rescue with WT ASXL1 significantly lowered the epigenetic age (Figure 2.29A). Specifically, the difference between EA and chronological age was significantly higher in the patient than family and unrelated HCs, and this difference was significantly reduced by WT ASXL1 (P < 0.0005) (Figure 2.29A). Two other first-generation epigenetic clocks, the Hannum and Horvath skin-and-blood clocks, corroborated this trend (Figure 2.29B-C). Based on this series of experiments, WT ASXL1 transduction rescued the DNAme differences and attenuated the advanced epigenetic aging driven by the patientâs ASXL1 variants. 66 Figure 2.28 Heatmaps of enriched pathways based on DE in cultured T cells. The DE analysis compared (A) patient vs. HC and (B) rescued vs patient, using both the Hallmark (top) and Reactome (bottom) MSigDB gene sets. Top 10 positively or negatively enriched pathways with P < 0.05 were shown. The color scales reflect the normalized expression of the core enrichment genes in each pathway. Some Reactome pathway names were shortened for plotting purposes, as indicated with a * symbol. The shortened pathways include i) CHROMATIN MODIFICATIONS DURING MZT*: CHROMATIN MODIFICATIONS DURING THE MATERNAL TO ZYGOTIC TRANSITION MZT; ii) RESPONSE OF EIF2AK4 GCN2*: RESPONSE OF EIF2AK4 GCN2 TO AMINO ACID DEFICIENCY; iii) SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING*: SRP DEPENDENT COTRANSLATIONAL PROTEIN TARGETING TO MEMBRANE; iv) ACTIVATION OF THE MRNA*: ACTIVATION OF THE MRNA UPON BINDING OF THE CAP BINDING COMPLEX AND EIFS AND SUBSEQUENT 67 BINDING TO 43S; v) IMMUNOREGULATORY INTERACTIONS*: IMMUNOREGULATORY INTERACTIONS BETWEEN A LYMPHOID AND A NON LYMPHOID CELL. Figure 2.29 Higher EA in expanded patient-derived T cells was rescued by WT ASXL1. Bar graph showing the differences in (A) Horvath pan-tissue, (B) Horvath skin and blood, and (C) Hannum EA against chronological age of the expanded T cells. *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance with Dunnettâs multiple comparison test. 2.3.5 Patient CD4 T cells displayed elevated PD1 and contraction of the T follicular helper cell compartment Given the methylation impairments in patient T cells, we conducted detailed immunophenotyping of peripheral blood collected before the patientâs lymphoma diagnosis. Naïve CD4 and CD8 T-cell subsets and recent thymic emigrants were markedly reduced, suggesting poor thymic output (Figure 2.30, Figure 2.31, Table 2.2). The patient also showed expansion of PD1+ CD4 and CD8 memory cells, indicating T-cell exhaustion (Figure 2.32, Figure 2.33). To further investigate whether T follicular helper (Tfh) cells contributed to the increased patient PD1 expression, we performed flow on patient PBMC collected over five years (three patient samples: pre-lymphoma, lymphoma pre-68 chemotherapy, and lymphoma during chemotherapy; one post-transplant sample collected one year post-HSCT). During the time these samples were collected, the patient also had near absent B cells with decreased memory proportions (Figure 2.2, Figure 2.34). A paucity of circulating Tfh (CXCR5+) cells was observed in patient CD4 memory T cells (Figure 2.35). Accompanied by findings of reduced IL4 expression (Figure 2.36), the data suggest that the patient had significantly restricted Tfh functions, and that PD1 was independent of CXCR5 expression (Figure 2.37). Notably, both the Tfh and the IL4-producing cell proportions restored to levels similar to the HCs one year post-HSCT (Figure 2.35, Figure 2.36). Figure 2.30 T cell phenotyping showing altered T cell compartments in the patient. Clinical flow cytometry contour plots showing (top) CD4 and (bottom) CD8 T cell compartments, as determined by CD197 (CCR7) and CD45RA, of the patient and one representative age-matched HC. 69 Figure 2.31 Quantification of T cell compartments in the index patient and HC based on clinical flow cytometry. Quantification of the relative proportions of CD4 (top) and CD8 (bottom) naïve, TCM, TEM, and TEMRA compartments. The quantification includes five age-matched HCs and two patient samples collected during lymphoma and pre-HSCT (based on 7 independent experiments). Table 2.2 Immunophenotyping of Index patient lymphocytes at age 11 years. Red denotes values above the reference range, while blue denotes values below the age-matched reference range. Hematologic and immunologic parameters Index patient Age-matched reference range T cell studies (Units) CD3+ (109 cells/L) 0.374â 0.850-3.200 CD3+CD4+ (109 cells/L) 0.151â 0.400-2.100 CD3+CD8+ (109 cells/L) 0.208â 0.300-1.300 CD3+CD4+CD45RA+CD31+ (recent thymic emigrants) (%) 2â 31-81 CD4+ T cell repertoire Normal Polyclonal Pattern CD8+ T cell repertoire CD3+CD8+ TCR Vβ12 (% of total CD8 cells) 39.5â 0.33-3.33 CD3+CD8+ TCR Vβ14 (% of total CD8 cells) 18.35â 1.50-14.30 B cell studies (Units) CD19+ B lymphocyte absolute (109 cells/L) 0.069â 0.120-0.740 CD19+CD27+ memory B cells (%) 1.3â 13.3-47.9 CD19+CD27-IgD+ naïve (%) 86.1â 57.3-82.5 CD19+CD27+IgM+IgD+CD38dimCD24+ (memory, non-switched) (%) 0.8â 4.6-18.2 70 CD19+CD27+IgM-IgD-CD38dimCD24- (memory, class switched) (%) 0.5â 8.7-25.6 CD19+IgM+CD38++ (transitional) (%) 0.2â 1.4-13.0 CD19+CD21loCD38lo (%) 65â 1.0-11.0 Figure 2.32 PD1+ cell populations in primary CD4 and CD8 memory T cells of HCs and the patient based on clinical flow cytometry. (Left) Example contour plots of one representative age-matched HC and the patient. (Right) Quantification of PD1+ population in five age-matched HCs and two patient samplesâ CD4 and CD8 memory T cells (based on 7 independent experiments). 71 Figure 2.33 Quantification of PD1+ cell populations in the naïve, TCM, TEM, and TEMRA compartments for CD4 and CD8 T cells based on clinical flow cytometry. Samples from the index patient were compared against compared against five age-matched HCs (based on 7 independent experiments). Figure 2.34 B cell phenotyping of the patient and HCsâ PBMC. (Top) Representative contour plot of B-cell compartments based on IgD and CD27 expression for HC, index 72 patient, and patient one-year post-HSCT, measured with research flow cytometry. (Bottom) Quantification of naïve, non-class-switch and class-switched B-cell compartments in unrelated HC, family HC, patient, and post-HSCT mature B cells. *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance and Dunnettâs multiple comparison test. Figure 2.35 Circulating Tfh cell profiling in the patient and HCsâ PBMC. (Top) Representative contour plot of research flow cytometry on circulating Tfh cells (CD4+CD27-/CD27+CD45RA-CXCR5+) in a HC, the patient, and the patient one-year post-HSCT. (Bottom) Quantification of CXCR5+ population in an unrelated HC, a family HC, the patient, and the patient post-HSCT CD4 memory T cells. *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance with Dunnettâs multiple comparison test. 73 Figure 2.36 Characterization of CD4+ memory T cells in the patient and HCsâ PBMC. (Top) Representative contour plot of research flow cytometry on IFNg and IL4-expressing cells in CD4 memory T cells of a HC, the patient, and the patient one-year post-HSCT (n = 6, 3 and 1 for HC, patient, and patient post-HSCT samples, respectively; based on 3 independent experiments). (Bottom) Quantification of IL4+ population in an unrelated HC, a family HC, the patient, and the patient post-HSCT CD4 memory T cells (n = 6, 3 and 1 for HC, patient, and patient post-HSCT samples, respectively; based on 3 independent experiments). *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance with Dunnettâs multiple comparison test. 74 Figure 2.37 Further profiling of Tfh cell function in terms of PD1 expression. Contour plot of PD1+ and CXCR5+ CD4 memory T cells based on research flow, with samples collected from a healthy control, the patient, and the patient post-HSCT. The patient pre- and post-HSCT samples showed minimal CXCR5 expression. 2.3.6 ASXL1 variants impaired CD4 and CD8 T-cell maintenance and activation in patient cells We also quantified the expression of the senescence marker CD57 in patient cells. We found that patient CD8 T cells exhibited elevated CD57 expression in the effector memory compartment and had a restricted T-cell receptor repertoire (Table 2.2, Figure 2.38, Figure 2.39). This was not prominent in CD4 T cells (Figure 2.38, Figure 2.39). When 75 further profiling the expanded T cells from patient PBMCs, we observed that, in contrast to HC, the patientâs CD8 T cell population failed to expand in the presence of soluble activators, despite this ex vivo environment promoting a skewing towards CD8 T-cell expansion407 (Figure 2.40). This skewing led to a significantly higher CD4/CD8 ratio that was rescued with the transduction of WT ASXL1 (Figure 2.40). This high CD4/CD8 ratio, however, was not observed in primary patient blood (Table 2.2). Figure 2.38 CD57+ cell populations in primary CD4 and CD8 memory T cells of HCs and the patient based on clinical flow cytometry. (Left) Example contour plots of one representative age-matched HC and the patient. (Right) Quantification of CD57+ population in five age-matched HCs and two patient samplesâ CD4 and CD8 memory T cells (based on 7 independent experiments). 76 Figure 2.39 Quantification of CD57+ cell populations in the naïve, TCM, TEM, and TEMRA compartments. The patientâs CD4 and CD8 T cells (n = 2) were compared against five age-matched HCs (based on 7 independent experiments). Figure 2.40 Relative proliferative capacity of expanded CD4 and CD8 T cells. (Top) Contour plot illustrating the relative proportions of CD4 and CD8 populations in cultured T cells from a HC, the patient, and the patient samples transduced with wild-type ASXL1. A representative plot is shown for each condition. (Bottom) Quantification of the CD4%, CD8%, and CD4/CD8 ratio from expanded T cells of 5 HCs, along with expanded T cells of 77 the patient and the rescued samples (patient sample that was transduced with WT ASXL1). Statistical significance was calculated using a one-way analysis of variance, followed by Dunnettâs multiple comparison test for the HC, the patient and the rescued samples (n = 27, 6 and 6 for HC, patient, and rescued samples, respectively; based on 6 independent experiments). Figure 2.41 Profiling of pSTAT5 abundance in expanded T cells. pSTAT5 abundance was measured with flow cytometry, on expanded CD4 and CD8 T cells from the patient (purple), family HC (orange), and unrelated HC (light blue) after stimulation with IL-2 (1 U/mL) for 15 minutes (n = 3 for each condition, based on 3 independent experiments). A histogram from one representative experiment is shown here. pSTAT5+ cells were determined by gating at the unstimulated patient sample in each experiment. *P < 0.05, **P < 0.01, ***P < 0.001, unpaired t test at each dose followed by the Holmâ Å ídák multiple comparison test. We then assessed pSTAT5 signaling and reactive oxygen species (ROS) production, as alterations in these pathways were reported in T cells containing an ASXL1 variant associated with CH of indeterminate potential384. We observed significantly reduced pSTAT5 abundance after low-dose IL-2 (1U/mL) treatment in expanded patient CD8 T cells, which was rescued by stable WT ASXL1 expression, indicating a causative role for ASXL1 in regulating STAT5-mediated CD8 T cell homeostasis (Figure 2.41, bottom). In contrast, pSTAT5 levels in CD4 T cells were not significantly different between the patient and the 78 HCs, and the rescued condition did not affect IL2-mediated pSTAT5 abundance (Figure 2.41, top). The finding in CD8 T cells was mirrored by the transcriptomic analysis showing downregulated IL2-STAT5 signaling in patient samples compared to the HC, and upregulation of the pathway in the rescued condition (Figure 2.28, top). Finally, significantly higher ROS production was observed in expanded patient CD4 T cells, which was rescued upon transduction of WT ASXL1 (Figure 2.42, top). In contrast, while ROS expression was lower in patient CD8 T cell samples, WT ASXL1 failed to rescue the phenotype (Figure 2.42, bottom). These findings suggest that ASXL1 deficiency mediates impairment of CD4 and CD8 T cells by alternative modes. Figure 2.42 ROS profiling of the patient and HCsâ expanded T cells. Oxidative stress was detected in expanded CD4 and CD8 T cells of the patient and HCs by flow cytometry using CellROX Deep Red reagent. *P < 0.05, **P < 0.01, one-way analysis of variance and Tukeyâs post hoc test. 79 2.3.7 Reduced NKT and NK cell cytotoxicity was rescued in vitro with WT ASXL1 and recovered post-HSCT Finally, we profiled the patientâs NK cells to assess their potential contribution to the clinical phenotype of persistent viral infection. We noted a significant contraction in the proportion of CD3+CD56+ (NKT) cells in patient PBMCs compared to HCs, which was partially ameliorated one year post-HSCT (Figure 2.43). Interestingly, we observed a reduction of DNAme in the gene body of NCAM1 â encoding for CD56 â in patient expanded T cells compared to HCs (Figure 2.44). The mRNA expression of NCAM1 also decreased in patient samples accordingly (Figure 2.45). Upon transduction of WT ASXL1, we observed a partial rescue of the NCAM1 DNAme level and NCAM1 transcript levels (Figure 2.44, Figure 2.45), likely reflecting a parallel to the CD56+ expression in patient T cells post-HSCT (Figure 2.43C). The patient PBMC also had a lower proportion of mature and cytotoxic NK cells (CD16+CD57+ NK cells, Figure 2.46), likely further explaining the patientâs lack of cytotoxic response to viral infections. 80 Figure 2.43 CD3+CD56+ cell populations in the patient and HCsâ PBMC. (A) Representative contour plot of CD56+ CD3+ cells from one HC, the patient, and the patient post-HSCT. (B-C) Quantification of (B) percentage of CD56+ CD3+ cells and (C) CD56 MFI in CD3+ cells for 6 unrelated HCs, 6 family HCs, 3 patient, and 1 post-HSCT patient samples (based on 3 independent experiments). *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance, followed by Dunnettâs multiple comparison test for HC and patient samples. Figure 2.44 DNAme profiles of NCAM1 gene. Average percentage of DNA methylation in loci measured in the NCAM1 gene (encoding for CD56) from expanded T cells derived from the HC, the patient, and the rescued (patient transduced with WT ASXL1) samples (n = 3 for each condition, based on 1 independent experiment, with patient and rescued samples collected at 3 time points). * indicates significantly differentially methylated loci between HC and patient conditions, with thresholds of Padj < 0.2 and delta-beta > 0.1. Differential methylation analysis was performed with limma, and multiple test correction was performed with the Benjamini-Hochberg method for all tested DNAme loci. Figure 2.45 Expression level of NCAM1. NCAM1 transcript per million for one HC, the patient, and the rescued (patient cells transduced with WT ASXL1) expanded T cells (n = 3 81 for each condition, based on 1 independent experiment, with patient and rescued samples collected at 3 time points). *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance, followed by Dunnettâs multiple comparison test for HC and patient samples. Figure 2.46 NK cell phenotyping of the patient and HCsâ PBMC. (A) Representative contour plot of CD16 and CD57 expression in CD56dim NK cells from one HC, the patient, and the patient post-HSCT PBMCs. (B-C) Percentage of (A) CD16 and (B) CD57-producing CD56dim NK cells from a HC, the patient, and the patient post-HSCT PBMCs (n = 6, 3 and 1 for HC, patient, and patient post-HSCT samples, respectively; based on 3 independent experiments). *P < 0.05, **P < 0.01, ***P < 0.001, one-way analysis of variance, followed by Dunnettâs multiple comparison test for HC and patient samples. 2.4 Discussion We report the clinical, epigenetic, and immune profiling of the first case of autosomal recessive ASXL1 deficiency in a female with combined immunodeficiency and EBV-associated Hodgkin lymphoma. Single-patient studies account for approximately one-third of newly identified monogenic immune disorders311, requiring methodical validation to 82 establish causality. Following the guidelines proposed by Casanova et al. for studies in single patients408, we showed that the ASXL1 variants identified in the patient are rare, monogenic, and have complete penetrance. Studies in primary patient cells demonstrated decreased ASXL1 protein abundance, allele-specific functional impairment, dysregulated DNAme, and diminished T cell help and cytotoxic capacity. Finally, we showed that epigenetic and immune defects in the patientâs cells were rescued by transduction of WT ASXL1 in vitro, in parallel with partial recovery of multiple cellular compartments one year post-HSCT. The patientâs rare and severe clinical presentation provided further support for an underlying genetic cause408. They had combined immunodeficiency, with T cell lymphopenia from early childhood followed by progressive loss of B cells. After receiving live-attenuated MMR vaccine, they developed vaccine-strain rubella-positive granulomas. They also had persistent macrocytosis and mild bone marrow hypocellularity, and developed chemorefractory advanced Hodgkin lymphoma at age 14 years. Based on our findings, patients presenting with this unique clinical profile of severe viral-associated complications and immunodeficiency should be evaluated for autosomal recessive ASXL1 deficiency. Heterozygous germline truncating variants near the C-terminus of ASXL1 are known to cause the neurodevelopmental disorder BOS291. The hematologic and immune disorder described here is distinct from BOS in its molecular and clinical effects. We identified biallelic missense ASXL1 variants resulting in decreased ASXL1 protein abundance and abnormal DNAme. Both variants are located outside of the mutational hotspot associated with BOS, and the DNAme signature of our patient does not overlap with that seen in BOS patients298. Previous studies have demonstrated no impairment of ASXL1 protein abundance in BOS and suggest that the C-terminus truncating variants may be gain-of-function131,136. Mouse models indicate that Asxl1â/â mice have developmental abnormalities and failure to thrive, recapitulating elements of the BOS patient phenotype134,294, while Asxl1+/â mice develop an MDS-like phenotype possibly attributable to Asxl1 haploinsufficiency134. Future studies directly comparing samples from patients with 83 autosomal recessive ASXL1 deficiency to those with BOS are needed to further elucidate the mechanistic differences between these two diseases. We attribute the immune disorder observed in our patient to ASXL1 deficiency, as supported by parallels between the patientâs phenotype and Asxl1+/â and Asxl1-mutant mice. This includes impaired self-renewing capacity of hematopoietic stem cells; severely diminished naïve T cells and T-cell exhaustion384; bone marrow hypocellularity409; presence of immature red blood cells and macrocytosis134,294. While differences in ASXL1âs function in mice versus humans limits direct comparisons between the two species, the shared immune and hematologic features observed in the ASXL1-deficient patient and the Asxl1-mutant mice support that altered ASXL1 underlies the patientâs phenotype. EWAS showed significant alterations of DNAme in patient T cells, PBMCs, and whole blood, but not buccal swabs; DNAme decreased predominately in T cells and whole blood, but was increased in PBMCs. The contrasting significance and directionality of EWAS results across tissue types suggest cell type-specific impacts of abnormal ASXL1 on global DNAme. This was further supported by the pronounced increase in Horvath pan-tissue epigenetic age and reduced telomere length observed in patient lymphocytes. Global loss with regional gain of DNAme are key features of aging, as epigenetic marks are ineffectively maintained through the S phase of mitosis410. We showed the patientâs ASXL1 variants drive DNAme loss and increased epigenetic age in T cells. This is supported by recent findings showing high epigenetic age differences in BOS patients with ASXL1 truncating variants298, highlighting the role of ASXL1 in cellular aging. Cellular aging may also be associated with the abnormal ROS production seen in ASXL1-deficienct patient CD4 T cells. Oxidative stress and epigenetic regulation are both linked to cellular aging, and the interplay between the two mechanisms is well documented411â416. Asxl-mutant mice also display mitochondrial dysfunction and cellular senescence384,409; this aligns with our findings linking altered ASXL1 with epigenetic dysregulation and ROS production. The granulomas that emerged at age 3 years represented a particularly enigmatic feature of this patientâs clinical course. These lesions persisted for more than a decade, were 84 refractory to treatment, and were labeled as idiopathic until recognized as being positive for vaccine-strain rubella virus. Rubella virus was first identified in the granulomas of three IEI patients in 2014403, and is now linked to ~60% of idiopathic cutaneous granulomas across a variety of IEIs417â420. Impaired T and NK cell cytotoxicity specifically have been implicated in susceptibility to rubella vaccine-induced granulomas418. Rubella strains identified in affected patients often feature mutations in CD8 T cell and neutralizing B cell epitopes used to evade cellular and humoral responses421, suggesting that defects in both modes of adaptive immunity can contribute to persistent rubella infection. Our study presents a case of rubella-vaccine granulomas in a patient with an IEI caused by defective epigenetic regulation, who showed abnormalities in key imx mune cells critical for viral host defense. As DNAme landscape is distinct across cell populations and crucial to hematopoiesis and the maintenance of cellular identity, it is tempting to hypothesize that epigenetic dysregulation contributes to the T and NK cell deficiency observed in the index patient6,22,187,192,422. Supporting this hypothesis, NKT cells were decreased in the ASXL1-deficient patient, with altered NCAM1 (CD56) DNAme and expression levels. Immunophenotyping demonstrated decreased CD8 T cell proliferative capacity and impaired IL2-STAT5 signaling that was rescued by introduction of WT-ASXL1, and reduced CD16+ CD56dim NK cells, which express granzyme and perforin, and are responsible for antibody-dependent cellular cytotoxicity423. Diminished cytotoxicity coincided with impaired humoral immunity, as reflected in low Tfh prevalence and hypogammaglobulinemia, ultimately creating an environment susceptible to infection and ineffective in forming antiviral memory. Our detailed molecular and clinical evaluation of human ASXL1 deficiency provides further insight into the role of immune dysregulation in cancer predisposition. Global hypomethylation has been proposed as a replicative barrier against highly proliferative and premalignant cells by inducing cellular senescence238,320. In this case of ASXL1 deficiency, however, T cell senescence may have contributed to the patientâs impaired antiviral and antitumor immunity, predisposing to EBV-associated Hodgkin lymphoma424. Indeed, recent modeling of an ASXL1 variant associated with CH showed that it promoted the 85 progression of solid tumors via impaired intra-thymic T-cell development, naïve-memory T-cell imbalance, and T-cell exhaustion384. Consistent with these findings, we also observed the near-absence of recent thymic emigrants and naïve T cells, decreased CD8 proliferative capacity and increased senescence in the patient. As NK and NKT cells play pivotal roles in antitumor immunity, and CD8 T cells have been shown specifically to inhibit EBV-associated lymphoproliferation, the patientâs immune landscape likely resulted in limited immunosurveillance capacity425â430. Some of these mechanisms may also play roles in other hematopoietic malignancies, specifically acute myeloid leukemia, chronic myelomonocytic leukemia, and MDS, where somatic ASXL1 variants are commonly described326,431,432. Taken together, ASXL1 deficiency may drive the maintenance of an oncogenic milieu. This study has many strengths, notably the detailed immunological description of the first human case of biallelic ASXL1 deficiency and well-controlled epigenetic profiling with family controls and a population reference data set across multiple tissues. However, there were also limitations. First, this was a single-patient study, and ultimately the full phenotype of human ASXL1 deficiency will only be defined through diagnosis of more patients. Second, while we observed global differential DNAme in patient whole blood and PBMCs, we explored the effect of WT ASXL1 transduction only in patient T cells. Sample limitations restricted us from working on other cell types (e.g., neutrophils) in a controlled setting. While we suspect that the patientâs missense variants disrupt ASXL1 expression at a post-translational level, our ability to evaluate the mechanisms by which this occurs were limited due to lack of primary samples predating treatment for lymphoma. Future work will aim to further dissect how p.C111Y and p.1237F destabilize ASXL1 protein. Similar to previous findings, we showed the effects of ASXL1 variants to be highly cell type-specific433, and therefore examining different cellular models could elucidate the roles of ASXL1 in hematopoiesis and disease. Finally, although our patient has benefitted clinically from allogeneic HSCT, follow-up was only out to 18 months at the time of writing; we are therefore unable to comment on long-term outcome post-HSCT. 86 In conclusion, our study identified a novel human IEI caused by germline biallelic deleterious ASXL1 variants resulting in ASXL1 deficiency. This discovery will empower the diagnosis of future patients with ASXL1 deficiency, and expands upon the clinical spectrum of ASXL1-associated disease. We recommend genetic testing at the first sign of combined immune deficiency as a powerful precision medicine approach to guide diagnosis and inform therapeutic decision-making. 87 3 Chapter 3: Identifying Rare Disease Epigenetic Signature with Population-Specific Reference DNAme Datasets 3.1 Rationale and Introduction Mutations in genes coding for proteins comprising our epigenetic machinery can lead to RDEOs affecting multiple human systems434,435. Understanding the genetic regions and contexts of the epigenetic disruptions can provide insight into the molecular mechanisms underlying the RDEOs. The epigenetic pattern can also serve as biomarkers for RDEOs that have similar physiology or aid in differential diagnosis between subtypes for the same disease436,437. While this is an exciting area of active research, EWAS in RDEOs are often constrained by a lack of power due to limited sample size, as a result of the disease rarity in the population436,438. Typically, researchers are able to recruit few patients and limited number of HCs with matching demographics, rendering the differential DNAme analysis underpowered except for DNAme sites with strong and consistent disease associations. In addition, the case and control numbers are seldom balanced, leading to an inflation of type 1 error rate, or the likelihood of identifying false-positives439. In the transcriptomic context, previous research has established that comparing rare disease patientsâ profiles against a large dataset of matching controls facilitate the identification of differentially expressed genes440. To implement a similar strategy for DNAme data, it is critical to establish a normative baseline in the healthy population, with matching demographics to control for main contributors to DNAme variability, such as age, sex, and genetic ancestry. With the availability of publicly available DNAme datasets, it has become increasingly possible to create a reference epigenome for characterization of RDEOs189,340,394. In this chapter, I compiled publicly available pediatric datasets with DNAme microarray data and metadata on chronological age and biological sex. I also compared three statistical approaches methods â outlier detection test, one-sample t-test, and permutation test â for DM detection in RDEO cases, using the samples described in Chapter 2 as an example for sensitivity and specificity analyses. 88 3.2 Material and Methods 3.2.1 Sample selection for pediatric population reference set From the GEO repository, I used the dbGetQuery function from GEOquery (v2.38.4) to search for Illumina DNAme microarray data, either measured on the Infinium Human Methylation 450K BeadChip, GPL13534 and GPL16304, or the Infinium MethylationEPIC v1.0 BeadChip, GPL21145 and GPL23976394,395. Additionally, I used the search terms âbloodâ, âmonocyteâ, âPBâ, âCD[4815]â, âlymphocyteâ, âleukocyteâ, âperipheralâ, âbuffyâ, âhaematoâ, âserumâ, âbuccalâ, âoralâ, âBECâ across capitalization, followed by manual curation to select for whole blood, PBMC, and buccal swab tissues. To ensure consistent data preprocessing and minimization of batch effects, I used only cohorts for which raw IDAT files were available in the database. The DNAme data and metadata were then compiled manually. Missing metadata were curated from the GEO data set publications and included variables such as age, sex, tissue, ethnicity, and disease status. For disease status, I used the designation of âhealthy control (HC)â for samples collected from individuals without any notable or persistent severe health conditions, as described in the cohort metadata and the associated publications (Table 3.1-3.3). As inclusion criteria for the HC group, only cohorts that included age information and had samples in the pediatric age range were retained, and HC samples were selected. Table 3.1 Cohorts and demographics information of HC whole blood samples in epigenome-wide association study. Cohort Sample size Sex = Female Sex (percent female) Age (years) GSE102177441 16 8 50% 6.87â14.44 GSE112987442 54 20 37% 6.2â17.9 GSE113012443 13 8 62% 6.31â10.02 GSE113967444 26 7 27% 6â16 GSE125367445 15 7 47% 6â16 GSE142512446 78 30 38% 6.01â17.71 GSE73103447 112 80 71% 14â16 89 GSE87571448 64 32 50% 14â17 GSE97362449 74 21 28% 6â17 Total 452 213 47% 6â17.9 Table 3.2 Cohorts and demographics information of HC PBMC samples in epigenome-wide association study. Cohort Sample size Sex = Female Sex (percent female) Age (years) GSE124366450 78 35 45% 7â13 GSE132181451 194 95 48% 7â7 GSE175379452 13 10 77% 9.0â17.7 Total 285 140 49% 7â17.7 Table 3.3 Cohorts and demographics information of HC buccal swab samples in epigenome-wide association study. 3.2.2 Normalization, QC, and imputation of DNA methylation data For the analysis of primary tissues, the HC cohorts and patient samples were preprocessed together. All subsequent analyses were performed in R version 4.0.3456. The IDAT files were normalized with the preprocessFunnorm function in the minfi package, Cohort Sample size Sex = Female Sex (percent female) Age (years) GSE124366450 35 15 43% 7â11 GSE137502229 21 11 52% 18â18 GSE137894229 69 38 55% 6.01â11.21 GSE137898229 239 113 47% 6.00â10.96 GSE137903229 247 127 51% 6.99â11.96 GSE150901453 12 7 58% 8â8 GSE80261454 92 51 55% 6â18 GSE94734455 10 10 100% 9.40â13.37 Total 725 372 51% 6â18 90 which included two types of normalization: Noob (normalized out-of-band) normalization and functional normalization457,458. The two normalization steps utilized the readings from control and out-of-band probes to adjust for dye bias from the two-color channels and probe-type bias from Type I and Type II probes on the arrays, respectively. Samples from the 450k and EPIC microarrays were normalized separately, and then combined by subsetting to the shared probes. QC was performed at both sample and probe levels. Samples with bisulfite conversion rate < 75 or average detection P-value > 0.01 were removed. Three other outlier detection methodsâdetectOutlier from lumi459, outlyx from watermelon460, and Z-test on the first principal componentâwere implemented to identify outliers with distinct methylation patterns. Furthermore, sex prediction was performed with the getSex function in minfi. Samples with mismatches in reported and predicted sex were also removed. Finally, probes in which > 5% of samples had bead count < 3 or an average detection P-value > 0.05 were filtered out. After QC, the probes with missing values were imputed in a probe-wise manner based on the DNAme patterns of other probes with the lowest Euclidean distance. The method was implemented with the imputeKNN function in the impute package, where k = 10. 3.2.3 Cell type proportion prediction Cell type proportion of whole blood and PBMC DNAme data were estimated from the imputed data with the Houseman reference-based prediction method461, as implemented using the estimateCellCounts2 function from the FlowSorted.Blood.EPIC package with the FlowSorted.BloodExtended.EPIC reference462. In buccal swabs, cell type proportion was predicted with the hepidish function in the EpiDISH package463. 3.2.4 Correcting for variances associated with inter-sample cellular heterogeneity Given that the patient shows unique cell type proportions distinct from the HC, it is essential to adjust for cell type-associated DNAme variation to prevent confounding with the disease-associated DNAme signature. In the primary tissues, this was accounted for 91 with a regression model. I first transformed the proportion into an isometric log ratio followed by principal component analysis (PCA) to extract components that capture the variabilities corresponding to cell type differences. I then fitted a regression model of the DNAme beta value as a function of the cell type PCs3 and extracted the residuals as the cell type-adjusted DNAme values. For the analysis in T cells, we accounted for the CD8 proportion directly as a covariate in our EWAS. 3.2.5 Batch correction Given that we only included samples for which raw IDAT files were available, I also had access to the chip ID and position at which each sample was run. I identified three known sources of batch effect, ie, the array platform (450k or EPIC), chip ID, and chip row. Therefore, I adjusted the data for these three known batch effects with the ComBat function in the sva package377, which uses an empirical Bayesian framework to estimate the batch effect on samples across probes and account for them with a regression model. Each cohort can also be considered as a batch, but that is often confounded with different tissues, disease status, and age range, so we did not include cohort as a known batch variable. The batch effect of the three variables was confirmed to be removed with PCA. Platform ID, chip ID, and chip row were originally strongly correlated with PC1 through PC5 on Pearsonâs correlation test (multiple test Padj < 0.001), and most of the effects were removed by ComBat. For the T cell DNAme data, batch effect was accounted for in the regression model instead, as too few batches were present for ComBat. 3.2.6 Statistical approaches for differential DNA methylation analysis Three statistical approaches for detecting DMs between the HCs and patient samples were considered: Grubbsâ outlier detection test, one-sample t-test, and permutation test. The conceptual basis of each method and their implementation are described as follows. The Grubbsâ outlier detection test is conceptually the most similar to published methods for detection of differential expression signals, by identifying DNAme sites where the patient sample acts as an outlier compared to the HC sample distribution440,464. For each 92 DNAme locus, the Grubbsâ test detects if an outlier exists within the sample dataset, by comparing either the maximum or minimum measurement against the sample mean, given the sample variance465,466. The test statistic, G, is calculated as follows: ðº =ð¥ððð¥â?Ì ?ð or ðº = ?Ì ?âð¥ðððð where ?Ì ? is the sample mean, and ð is the sample standard deviation. The test statistic is compared against the t distribution with n-2 degrees of freedom for p-value calculation. The Grubbsâ test was implemented with the grubbs.test() function in the outliers R package (v0.15). The second approach, one-sample t-test, typically compares a sample dataset against an a priori score467. For the purpose of detecting disease-associated DMs, the patient sampleâs DNAme level is considered the a priori score to which I compare the HC sample distribution against. The test statistic, t, is expressed as: ð¡ = ?Ì ? â ðð âðâ where ?Ì ? is the sample mean, ð is the a priori score, ð is the sample standard deviation, and n is the sample size. The t distribution has n-p-1 degrees of freedom where p is the number of covariates of the model, which is 3 in this case, and hence the t(n-4) distribution was used for p-value calculation. Mathematically, the t statistic is highly similar to the G statistics. However, the two tests are conceptually distinct: under the null hypothesis, the Grubbsâ states that the patient sample is not an outlier to the HC sample distribution, whereas the one-sample t-testâs states that the HC sample mean is not significantly different from the âpopulation meanâ (i.e. the patientâs DNAme). In implementation, I performed the test with limma() in the limma package (v3.48.0), accounting for covariates including age and sex. The previous two tests both assume normality. And the normality assumption was satisfied under both scenarios due to the central limit theorem (CLT). The central limit theorem states that, at a large enough sample size, the sampling distribution is approximately normal irrespective of the population distribution468. To explore a non-93 parametric method no model assumption, we also implemented the sign test with permutations469. The permutation procedure randomly shuffles the patient labels, calculating the mean DNAme of the randomly labelled patient and HC groups each time. In this case, the test statistic is simply the difference in mean DNAme level of the two groups, i.e. ð = ?Ì ? â ?Ì ? where ?Ì ? is the patient sample DNAme level, and ?Ì ? is the HC sample mean. ðððð is the observed mean difference of the actual labels, whereas ðð represents the mean difference of the randomly shuffled group i, for all permutations i = 1,â¦,B. The number of times where the shuffled label yields a sample mean as least as extreme as the actual difference (|ðð| â¥|ðððð |) divided by the total number of shufflings B plus one translates to the two-sided p-value. I shuffled the labels B=3000 times for the permutation test. If, for example, |ðð| is greater than |ðððð | only 5 times, then the p-value is 53001= 0.0017. 3.3 Results 3.3.1 Curation of pediatric population DNAme reference dataset I downloaded and preprocessed DNAme microarray data publicly available on GEO to compile healthy control samples for the creation of population DNAme reference profile. After QC, outlier removal, pediatric age filtering (6-18), and selection for HCs, 452, 285, and 725 samples in whole blood, PBMC, and buccal swabs remained (Tables 3.1-3.3). The HC samplesâ exclusion criteria included any notable disease history or health conditions, including developmental disorders, immune disorders, diabetes, and cancers. 3.3.2 Comparable trends observed across three statistical approaches In order to detect DM patterns between a single patient and a group of HCs, I employed three statistical methods: Grubbsâ outlier detection test, one-sample t-test, and permutation test. The one-sample t-test was performed with limma with biological sex and age accounted for as covariates in the model. The three methods were applied to all three 94 tissue types (i.e. whole blood, PBMC, and buccal swabs). Additionally, a separate permutation test was conducted to compare the sensitivity and specificity of the three methods. To achieve this, the labels of patient and HCs were shuffled 20 times, leading to 20 comparisons of a randomly selected HC against the remaining samples for each of the statistical tests. The distributions of DMs from these comparisons inform the false positive rate of the statistical tests. For example, applying an effect-size threshold appeared to improve the detection rate of true positives, as it resulted in only a minor reduction in the patient DMs, but typically shrink the median HC DMs by around 40% (Table 3.4). Therefore, while I did not apply an effect size threshold in Chapter 2 in the primary tissues, a 5% delta beta cutoff was applied in defining and characterizing DMs in the downstream analyses in this chapter. Similar trends were observed across the three statistical tests. In whole blood, more DMs with significantly decreased DNAme were identified in the patient samples compared to those with increased DNAme (Figure 3.1). In contrast, all three methods consistently identified more DMs with increased DNAme in the patient condition in PBMC and buccal swabs (Figure 3.2 and 3.3). This was especially prominent in the buccal swabs, where all except for one DM showed significantly increased DNAme (Figure 3.3). The consistency suggests that all three tests were sensitive to a global difference in DNAme in the patient sample. Furthermore, compared to the median number of DMs identified with HC samples in the 20 shuffling, more DMs were identified in the patient sample in whole blood and PBMC (Table 3.4). This was not observed in the buccal swabs. This trend was more apparent in Figure 3.4, showing that all three tests identified more DMs in the patientâs whole blood compared to the distribution of the HCs. More DMs were also identified in the patientâs PBMC than most of the HCs, but the difference was not as apparent. as in whole blood. In contrast, the patientâs buccal sample did not have more DMs than the typical distribution observed in the HCs. These findings further suggest that, relative to the baseline level of DNAme variability present in the population, all three detection methods were sensitive to the differential DNAme signature in the patient immune cells. 95 96 Table 3.4 Number of DMs identified with three statistical approaches. The three statistical tests, Grubbsâ outlier detection test, one-sample t-test, and permutation test compared hundreds of HCs against a patient with ASXL1-associated combined immunodeficiency or one HC (results from 20 permutations) in whole blood, PBMC, and buccal swabs. # of DMs detected in whole blood (n = 452) # of DMs detected in PBMC (n = 285) # of DMs detected in buccal swabs (n = 725) Patient HC Patient HC Patient HC Grubbsâ test 11,562 0-1,744 (median: 205.5) 2,814 130-3,799 (median: 451.5) 5 0-386 (median: 16.5) Grubbsâ test (5% delta beta cutoff) 11,387 0-980 (median: 108.5) 1,827 91-2,242 (median: 274) 5 0-348 (median: 10.5) One-sample t-test 50,885 2-21,179 (median: 444) 9,019 310-3,947 (median: 765.5) 42 0-615 (median: 48) One-sample t-test (5% delta beta cutoff) 42,459 2-14,292 (median: 249) 6,166 242-2,284 (median: 468) 41 0-591 (median: 42.5) Permutation test 511 0-166 (median: 8.5) 10 0-8 (median: 2) 21 3-156 (median: 38) Permutation test (5% delta beta cutoff) 495 0-126 (median: 5.5) 4 0-4 (median: 1) 21 2-132 (median: 30) 97 Figure 3.1 Volcano plots of DM signature detected in whole blood. Volcano plot showing effect size (i.e. delta beta) in the x-axis and -log(padj) in y-axis comparing either one patient (left) or one HC (right) against the remaining 452 whole blood samples. The HC condition shown is a representative plot with the median number of DMs amongst the 20 tests. DMs with significantly higher and lower DNAme in the target sample are represented 98 with orange and blue points, respectively. Grubbsâ test and permutation test occasionally yield test p-value of 0, resulting in a row of points aligned with the maximum of the y-axis (506 and 16 with Grubbsâ test for Patient and HC conditions, respectively). Figure 3.2 Volcano plots of DM signature detected in PBMC. Volcano plot showing effect size (i.e. delta beta) in the x-axis and -log(padj) in y-axis comparing either one patient (left) or one HC (right) against the remaining 285 PBMC samples. The HC condition shown 99 is one with the median number of DMs amongst the 20 tests. DMs with significantly higher and lower DNAme in the target sample are represented with orange and blue points, respectively. Grubbsâ test and permutation test occasionally yield test p-value of 0, resulting in a row of points aligned with the maximum of the y-axis (286 and 66 with Grubbsâ test for Patient and HC conditions, respectively). 100 Figure 3.3 Volcano plots of DM signature detected in buccal swabs. Volcano plot showing effect size (i.e. delta beta) in the x-axis and -log(padj) in y-axis comparing either one patient (left) or one HC (right) against the remaining 725 buccal samples. The HC condition shown is one with the median number of DMs amongst the 20 tests. DMs with significantly higher and lower DNAme in the target sample are represented with orange and blue points, respectively. Grubbsâ test and permutation test occasionally yield test p-value of 0, resulting in a row of points aligned with the maximum of the y-axis. 3.3.3 One-sample t-test yielded more unique results than the other two methods Comparing the three methods, one-sample t-test resulted in the highest number of DMs for both the patient and the HCs except for the buccal swabs (Table 3.4). Grubbsâ test and one-sample t-test were found to consistently show high overlap in the DMs identified. That is, almost all the DMs identified with Grubbsâ tests were also DMs according to the one-sample t-test in all three tissues (Figure 3.5). Permutation test also showed high overlap with one-sample t-test with the exception of buccal swabs, where only 38.1% of the DMs identified by permutation test were also identified by one-sample t-test. In contrast, the one-sample t-test generated a high percentage of unique DMs (72.9%, 70.6%, and 68.3% in whole blood, PBMC, and buccal swabs, respectively).101 Figure 3.4 Distribution of the number of significant DMs (with 5% delta beta cutoff) yielded by three statistical tests in whole blood, PBMC, and buccal swabs. For each statistical test and tissue examined, the labels of patient and HCs were shuffled 20 times, resulting in 20 HC-HC comparisons (light blue). The DM result from the actual labels is superimposed (dark purple) to indicate where in the distribution the patient-HC comparison lies. 102 Figure 3.5 Venn diagram showing the numbers of DMs (with 5% delta beta cutoff) identified in the patientâs whole blood, PBMC, and buccal swabs. 3.4 Discussion In this chapter, I described and characterized statistical approaches for detecting DM signatures in cases of RDEOs with very few disease cases. As population characteristics are known to drive DNAme variability, the curation of HC samples with matching demographic information and tissue of interest potentially reduce DNAme variation not associated with disease status, facilitating more apt comparisons when detecting DMs in a single patient. Test results from the three statistical methods, Grubbsâ outlier detection 103 test, one-sample t-test, and permutation test, shared similar patterns in whole blood, PBMC, and buccal swabs. The patientâs whole blood sample consistently showed a high number of DMs compared to the HCs, whereas few DMs were observed in the buccal swabs. These findings echoed the findings in Chapter 2, where we observed minimal DM signature in the patientâs buccal swabsâ epigenetic age when compared against the HCs. Overall, the results demonstrated the sensitivity of all three methods as they were able to detect DMs in the patientâs immune cells beyond the typical DNAme variability observed in the HC population. However, quantifying the specificity and sensitivity of each method required comparisons against the ground truth. In the future, simulation studies with predefined variability and ground-truth DMs should be employed to further characterize the three statistical methods. Comparing the results across statistical approaches, the two parametric tests assuming normality, one-sample t-test and Grubbsâ outlier detection test, yielded results with higher overlap. However, the majority of the significant DMs for one-sample t-test and a substantial portion of them for permutation test were unique. The one-sample t-test is less stringent than the other two tests, both in the sense that it compares the patient DNAme from a single measurement against the HC sample means, but also in that it accounts for standard error of mean instead of standard deviation as in the case of Grubbsâ test. These differences likely led to higher numbers of DMs with the one-sample t-test. Irrespective of the three methods, the approach of comparing one sample against a group of HCs suffer from the inability to assess within-group variance. When DMs are detected, we assume we are able to detect the difference because variance attributable to technical noise and natural population variability is accounted for in a large enough sample size of HCs. However, it is still impossible to truly differentiate the within-group and between-group differences with only one patient sample. With at least a sample size of three in the patient group, we can begin to evaluate technical and other within-group variability to improve the detection of disease-associated signals. Hence, any results yielded from our one-sample-based tests should be interpreted with caution, and only used as exploratory analysis to guide further investigations. 104 Nevertheless, the findings here can guide functional validation, as demonstrated in Chapter 2. As the EWAS on the ASXL1 patient served as an exploratory analysis and aims to evaluate potential downstream regulatory elements, the one-sample t-test results were used as final set of DMs in Chapter 2. The one-sample t-test showed high overlap with the Grubbsâ test while accounting for age and sex variables. Indeed, we were able to show enrichment in relevant chromatin states, including Polycomb-repressed and bivalent regulatory elements (Figure 2.27). In general, with further functional validation, the DMs identified with this approach have the capacity to act as biomarkers for disease detection or therapeutic discovery in the future. With the availability of publicly available data sets, and efforts to create reference epigenomes, such as the Encyclopedia of DNA Elements (ENCODE) project and the NIH Roadmap Epigenomics Project, it is becoming increasingly possible to create a reference epigenome for characterization of RDEOs189. While the currently available reference epigenomes are diverse in the types of epigenetic marks examined and the tissues from which the data were generated, they nevertheless have small sample sizes and are low in demographic diversity. The International Human Epigenome Consortium (IHEC) web portal coordinates the creation and publication of reference epigenome maps from seven consortia, including ENCODE, Roadmap, and Blueprint470. At present, 178 whole-genome bisulfite sequencing (WGBS) data sets in primary human blood cells (including a variety of cell types) and 10 data sets in brain are available on IHEC. By comparing small disease cohorts against large healthy control groups, transcriptomics data have been used to pinpoint disease-associated genes in RDs with variants of unknown significance440,464. By expanding on the reference epigenome efforts and applying a similar outlier detection method to identify significantly dysregulated elements, future studies will better identify the downstream pathways that contribute to the pathophysiology of ultra-rare RDEOs. 105 4 Chapter 4: Advancing Pediatric and Longitudinal DNA Methylation Studies with CellsPickMe, an Integrated Blood Cell Deconvolution Method 4.1 Rationale and Introduction Multiple pregnancy and birth cohorts have been established in recent decades to examine the association between environmental exposures and health outcomes later in life under the DOHaD framework; these studies are often characterized with longitudinal follow-up of deep phenotyping, including DNAme profiling471â475. However, this early developmental period coincides with profound remodeling of the hematopoietic landscape375,476,477. This includes a rapid loss of nucleated red blood cells (nRBCs) after birth478â480, increased neutrophil proportion accompanied with high neutrophil heterogeneity in neonates481â483, and changes in naïve and memory lymphocyte profiles upon antigen exposure including vaccinations476,477,484,485. The dynamic of peripheral blood cell populations provides insight into individualsâ immune status, stress response, as well as developmental and aging trajectory194,363,364,486; yet, it creates an additional layer of complexity to epigenomic investigations, as cellular functions and identities are intrinsically linked to their DNAme signature5,22,191 Longitudinal birth studies have reported persistent and wide-spread alterations in DNAme within the first years of life370,371,487. These studies compared the DNAme patterns of cord blood with that of peripheral blood collected at pediatric time points, ranging from neonates (less than 1 month old), infants (birth â 2 years), children (2 â 12 years), to adolescents (12 â 21 years)370,371,487,488. Several reference-based deconvolution algorithms have been independently developed for adult and cord blood samples254,461â463,489. Yet, without a method to harmonize deconvolution of cord, pediatric, and even adult blood under a single framework, direct inference of DNAme changes across the early developmental period is inevitably confounded by a profound variance in cellular composition193,353,490,491. 106 To address this methodological gap, it is crucial to note that not only does the cellular composition of blood vary with age, but so does the DNAme profile of sorted cells489,492â494. The findings suggest that applying a reference dataset developed from cord blood or adult blood in pediatric populations may introduce prediction errors495. Additionally, performing cellular deconvolution in longitudinal birth cohorts spanning a range of ages currently require multiple, non-comparable references. To overcome this conundrum, we proposed an integrated DNAme reference of cord and adult sorted blood cells, with the goal of capturing cell-type-specific signals that account for changes in the hematopoietic landscape. In addition to reference dataset selection, we evaluated the impact of other steps in the reference-based cell type deconvolution procedure, including data normalization, feature selection, and regression-based prediction496. The algorithmâs performance was assessed using the pediatric blood samples of a Canadian population-based prospective birth cohort, the CHILD cohort473. The resulting reference-based deconvolution procedure, CellPicksMe, facilitates the joint deconvolution of cord and whole blood and supports the inference of cell type proportions in longitudinal pediatric cohorts where age-appropriate reference datasets are currently unavailable. 4.2 Materials and Methods 4.2.1 Cohort description a. Canadian Healthy Infant Longitudinal Development (CHILD) The CHILD cohort is a population-based prospective birth cohort study that recruited 3621 pregnant mothers across four sites in Canada473. At birth, cord blood samples of children enrolled in the study were collected from the umbilical cords (n = 838). At age one and five years (referred to as age 1 and age 5 hereafter), peripheral blood samples were also collected as previously described (n = 1616)497. Out of the participants, the majority of them identified as White (75%), followed by East Asian (8%), South East Asian (5%), First Nations (3%), Multiracial (3%), South Asian (2%), Black (2%), and Hispanic (2%). DNA was extracted from the cord, age 1, and age 5 blood samples, and the DNAme profiles assayed. 107 b. Isle of Wight (IOW) The IOW birth cohort is a whole population prospective study that enrolled all children born on the Isle of Wight, UK, between January 1989 and February 1990 (n = 1536)475. Blood samples were collected from the participants at birth (n = 777) as Gutherie cards, and at age 10 (n = 406), 18 (n = 141), and 26 years (n = 295) as peripheral blood draws. The DNAme profiles were then assessed as previously described498. 4.2.2 DNA sample collection and methylation array for the CHILD cohort DNA was isolated from cord and peripheral whole blood from CHILD cohort samples using the DNeasy Blood & Tissue Kit (Qiagen, Venlo, Netherlands). The DNA concentration and quality were evaluated via a NanoDrop 8000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). For DNA methylation profiling, the purified DNA was bisulfite converted with the EZ-96 DNA Methylation Kit (Zymo Research, Irvine, CA, USA) and assayed with the Infinium MethylationEPIC BeadChip array (Illumina, San Diego, CA). The resulting raw intensity IDAT files contained 866,836 data points covering 863,904 CpG sites. 4.2.3 CHILD cohort DNA methylation data processing All available CHILD whole blood DNAme data were read into and further processed with RStudio (version 4.0.3)499. Quality control was performed using the ewastools v1.7 package based on technical parameters evaluated on the 636 control probes500. The minfi v1.44 package was then used to evaluate methylated and unmethylated intensities, and check for sex concordance between reported and predicted biological sex501. Outliers and poorly performing samples were then identified, with the former detected with the lumi package459,502, and the latter defined as those with a high detection p-values (p > 0.01 in > 1% of probes), DNAme intensities significantly deviating from the average of negative control probes, or a bead count of less than three on more than 1% of probes. The 59 SNP probes on the array were used to further confirm sample relatedness amongst the cord blood, age 1 and age 5 samples for each child. Samples that failed one or more of the 108 quality control metrics were removed (n = 29). After quality control, technical replicates were also removed (n = 22). Overall, 807 samples at age 5 and 795 samples at age 1 remained after sample filtering for cell deconvolution. 4.2.4 Pan-age blood reference-based cell deconvolution We followed the well adopted statistical procedures in cell type deconvolution461, as outlined in Figure 4.1. The method assumed the statistical model: ðµ = â wðbð + ððð=1 where B represented the beta matrix of sample DNAme, and w and b represented the proportion and beta value profile of cell type i in asample with j cell types, respectively. The error term represented variability from either cell types not included in the reference dataset, other biological sources of DNAme variability, or technical noise. The prediction algorithm aimed to calculate bi in a subset of cell-type-specific DNAme sites in the reference dataset for the estimation of wi in the sample dataset. Step 1. Reference selection To create a cell type deconvolution algorithm that allows for cell type prediction in both cord and peripheral blood, four published DNAme datasets of purified blood cell types were combined to create the UniBlood references â the FlowSorted.Blood.450k (Reinius) 503, FlowSorted.Blood.EPIC (IDOL)254, FlowSorted.CordBlood.450k (Cord)489, and FlowSorted.Blood.Extended.EPIC (Extended)462 references. The first two of these encompass 6 immune cell types (B-cells, CD4+ T-cells, CD8+ T-cells, natural killer [NK] cells, monocytes, and granulocytes), whereas the cord blood reference also includes nRBCs. Finally, the Extended reference462 includes 12 cell subtypes sorted at finer granularity (naïve and memory B cells, naïve and memory CD4+ T cells, naïve and memory CD8+ T cells, regulatory T cells (Tregs), NK cells, monocytes, basophils, eosinophils, and neutrophils). We curated three UniBlood references, with different dataset composition. 109 1. UniBlood7: Included IDOL, Reinius, and Cord reference datasets. The cord and adult blood cells of the same types were grouped together (e.g. CD4+ T cells of the 3 datasets were grouped together and considered as one cell type). 2. UniBlood13: Included Extended and nRBCs from the Cord reference. As nRBCs have the most distinct DNAme profile and is absent in adult blood, this data set assumed the other cell types maintain similar DNAme profiles across development, and only the absent nRBC is required to deconvolute cord and pediatric samples. 3. UniBlood19: Included Extended and Cord reference datasets. This reference set considers neonatal and adult blood cells as distinct entities. This data set assumed that neonatal and adult blood cellsâ DNAme profiles are distinct. To ensure equal representation of cord and adult cell types, we randomly sampled 10 samples for each cell type in the Cord reference (seed = 1234) prior to combining the datasets. We compared the influence of UniBlood references on deconvolution accuracy, against that of the Cord, IDOL, and Extended reference. Step 2. Normalization of reference and sample datasets Normalization aimed to reduce batch effect between reference and sample datasets and generate comparable sample distribution to ensure the estimated coefficients in the reference applies to the samples. We compared common normalization strategies, including those applicable to RGChannelSet object (Noob normalization [Noob], as implemented with minfi::preprocessNoob, Functional normalization [Funnorm], as implemented with minfi::preprocessFunnorm, Quantile normalization [Quantile], as implemented with minfi::preprocessQuantile), those applicable to beta matrix (Quantile normalization [Quantile.B], as implemented with limma::normalizeQuantiles), and a no normalization condition. Step 3. Feature selection The process of reference-based cell type prediction then selects probes that best discern the cell types included in the reference. The benchmarking approach uses t-tests to select 110 for probes with the highest mean difference when comparing a given cell type with all others. Alternatively, pre-selected probe sets exist, including IDOL probes490, IDOL-ext probes462, and DNase hypersensitive sites (DHS)504. We proposed new probe selection methods based on machine learning algorithms that have intrinsic feature selection. Random forest (RF), elastic net (EN), boosted logistic regression (BLR), gradient boosted machine (GBM), and classification and regression tree (CART) were implemented with leave-one-out cross validation (LOOCV) using the caret package. Step 4. Coefficient estimation and regression-based prediction Sample cell type proportions are estimated with linear regression under constraints such that wð â¥ 0 and â wð â¤ðð=1 1. The most common implementation of such conditions uses constraint projection (CP) with quadratic programming to optimize the two inequality constraints (Houseman)505. Two other popular approaches use robust partial correlation (RPC) and support vector regression (SVR) (EpiDISH & CIBERSORT)359. These two methods apply the normalization constraints a posteriori by setting negative estimates to 0 and readjusting each estimate proportionally so that the cell types sum up to 1. We compared the deconvolution performance with the three regression methods. 111 Figure 4.1 Schematic diagram of the standard reference-based DNAme cell type prediction pipeline and options evaluated in the optimization procedure. Top: Prediction steps and corresponding functions for implementation in bold (left) and options for the corresponding steps (right), with the bold option being the default in the benchmark method, estimateCellCounts2, and we used these default options as the starting point of the optimization process. Bottom: Methods for evaluation, including the calculation of absolute error, comparison to clinical ranges, and estimation of variance explained. 112 4.2.5 Model Performance Assessment To compare the performance across conditions for a given prediction step, three main metrics were assessed: absolute error (AE), comparison with age-specific clinical count interval, and variance explained accounting for the number of covariates (adjusted R2) for DNAme. For the CHILD samples, the complete blood count (CBC), comprised of lymphocytes, monocytes, and granulocytes portions, was considered as the ground truth. After inference, the predicted proportions were summed for each of the three cell subsets, with neutrophils, basophils, and eosinophils adding up to the granulocyte proportion, and T cells, B cells, and NK cells adding up to the lymphocyte proportion. AE was calculated as the difference between predicted and actual cell type proportions for each subset. One-way ANCOVA tests were applied across conditions to evaluate the differences in AE distribution, with post-hoc Tukeyâs test to correct for multiple testing and identify the groups with significantly different distributions. Additionally, the CEll TYpe deconvolution GOodness (CETYGO) score was calculated as the root mean squared error of the DNAme based on the estimated cell type proportions and the observed DNAme495. The CETYGO score ranges from 0 to 1, with a lower score indicating a better fit of the reference dataset and prediction procedure for the samples of interest. For the comparison with the clinical interval, we calculated the median predicted proportion for each combination, and considered the prediction to overlap with the clinical range reported in the literature if the median falls within the 10th and 90th percentile of the interval (Table 4.1) 375,506â510. Finally, we assessed the variance in the DNAme data explained by cell type proportion estimated across reference datasets and normalization methods. The estimated proportions were first summarized with principal component analysis. We applied EWAS on the estimated cell type PCs accounting for at least 90% of variance explained in the estimated proportion derived from different deconvolution conditions (e.g. A, B,â¦). DNAme ~ CT_PC1conditionA + CT_PC2 conditionA + â¦ + CT_PCx conditionA DNAme ~ CT_PC1 conditionB + CT_PC2 conditionB + â¦ + CT_PCy conditionB 113 We then compared the distribution of the adjusted R2 across measured DNAme sites among prediction conditions. One-way ANOVA with post-hoc Tukeyâs test were utilized to calculate statistical significance. Table 4.1 Median clinical interval of cell populations (10th to 90th percentile) in 5-year-old healthy individuals. The cell counts were measured with flow cytometry or hematology analyzers. Age-specific clinical intervals 1 (10th-90th percentile) Age-specific clinical intervals 2 [male and female average] (10th-90th percentile) % B cells in lymphocytes 22.0 (12.9â29.2)507 15.14 (10.21â21.77)506 % T cells in lymphocytes 68.6 (59.7â77.6)507 67.60 (59.50â74.08)506 % CD4+ T cells in lymphocytes 38.0 (31.1â47.4)507 34.1 (26.17â41.07)506 % CD8+ T cells in lymphocytes 21.0 (16.0â26.9)507 26.13 (19.68â34.06)506 CD4 / CD8 ratio 1.77 (1.26â2.90)507 1.30 (0.87â2.05)506 % NK cells in lymphocytes 8.0 (4.7â16.2)507 15.23 (7.83â22.24)506 % Monocytes in WBC (3.8-10.5)375 % Immature granulocytes in WBC (0.1-1.8)375 % Granulocytes in WBC (14.4â86.1)375 % Neutrophils in WBC (13.8-75)375 % Basophils in WBC (0.1-0.6)375 % Eosinophils in WBC (0.5-10.5)375 % Nucleated RBC in nucleated cells (0-0)375 0.2-1.3508 % Transitional B cells in B cells 6.3 (4.6-8.5)510 6.53 (2.58-12.3)506 % Naïve B cells in B cells 65.7 (47.3â77.0)510 63.43 (48.36â75.84)506 114 % Memory B cells in B cells 27.1 (18.6â46.7)510 13.55 (7.76â20.19)506 % Naïve CD4+ T cells in T cells 67 (46â99)509 62.53 (40.75â75.28)506 % Memory CD4+ T cells in T cells 20.1 (0.62â100)509 36.85 (23.85â59.86)506 % Tregs in T cells 8 (4â14)509 % Naïve CD8+ T cells in T cells 42 (16â100)509 61.65 (38.03â79.08)506 % Memory CD8+ T cells in T cells 52 (21â100)509 35.70 (14.84â71.29)506 4.2.6 Code Availability An R package is available on GitHub (https://github.com/maggie-fu/CellsPickMe) and can be installed through devtools::install_github("maggie-fu/CellsPickMe"). The version used to generate results in this thesis is v1.0.0. 4.3 Results 4.3.1 Cellular maturity and developmental time point are major contributors to DNA methylation variability We first explored whether there is a distinct DNAme signature across developmental stages that can impact pediatric cellular deconvolution process. To examine DNAme variation through the life course, we applied PCA to all probes in sorted DNAme reference data sets of cord and adult blood: FlowSorted.CordBlood.450k (referred as Cord), FlowSorted.Blood.EPIC (referred as IDOL), and FlowSorted.Blood.Extended.EPIC (referred as Extended). Immune cells clustered distinctly on both a lineage axis along PC1(myeloid â lymphoid) and a maturity axis along PC2 (neonatal â naïveâ memory) (Figure 4.2). The nRBCs showed a distinct DNAme pattern, clustering in the middle of lymphocytes and granulocytes on the lineage axis, and further down the maturity axis compared to other cord immune cells. For T and B cells, there was an evident gradient along the maturity axis, transitioning from neonatal, adult naive, a mixture of adult naive and memory (IDOL reference did not distinguish between the two), and adult memory cells (Figure 4.2). 115 Figure 4.2 Principal component analysis showing the main patterns of DNAme variation in sorted cord and adult peripheral blood cell populations. Three of each sorted cell type were randomly sampled and shown to reduce visual cluster. nv: Naïve cells; mem: Memory cells; Treg: Regulatory T cells; NK: Natural killer cells; Mono: Monocytes; Gran: Granulocytes; Neu: Neutrophils; Bas: Basophils; Eos: Eosinophils; nRBC: Nucleated red blood cells. To study whether the pattern of DNAme variation is recapitulated in whole blood where bulk DNAme is representative of all cell types present in the sample, we clustered the DNAme profiles of sorted immune cells with that of cord and whole blood from two longitudinal birth cohorts, the Isle of Wight (IOW) and the Canadian Healthy Infant Longitudinal Development (CHILD) cohorts, along with an artificial blood cell mixture from 116 adult sorted cells, and whole umbilical cord blood from the Cord reference. Aside from technical batch effect and biological sex, which contributed to PC1 and PC2 variance, we observed an analogous trend of lineage and maturity axes along PC3 and PC4 (Figure 4.3). The heterogeneous blood samples clustered in the middle of PC3, as they are composed of a mixture of myeloid and lymphoid cells. These included the CHILD and IOWâs post-natal blood, the artificial blood mixture and the Cord reference cord blood samples. The CHILD cord blood showed a high overlap with the CORD samples and nRBC cells, whereas the IOW neonatal dried blood spots, with a reduced nRBC fraction, clustered adjacently as previously observed511. As the CHILD and IOW participants aged, their samples shifted down the maturity axis (Figure 4.3). At age 1, the CHILD samples clustered discretely from both cord and adult samples. As individuals aged, age 5 CHILD samples and age 10 and 18 IOW samples became more similar, yet remained in a distinct cluster from the age 26 IOW and ABM samples. 4.3.2 Training procedure of reference-based cell type deconvolution Building on evidence indicating that developmental stages in childhood are linked to distinct DNAme profiles, we aimed to refine the cell type deconvolution process for pediatric blood samples. Figure 4.1 shows a flowchart of the prediction process, along with alternative options to the benchmark method, EstimateCellCounts2 (ECC2)490. To that end, we utilized the CHILD cohort, with samples from cord blood at birth and peripheral blood at age 1 and 5 collected from approximately 800 children473. In addition, the absolute lymphocytes, monocytes, and granulocyte composition of the samples were quantified with a clinical-grade CBC, which was considered as the ground truth cell count measure for CHILD. We trained the optimal prediction pipeline in the age 5 samples and tested its validity in age 1 and cord samples. We further examined the performance of the trained cell type prediction pipeline in independent validation cohorts, and compared them to the benchmark method ECC2 in terms of mean absolute error (MAE) of predicted to CBC-based cell type proportions, whether the predicted proportions align with age-specific 117 clinical intervals, and the amount of variance (adjusted R2) explained by the predicted proportions (Figure 4.1). Figure 4.3 Principal component analysis showing the main patterns of DNAme variation in sorted blood cells and whole blood samples. The samples include the sorted cells from Figure 4.2 (plotted as blue gradient symbols), along with umbilical cord blood from Cord reference (plotted as pink square), artificial mixture of adult blood cells (plotted as red diamond), and two additional longitudinal pediatric cohorts: CHILD (plotted as circles) and IOW (plotted as triangles). The ellipses were created based on 95% confidence interval using the ggplot2::stat_ellipse() function. Three of each sorted cell type were randomly sampled and shown to reduce visual cluster. nv: Naïve cells; mem: Memory cells; Treg: Regulatory T cells; NK: Natural killer cells; Mono: Monocytes; Gran: 118 Granulocytes; Neu: Neutrophils; Bas: Basophils; Eos: Eosinophils; nRBC: Nucleated red blood cells. 4.3.3 Normalization impacted prediction performance across reference datasets As the first step of the deconvolution process, we examined which blood reference dataset was most relevant to pediatric cohorts using CHILD age 5 samples. As no pediatric blood reference is available, we created three UniBlood references composed of both adult and cord blood cells to capture cellular heterogeneity across the developmental spectrum (Table 4.2). The UniBlood7 and UniBlood13 references considered the same cell types from cord and adult sorted cells to be the same population, aiming to identify development-agnostic cell-type DNAme signatures. In contrast, UniBlood19 considered cord and adult sorted cells as different populations (e.g. cord and adult monocytes), while taking into account the development-associated DNAme changes in immune cells. Since the literature suggests the performance of the deconvolution to be dependent on normalization methods489, references and normalization methods were evaluated together. We compared six reference datasets (Cord489, UniBlood7, UniBlood13, UniBlood19, IDOL490, and Extended462; Figure 4.1, Table 4.2) in combination with normalization methods commonly employed for DNAme microarray data, including Quantile Normalization (Quantile), normal-exponential out-of-band normalization (Noob), and Functional Normalization (Funnorm), all applicable to RGChannelSet objects only502. To allow for applications in other data objects, we also assessed the performance of quantile normalization on beta matrices (Quantile.B) as well as the no normalization (None) conditions, for a total of 30 combinations of references and normalization methods. 119 Table 4.2 Cellular composition and dataset included for the three reference datasets we compiled in this study, UniBlood7, UniBlood13, and UniBlood19. UniBlood7 UniBlood13 UniBlood19 FlowSorted.Blood.450k ð¸ FlowSorted.Blood.EPIC (IDOL) ð¸ only monocytes, neutrophils, and NK cells only monocytes, neutrophils, and NK cells FlowSorted.Blood.Extended.EPIC (Extended) ð¸ ð¸ FlowSorted.CordBlood.450k (Cord) ð¸ 6 randomly selected nRBC samples 6 randomly selected samples for every cell type Adult and cord cell types grouped? Yes No No Cell types included Seven cell types: B cells, CD4+ T cells, CD8+ T cells, NK cells, monocytes, granulocytes, and nRBCs Thirteen cell types: naïve and memory B cells, naïve and memory CD4+ T cells, naïve and memory CD8+ T cells, Tregs, NK cells, monocytes, neutrophils, basophils, eosinophils, and nRBCs Nineteen cell types: neonatal, naïve, and memory B cells, neonatal, naïve, and memory CD4+ T cells, neonatal, naïve, and memory CD8+ T cells, adult Tregs, neonatal and adult NK cells, neonatal and adult monocytes, neonatal granulocytes, adult neutrophils, basophils, and eosinophils, and nRBCs 120 We compared the prediction performance of references by calculating the absolute error (AE) of predicted to lymphocyte, monocyte, and granulocyte proportions measured with CBC. We observed the performance of references to be dependent on the normalization method. For example, when comparing across references under Noob or Funnorm normalization, UniBlood13, UniBlood19, and Extended references resulted in the lowest AE (padj < 0.05, Figure 4.4). On the other hand, IDOL reference yielded the lowest AE with Quantile normalization. For each condition, we also calculated the CETYGO score, which estimates deconvolution accuracy based on the deviation between a samplesâ DNAme and the expected DNAme based the predicted cell type proportion and the reference profiles495. In concordance to the AE, Extended and UniBlood19 led to lower CETYGO score (indicating better prediction performance) with Noob and Funnorm normalization, whereas Extended and IDOL resulted in lower CETYGO score with Quantile normalization (Figure 4.5). Additionally, Quantile normalization led to the lowest prediction error across the board for all reference panels (Figure 4.4). Statistically, with cell type and reference panel accounted for, the Quantile normalization method significantly outperformed all other methods (Table 4.3). The RGChannelSet-based normalization methods consistently outperformed Quantile.B (padj < 2.22e-16), which in turn significantly outperformed the no normalization condition (padj = 1.68e-5). CETYGO score also corroborated the improved performance with Quantile normalization (Figure 4.5). Despite the utility of CETYGO score, we showed that the metric failed to predict AE at the sample level (Figure 4.6). Given the consistent performance of Quantile normalization, we decided to move forward with this normalization procedure. 121 122 Figure 4.4 Violin plot of absolute prediction error of cell type proportions across normalization methods and reference panels in CHILD age 5 samples. Absolute prediction error was evaluated as the difference between predicted cell type proportion and actual proportion derived from complete blood count. The mean absolute error of a given reference-normalization (columns-rows) method pair condition is shown above the violin plot, with compact letter display indicating the significance of absolute error differences across reference panels within a normalization method. Groups with the same letter are not significantly different (padj > 0.05) whereas those with different letters are significantly different (padj â¤ 0.05). Significance is calculated with one-way ANOVA with post-hoc Tukeyâs test. 123 Figure 4.5 Violin plot of CEYGO score across normalization methods and reference panels in CHILD age 5 samples. The mean CETYGO score of a given reference panel-normalization (columns-rows) method pair condition is shown above the violin plot, with compact letter display indicating the significance of absolute error differences across 124 reference panels within a normalization method. Groups with the same letter are not significantly different (padj > 0.05) whereas those with different letters are significantly different (padj â¤ 0.05). Significance is calculated with one-way ANOVA with post-hoc Tukeyâs test, with multiple test correction with Bonferroni method. Table 4.3 Pair-wise comparison of prediction error pooled over all references. Statistical significance was calculated with ANOVA with post-hoc Tukey HSD (Honestly Significant Difference) test. For each comparison, the normalization method that yielded significantly lowered AE is boldened and colored in navy blue. group1 group2 statistic Df p p.adj p.adj.signif Noob Funnorm 2.1 25949 3.60E-02 3.60E-01 ns Noob Quantile 35.5 24830 3.26E-269 3.26E-268 **** Noob Quantile.B -71.4 17923 0 0 **** Noob None -81.9 18587 0 0 **** Funnorm Quantile 33.5 24963 1.96E-240 1.96E-239 **** Funnorm Quantile.B -72.8 17796 0 0 **** Funnorm None -83.4 18446 0 0 **** Quantile Quantile.B -92.7 16260 0 0 **** Quantile None -105 16725 0 0 **** Quantile.B None -4.79 25832 1.68E-06 1.68E-05 **** 125 Figure 4.6 Correlation of absolute prediction error of cell type proportions and CETYGO score for age 5 CHILD samples. Absolute prediction error was evaluated as the difference between predicted cell type proportion and actual proportion derived from complete blood count. The lymphocytes (brown), monocytes (green), and granulocytes (blue) were plotted separately. 4.3.4 UniBlood19 reference unveiled epigenetic signatures associated with developmental immune cell transition As the previous analysis focused on the prediction accuracy of the CBC subsets: lymphocytes, monocytes, and granulocytes, we next examined whether the predicted cell type proportions were accurate in additional cell type subsets (e.g. naïve CD4+ T cells). We 126 compared the estimated proportions to age-specific clinical intervals reported in literature as we expected CHILD age 5 participants to align with healthy parameters506â510,512. Using Quantile normalization, we observed a full alignment with clinical intervals for references predicting 6 to 7 cell types (Cord, UniBlood7, and IDOL) (Figure 4.7). With increasing cell type resolution, we observed some deviation from the clinical intervals. Extended, UniBlood13, and UniBlood19 all overpredicted the proportion of CD8+ T cells and Tregs, and underpredicted the proportion of eosinophils (Figure 4.7). Extended and UniBlood13 also overpredicted naive B cells, whereas UniBlood13 and UniBlood19 underpredicted memory CD4+ T cells. UniBlood13 reference yielded predictions that fell far outside the clinical intervals for multiple cell types, while the predictions with UniBlood19 and Extended were closer to expected based on clinical range. Noob normalization led to higher alignment with the clinical intervals for references predicting 12 or more cell types, whereas not performing normalization led to significant deviation from clinical intervals across references for most cell populations, highlighting the role of data normalization in deconvolution accuracy (Figure 4.8). Overall, IDOL reference had the highest percentage of predicted categories matching the clinical intervals across normalization methods, partly because it predicted fewer cell types (Table 4.4), and Quantile normalization led to the highest alignment with clinical intervals, closely followed by Noob normalization. 127 Figure 4.7 Age-specific clinical interval of hematological fractions and predicted cell type proportions of CHILD age 5 blood samples (quantile normalized). The clinical intervals (gray bars) were based on children at 5 years of age, compared against the median estimated cell type proportions of CHILD participants (dots). Six cell reference datasets were employed (columns) and Quantile normalization was performed. The clinical intervals were compiled from six publications with participants across sexes and of various genetic ancestry375,506â510, all measured with flow cytometry or hematology analyzers. The range presented here represents the widest 10th and 90th percentile range reported across literature (Supplementary Table 3). The colored dots are the median predicted proportion of a given cell population (salmon: inside clinical interval; black: outside clinical interval). For transitional B cells and immature granulocytes, the values correspond to estimated neonatal B cells and neonatal granulocytes proportions. WBC: white blood cells. 128 129 Figure 4.8 Age-specific clinical interval of hematological fractions and predicted cell type proportions of CHILD age 5 blood samples. The clinical intervals (gray bars) were based on children at 5 years of age, compared against the mean estimated cell type proportions of CHILD participants (dots). Six cell reference datasets were employed (columns) where A) Noob or B) no normalization was performed. The clinical intervals were compiled from six publications with participants across sexes and of various genetic ancestry375,506â510, all measured with flow cytometry or hematology analyzers. The range presented here represents the most lenient 10th and 90th percentile range reported across literature (Supplementary Table 3). The colored dots are the median predicted proportion of a given cell population (salmon: inside clinical interval; black: outside clinical interval). For transitional B cells and immature granulocytes, the values correspond to estimated neonatal B cells and neonatal granulocytes proportions. WBC: white blood cells. Table 4.4 Percentage of median predicted cell populations that fell within the clinical intervals, out of the categories predicted. The normalization method that led to the highest percentage of accurate predictions for each reference dataset is boldened. Median predicted proportions (10th-90th Percentile) Cord UniBlood7 IDOL UniBlood13 UniBlood19 Extended Average Noob normalization (RGset) 77.78% 88.89% 100.00% 63.16% 80.95% 83.33% 82.35% Functional normalization (RGset) 77.78% 77.78% 100.00% 63.16% 71.43% 61.11% 75.21% Quantile normalization (RGset) 100.00% 100.00% 100.00% 52.63% 71.43% 77.78% 83.64% Quantile normalization (Beta matrix) 66.67% 33.33% 62.50% 42.11% 33.33% 50.00% 47.99% No normalization 55.56% 55.56% 37.50% 42.11% 33.33% 38.89% 43.83% Average 75.56% 71.11% 80.00% 52.63% 58.09% 62.22% 130 By stratifying cord and adult immune cells in our reference panel, UniBlood19, we were able to delineate cell types in our pediatric samples that more closely resembled cord immune cells. These cell populations are more prevalent in peripheral blood during the pediatric time interval, such as immature granulocytes (predicted as neonatal granulocytes) and transitional B cells (predicted as neonatal B cells)375,512. We predicted a low prevalence of both populations in CHILD age 5 samples, showing an underprediction of transitional B cells compared to the clinical interval (Figure 4.8). To further illustrate the differences in deconvolution output across several reference datasets, we visualized the estimated cell type proportions of CHILD age 5 samples using the UniBlood19 reference (Figure 4.9). We estimated a non-negligible proportion of neonatal cells, suggesting some of the immune cells at age 5 exhibit DNAme profiles more similar to that of neonatal cells. To explore whether this finding reflects biological changes during immune development or simply an artifact of the algorithm, we performed the same prediction with UniBlood19 reference for 3 other data sets: CHILD age 1, an artificial adult blood cell mixture (GSE182379) and an adult whole blood cohort (GSE112618). We observed higher estimates of neonatal cell type proportions in the pediatric age range and a decline with age (Figure 4.10). Finally, we wished to know which reference yielded predictions that explained the highest amount of variance. We observed that the deconvolution derived from UniBlood19 (the reference with the highest granularity) yielded the highest adjusted R2 estimates (Figure 4.11). Overall, we selected two reference panels in our analyses: IDOL and UniBlood19. The IDOL reference was chosen for the lowest MAE when Quantile normalization was employed, and the UniBlood19 reference was selected due to the higher variance explained, and the potential of investigating cord to adult immune transition in future studies. 131 Figure 4.9 Estimated blood cell proportions in age 5 CHILD samples across Extended, UniBlood13, and UniBlood19 reference panels. The â_cordâ suffix indicates predicted neonatal cells. nv: Naïve cells; mem: Memory cells; Treg: Regulatory T cells; NK: Natural killer cells; Mono: Monocytes; Gran: Granulocytes; Neu: Neutrophils; Bas: Basophils; Eos: Eosinophils; nRBC: Nucleated red blood cells. 132 Figure 4.10 Estimated blood cell proportions with the UniBlood19 reference panel for samples across age groups. Four datasets were evaluated: CHILD age 1, CHILD age 5, artificial mixture of adult-derived blood cells (GSE182379) and adult whole blood samples (GSE112618). The â_cordâ suffix indicates predicted neonatal cells. nv: Naïve cells; mem: Memory cells; Treg: Regulatory T cells; NK: Natural killer cells; Mono: Monocytes; Gran: Granulocytes; Neu: Neutrophils; Bas: Basophils; Eos: Eosinophils; nRBC: Nucleated red blood cells. 133 Figure 4.11 DNAme variance explained by cell type PCs in age 5 CHILD samples using quantile normalization. Density plot showing the distribution of adjusted R2, as DNAme is fitted in a regression model with cell type PCs accounting for more than 90% variance of predicted cell type proportions. For Cord, UniBlood7, IDOL, Extended, UniBlood13, and UniBlood19 references, 3, 3, 4, 5, 4, and 7 PCs were included, respectively. The vertical dotted line corresponds to the mean of the density distribution. Statistical significance comparing R2 distribution was calculated with one-way ANOVA with post-hoc Tukeyâs test, and shown with compact letter displayed. 134 4.3.5 Elastic net and random forest consistently performed well amongst feature selection methods The next step in the deconvolution process is feature selection to identify a subset of DNAme loci best predicting each cell type. For the benchmark method ECC2, the default method for feature selection is based on top 100 probes selected from T-tests for each cell type. While several preselected probe sets for cell type deconvolution are available462,490,513, they fail to account for the normalization procedure, which can affect both the features selected and the coefficients estimated. We employed machine learning methods, including lasso, EN, RF, BLR, CART, and GBM to curate a list of DNAme probes predictive of cell type identity post normalization. Similar to previous sections, we evaluated the prediction performance with MAE to CBC. Comparing the machine learning feature selection methods against the default method, t-tests using 1000 probes per cell type for prediction, we found multiple methods (all except for lasso and BLR) yielding comparable performance to t-tests for the IDOL reference (Figure 4.12). In contrast, EN and RF significantly outperformed t-test and other methods for UniBlood19 reference (Figure 4.13). To assess the effect of selected CpGs on cell type proportion prediction performance, we altered the number of probes supplemented for each cell type (k) from 10 to 5000. No clear relation between k and MAE was observed for IDOL reference, but t-test, EN, and RF consistently resulted in low MAE, with the first two yielding slight increased MAE past k = 1000, whereas RF performance improved with increasing k (Figure 4.14). For the UniBlood19 reference, MAE decreased as k increased for t-test, RF, and EN, with slight exception with k = 5000 (Figure 4.15). Specifically, EN consistently yielded the lowest MAE with k > 300. Overall, EN with k = 1000 performed well for both IDOL and UniBlood19 references, and was chosen as the feature selection method moving forward. Finally, we observed an equivalent performance across regression methods in the CHILD blood sample, so we decided to move forward with constraint projection that is well adopted in blood (Figure 4.16)461. 135 Figure 4.12 Absolute prediction error of cell type proportions of age 5 CHILD samples across machine-learning-based feature selection methods based on IDOL reference. Absolute prediction error was evaluated as the difference between predicted cell type proportion and actual proportion derived from complete blood count. Reference and sample DNAme data were Quantile normalized, and top 1000 cell-type-specific probes were selected per cell type. The mean absolute error of a given feature selection method is shown above the violin plot, with compact letter display indicating the of significance of error distribution across groups. Significance was calculated with one-way ANOVA with post-hoc Tukeyâs test. 136 Figure 4.13 Absolute prediction error of cell type proportions of age 5 CHILD samples across feature selection methods based on UniBlood19 reference. Absolute prediction error was evaluated as the difference between predicted cell type proportion and actual proportion derived from complete blood count. Reference and sample DNAme data were Quantile normalized, and top 1000 cell-type-specific probes were selected per cell type. The mean absolute error of a given feature selection method is shown above the violin plot, with compact letter display indicating the of significance of error distribution across groups. Significance was calculated with one-way ANOVA with post-hoc Tukeyâs test. 137 Figure 4.14 Mean absolute error of predicted cell type proportions of age 5 CHILD samples across varying number of available features (k) for each feature selection method based on IDOL reference. Figure 4.15 Mean absolute error of predicted cell type proportions of age 5 CHILD samples across varying number of available features (k) for each feature selection method based on UniBlood19 reference. 138 Figure 4.16 Absolute prediction error of cell type proportions across regression methods in age 5 CHILD samples. Absolute prediction error was evaluated as the difference between predicted cell type proportion and actual proportion derived from complete blood count. The mean absolute error of a given regression method is shown above the violin plot, with compact letter display indicating the of significance of error distribution across groups. Significance was calculated with one-way ANOVA with post-hoc Tukeyâs test. CP â constraint projection; RPC â robust partial correlation; SVR â support vector regression. 4.3.6 The trained pipeline outperformed benchmark methods in test datasets After optimizing the pipeline in CHILD age 5 samples, with quantile normalization, IDOL or UniBlood19 reference, EN feature selection, and constraint projection (hereafter referred to as the CellsPickMe algorithm), we evaluated the CellsPickMe performance by comparing it against benchmark algorithms in the reserved CHILD and external validation cohorts. We first explored the performance of CHILD age 1 peripheral blood and cord blood. We found the CellsPickMe algorithm with UniBlood19 reference led to lower MAE than ECC2 with either the IDOL or Extended reference, albeit with a small effect size (Figure 4.17). We observed a similar phenomenon in the CHILD cord blood samples as well. Although not an exclusive cord blood reference, CellsPickMe with the UniBlood19 139 reference achieved lower MAE than ECC2 with the Cord reference, with or without using the preselected IDOL probes (Figure 4.18). Furthermore, the CellsPickMe algorithm predicted predominantly neonatal cells with comparable estimated nRBC proportions to using the Cord reference, demonstrating the algorithmâs ability to detect DNAme signature specific to neonatal cells and applied them in deconvolution (Figure 4.19). Figure 4.17 Absolute prediction error of cell type proportions in reserved CHILD age 1 samples across deconvolution pipelines. The performance of the CellsPickMe deconvolution algorithm and the benchmark estimateCellCounts2 (ECC2) methods were compared. The mean absolute error of a given regression method is shown above the violin plot, with compact letter display indicating the of significance of error distribution across groups. Significance was calculated with one-way ANOVA with post-hoc Tukeyâs test. 140 Figure 4.18 Absolute prediction error of cell type proportions in reserved CHILD cord blood samples. The performance of the CellsPickMe deconvolution algorithm and the benchmark estimateCellCounts2 (ECC2) methods were compared. The mean absolute error of a given regression method is shown above the violin plot, with compact letter display indicating the of significance of error distribution across groups. Significance was calculated with one-way ANOVA with post-hoc Tukeyâs test. 141 Figure 4.19 Deconvolution prediction results for CHILD cord blood across methods. The benchmark estimateCellCounts2 (ECC2) with or without the IDOL probe list were compared against CellsPickMe with UniBlood19 reference. 142 Finally, we validated the performance of CellsPickMe in an independent cohort. We utilized both the IDOL and UniBlood19 references and compared their performance to ECC2 using either the IDOL or Extended reference. In GSE112618 (adult whole blood), we observed the lowest MAE using CellsPickMe with IDOL (padj < 0.05), and a comparable MAE between CellsPickMe with UniBlood19 reference and ECC2 with Extended reference (Figure 4.20). Additionally, CellsPickMe with IDOL resulted in minor yet significant reduction in prediction error when compared against other methods in adult blood, demonstrating the utility of machine learning-based feature selection method even with the same reference dataset being employed. Figure 4.20 Absolute prediction error of cell type proportions in an external validation adult whole blood dataset (GSE112618). The performance of the CellsPickMe deconvolution algorithm and the benchmark estimateCellCounts2 (ECC2) methods were compared. The mean absolute error of a given regression method is shown above the violin plot, with compact letter display indicating the of significance of error distribution across groups. Significance was calculated with one-way ANOVA with post-hoc Tukeyâs test. 143 4.4 Discussion Through systematic and rigorous evaluation of algorithmic performance, we trained a cell deconvolution procedure, CellsPickMe, that can be applied to blood datasets across the developmental spectrum. The algorithm enhanced cell type prediction with features such as multi-age reference datasets, data-driven feature selection, and performance evaluation using CETYGO. In our optimization process, we noted that quantile normalization with the IDOL reference led to low prediction error, whereas the UniBlood19 reference uniquely elucidated cell type proportion differences in longitudinal birth cohort studies with matched cord and pediatric samples. Machine learning approaches, like EN and RF, not only matched or slightly improved prediction accuracy, but also offered a defined set of cell type-specific probes for downstream analysis. Ultimately, the trained pipeline â featuring quantile normalization, the UniBlood19 or IDOL reference, EN, and constraint projection â surpassed the existing benchmark in predicting cell types for CHILD age 1 and cord blood samples, as well as external adult datasets. We were inspired to create the UniBlood reference datasets because of one curious phenomenon observed in the PCA of sorted cellsâ DNAme, that there was a distinct maturity axis amongst the top PCs. Interestingly, the cord samples exist on this continuum as an extension of the naïve cell state. When we overlayed DNAme profiles of various developmental time points with that from the sorted cells, there was a distinct cluster of age 1 samples away from the cord blood, and another of the age 5 samples that was closer to the adult blood than their age 1 counterpart. This suggests pediatric cell types have intermediate DNAme profiles not currently accounted for in existing references. Our findings align with immune phenotype profiling with mass cytometry, which has shown that the immune landscape changes drastically immediately post-birth, with uncorrelated profiles between cord blood to that one-week post birth.477 We also observed this rapid shift in the immune system reflected in the optimization process, where the IDOL reference consistently showed high prediction accuracy and high alignment with age-specific clinical intervals. This suggests that, while the DNAme profiles of pediatric blood at age 5 is distinct from both cord blood and adult blood, it is more like the latter. The 144 observation speaks to the need to have a reference dataset with unified cord and adult blood cells to accurately assess this cellular transition in the pediatric age range. With the postnatal loss of nRBCs and rapid shift in neonatal immune system, DNAme research has reported challenges in integrating cord blood and pediatric peripheral blood370,371. Applying adult references on cord blood has been shown to be inadequate in accounting for cellular heterogeneity489. Similarly, we have demonstrated that applying cord references in pediatric peripheral blood samples yielded poor performance. The three UniBlood references offer the strategic advantage of harmonizing deconvolution of samples collected across multiple developmental time points under a single framework. This facilitates comparisons of association test results across developmental time points because cellular composition can be accounted for with the same constituent cell types. We also noted that UniBlood7 and UniBlood13 underperformed compared to UniBlood19, potentially because the former two references intended to identify developmentally agnostic cell type markers when cell-type-specific DNAme patterns changed significantly with early development. As a result, UniBlood19, which treated cord and adult blood cells as independent identities, was able to better capture cell-type-specific DNAme signatures and resulted in improved prediction performance. The literature has shown that neonatal immune cells are distinct from their adult counterpart in both identity and functions, including B cells514â516, T cells517â519, monocytes520,521, NK cells522,523, and granulocytes524,525. The neonatal immune system needs to be simultaneously capable of tolerating the maternal environment, as well as rapidly adapting to the pathogen exposure upon birth.514,518,519 Because of the rapid shift from maternally derived immunity after birth, it has been observed that immature granulocytes and transitional B cells are more prevalent in cord blood, with diminishing proportions later in life515,510,512,477,524,375. The neonatal immune cellsâ DNAme profile was sufficiently unique that CellsPickMe was able to accurately reproduced this dynamic, predicting high proportion of neonatal immune cells in cord blood and decreasing proportion in samples of older ages. Furthermore, the UniBlood19 reference can estimate neonatal cell populations, such as neonatal B cells and neonatal granulocytes. Quantifying these cell 145 types not only informs immune development but also functions as relevant medical biomarkers. For instance, as transitional B cells serve as a pivotal link between immature and mature B cells â negative selection against autoreactive clones â altered frequency of transitional B-cell subsets have been linked to systemic lupus erythematosus and other autoimmune disorders526â528. In the example of immature granulocytes, we hypothesize that the captured DNAme signature is linked to not just immature granulocytes, but also low density neutrophils (LDNs), as their identity overlap in cord blood524. LDNs are lighter neutrophils that are found in peripheral blood mononuclear cells after gradient centrifugation529. These cells possess immunosuppressive or proinflammatory characteristics, and have higher abundance in cord blood, pregnancy, or in patients with autoimmune diseases, cancer, and infection482,529â532. Our findings suggest that epigenetics is a viable avenue to explore immune cell development and transition across developmental trajectory, supplementing useful yet distinct information to commonly studied immune markers like cell surface markers and cytokine milieu. As cell identity and epigenetics are inextricably linked, one of the challenges was the selection of DNAme sites that are most predictive of given cell types. We aimed to build an algorithm that is both robust and adaptive. Instead of generating a curated list of DNAme sites, we performed the feature selection step post-normalization because the prediction performance is dependent on the preprocessing pipeline, as evidenced by both the discrepancy in MAE and alignment with clinical interval with and without normalization. We explored a handful of machine learning methods with embedded regularization steps to identify the probe sets that best predict cell type identity. Surprisingly, the benchmark method of selecting 100 probes with t-tests consistently performed well across experimental conditions. The results point to the relevance of cross-validating a range of machine learning algorithms for optimization purposes. The observation echoed the âno free lunchâ theorem for optimization, which proposes that the optimal solution of a given problem varies, and no one solution is superior for all problems or data sets.533,534 We also tested a range of k â number of initial features available for the regularization algorithm â and showed, for IDOL reference, optimal performance can be achieved with t-146 tests, EN, and RF, with as few as 10 probes per cell type. This option can potentially be implemented for users who wish to improve deconvolution efficiency both in terms of time and computational memory usage. Ultimately, we observed that EN and RF consistently resulted in low prediction error at higher k. For users who intend to investigate the biological process underlying immune cell-specific DNAme signature, we recommend performing feature selection with EN or RF with k between 1000 and 5000. While our study hints towards the application of DNAme-based deconvolution in studying immune system dynamics, there are some aspects that warrant further consideration. For one, CBC was considered as the ground truth cell count measure for CHILD. Despite being a well-established method for examining cellular composition in clinical settings, it cannot resolve lymphocyte subsets, which is critical for understanding immune responses535. Furthermore, collapsing the subsets can potentially bias the performance assessment process. We also do not have the genetic information of the samples included in the reference datasets. There can potentially be cell-type-specific methylation quantitative trait loci that drive feature selection, which can impact the deconvolution performance. Another consideration when utilizing CellsPickMe is the relatively longer runtime compared to other methods, but this is due to the inclusion of normalization and pre-selection of the prediction features which increases the rigor and reproducibility of our tool. While our method is more time-consuming, we have shown that these two steps significantly improve deconvolution outcomes, but computational demands should be considered when approaching these methods. Finally, despite having validated the prediction accuracy of CellsPickMe in CHILD as well as external cord and adult blood datasets, we have not examined its performance in more diverse settings, such as non-predominantly European genetic ancestry, elderly populations, or diseased conditions. Future studies can address the gap by comparing CellsPickMe predicted cell type proportions to empirical cell counts in these cohorts. In this study, we demonstrated CellsPickMe's superior performance beyond pediatric datasets, to cord and adult blood samples as well. Additionally, the feature selection methods employed in CellsPickMe demonstrated improved prediction performance 147 across validation datasets we tested and output a list of cell-type-specific DNAme sites that can be further explored. We also reported on the utility of applying CETYGO score for evaluation of the appropriateness of given reference datasets and normalization methods and incorporated this internal error assessment metric in the package for easy application. Overall, CellsPickMe improved upon existing cellular deconvolution methods in blood, enhancing researchersâ ability to explore and account for immune dynamics in their DNAme investigations, especially in longitudinal pediatric studies. 148 5 Chapter 5: Examining Cellular Heterogeneity in Human DNA Methylation Studies: Overview and Recommendations 5.1 Rationale and Introduction The chapter reviews the current literature on the best practices in estimating and accounting for ISCH in human DNAme studies. While the methods referenced in the chapter are often developed specifically for population-based studies, they are generally applicable to human DNAme studies. When researchers set out to examine cellular heterogeneity in epigenomic studies, the process typically involves, first, the estimation of ISCH in the cohort, followed by adjustment for it in downstream analysis, or, alternatively, the estimation of the cell type-specific differential DNAme signature. In this chapter, we outline the bioinformatic deconvolution tools DNAme currently employed to estimate cell type proportions from DNAme measures. We discuss the applicability of both reference-based and reference-free deconvolution methods. In addition, we provide code examples for ISCH prediction in four commonly used tissues: blood, buccal swabs, saliva, and brain. Then, we present methods developed to adjust for ISCH in the context of EWAS or other analyses. Finally, we discuss three algorithms to estimate cell type-specific DNAme associations, as well as limitations of the methods. A decision scheme is provided in Figure 5.1 to guide the decision process, depending on the ultimate goal of their studies, and their respective available measures. 149 Figure 5.1 Decision scheme for examining ISCH in DNAme studies. Starting from the cellular composition of the dataset, if the samples are composed of heterogeneous cell populations, follow the decision tree to determine optimal strategy to account for the ISCH. Blue boxes signify decision points, and yellow circles indicate the sections in this article that contain relevant detail. The purple circles represent different analysis strategies depending on the usersâ dataset characteristics and desired analysis. EWAS, epigenome-wide association study. Created with BioRender.com. 150 5.2 Main All pseudocode in this article is based on R and tested in R Studio456,499. We have included a vignette with example dataset for the readers to follow along and examine output format at https://rpubs.com/mfu/1217877. To ensure that the packages can be installed and run as presented here, we recommend R version 4.1.0+. As general setup, there are several required R packages for the code example provided in each section, which are mentioned at the beginning of each code block. Some of these are available on CRAN, and others are on Bioconductor536. The example code in Box 1 showcases how the packages can be downloaded from respective repositories. Box 1. Pseudocode for package installation in R The code example and the packages used are also optimized for the most commonly used Illumina Infinium BeadChip microarrays, both the 450K array and its successor, the EPIC (850K) array347,349. The methods can be adapted for the latest EPIC v2 array by specifying the annotation (see below), and sequencing-based DNAme profiling methods with packages like methyLiftover as well537,538. Estimation of ISCH usually requires some level of preprocessing of raw DNAme array data to obtain either an RGChannelSet object or a beta matrixâi.e., a table of beta values corresponding to the percentage of cells methylated with values ranging from 0 to 1. For ease of data processing, we present a curated list of cell type prediction tools compatible with each input type in Table 1. In most cases, preprocessing should be applied prior to extraction of the beta matrix, including quality control, background color correction, and probe type normalization539. Preprocessed beta # Install CRAN packages install.packages("dplyr") # Install Bioconductor packages if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("minfi") 151 matrix will be the expected input for methods that account for ISCH or estimate cell type-specific DNAme signature. Box 2. Pseudocode for reading raw IDAT files into R, and minimal preprocessing for beta matrix extraction 5.2.1 Obtaining ISCH information ISCH information of a dataset can be obtained via several methods, broadly divided into direct and indirect types. Direct method types include staining and microscopy of histological samples, clinical report of complete blood count (CBC), flow cytometry, and cytometry by time of flight (CyTOF), whereas DNAme-based ISCH prediction is considered indirect. Depending on the tissue of interest, DNAme-based prediction may be more or less accurate or granular than alternative sources of cell count information. In cases of blood, for example, the most advanced DNAme-based prediction gives higher resolution of cell type specificity than CBC, because of the ability to resolve populations of adaptive immune cells462,490,540. On the other hand, flow cytometry and CyTOF provide greater granularity in cell counts, and are thus considered the gold standards for cell count information194,489. The reported correlations between flow counts and predicted counts range from 0.15 to 0.99, depending on the cell types and prediction methods254,489. When the direct cell count measurement provides higher resolution than the bioinformatic prediction, the user can either bypass the prediction or compare the predicted ISCH with cell count measurement as a sanity check (refers to the Table 1 for available cell types for each reference dataset). library(minfi) library(dplyr) yourRGChannelSet <- read.metharray.exp("GSE112618_RAW") # Minimal preprocessing - only background color correction and inter-sample normalization yourBetaMatrix <- preprocessFunnorm(yourRGChannelSet) %>% getBeta() 152 5.2.1.1 Reference-based ISCH prediction As studies in human populations are typically performed in more readily accessible tissues, various types of blood preparations (e.g., cord blood, whole blood, dried blood spots, buffy coat, or peripheral blood mononuclear cells [PBMCs]), buccal swabs, and saliva samples tend to be the tissues of choice for such DNAme studies. DNAme reference profiles of sorted cells have been created for these commonly investigated tissues, as well as other biologically relevant tissues like the brain (Table 5.1). Reference-based prediction methods can be applied to these tissues, while reference-free methods are commonly adopted for tissues lacking reference profiles. Table 5.1 Available Reference-Based ISCH Prediction Algorithms and Reference Datasets with Corresponding Packages, Compatible Tissues, and Required Input Types Tissue Package/Source Function Input type Methods available, reference dataset available Blood, cord blood, brain minfi489,501,541 estimateCellCounts RGChannelSet Blood, cord blood ewastools542 estimateLC Beta matrix (preprocessed) Blood, cord blood, brain FlowSorted.Blood.EPIC462,489,490 estimateCellCounts2 RGChannelSet Blood, buccal swab, saliva, breast, cervix, lung (Note*) EpiDISH543 hepidish Beta matrix (preprocessed) Blood methylCC544 estimatecc RGChannelSet or BSseq Brain HiBED545 HiBED_deconvolution Beta matrix (preprocessed) 153 Brain scMD546 scMD Beta matrix (preprocessed) Brain cets547 cetsBrain Beta matrix (preprocessed) Placenta planet548 plCellCpGsFirst Beta matrix (preprocessed) Blood, cord blood, brain, liver, muscle, and others CimpleG549 CimpleG Beta matrix (preprocessed) Tissues with matched expression data scDeconv550 refDeconv Beta matrix (preprocessed) Cell-free DNA (to deconvolute tissues of origin with reference from 29 major human tissues) cfSort551 (Note***) Sequencing data (bismark-aligned BAM file) Cell-free DNA (to deconvolute tissues of origin with reference from 10 tissues including blood, brain, heart, and others) CelFiE552 (Note***) Processed sequencing data Cell-free DNAme from cancer and healthy individuals MethylBERT553 (Note***) N/A Solid tumors across 33 cancer types (Note*) MethylResolver554 MethylResolver Beta matrix (preprocessed) 20 solid tumor types and immune cells HiTIMED555 HiTIMED_deconvolution Beta matrix (preprocessed) 154 Tumor-infiltrating lymphocytes methylCibersort556 CIBERSORT Beta matrix (preprocessed) Tumor-infiltrating lymphocytes EMeth557 cv.emeth Beta matrix (preprocessed) Immune cells, and other unknown cell types are estimated with reference-free method PRMeth558 prmeth Beta matrix (preprocessed) Only reference dataset available (Note**) Saliva BeadSorted.Saliva.EPIC559 13 tissues, including brain, lung, heart, liver, and others EpiSCORE560 Breast tissue, breast milk GSE74877 GSE67024, and GSE110554561 More than 17 tissues, including blood, brain, muscle, heart, digestive system, and others ENCODE project189 Compilation of more than 23 tissues of origin from public datasets and adipocytes, hepatocytes, alveolar lung cells and others Moss et al.190 Only methods available EnsDeconv562 EnsDeconv Beta matrix (preprocessed) 155 Reference-based methods are recommended for tissues where a reference DNAme dataset of sorted cell populations is available, because the deconvolution process is based on biologically measured DNAme profiles instead of statistically inferred components, and they tend to yield more accurate estimates than reference-free methods563â565. An exception is when the epigenetic profile of the reference dataset and/or the set cell types available in the reference can be reasonably assumed to differ markedly from that of the population sampled. For example, the cord blood reference dataset was created because the presence of nucleated red blood cells and other distinct immune cell DNAme signatures in the fetal and neonatal circulation rendered the adult reference dataset inadequate for studying the differences in DNAme associated with systemic exposures at early developmental stages489. User discretion is therefore strongly advised in the selection of an appropriate reference dataset. The steps for reference-based cell type prediction are outlined in Figure 5.2. Figure 5.2 Flowchart of reference-based cell type (CT) proportion prediction for estimating ISCH in heterogeneous tissues. The top row shows the general steps, and the bottom row shows an example of the algorithm. The pre-selected probe sets are curated either based on supervised learning optimized on cell type classification (e.g. IDOL 156 probes), or based on biological relevance of the DNAme sites in cell type identity (e.g. DHS probes). The DNAme level is represented with the notation β, and the proportion of a given CT is represented with p. CP, constraint projection; RPC, robust partial correlation; SVR, support vector regression. Example: Estimating cell type proportions in blood The three main reference-based regression methods available for prediction are constraint projection (CP)461, robust partial correlation (RPC)543, and support vector regression (SVR)566. CP, also known as the Houseman method, utilizes quadratic programming to enforce constraints on the coefficients during regression such that the estimated cell type proportions are positive and sum to 1. RPC and SVR impose the same set of constraints in a post hoc manner, i.e. adjusting predicted proportions above 1 and below 0 to 1 and 0, respectively, post-deconvolution359. One of the most commonly used functions for estimating cell type proportion in human blood using a reference is estimateCellCount2 in the FlowSorted.Blood.EPIC package, which uses CP to estimate cell type proportions. Box 3 shows an example for running this method to estimate blood cell type composition in whole blood from reference data. Box 3. Pseudocode for estimating blood cell type proportions library(FlowSorted.Blood.EPIC) # Bioconductor package library(ExperimentHub) # Bioconductor package hub <- ExperimentHub() FlowSorted.Blood.EPIC <- hub"EH1136" # Loading reference dataset ecc_output <- estimateCellCounts2(yourRGChannelSet, processMethod = "preprocessNoob", # Note* probeSelect = "auto", # Note** cellTypes = c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Neu"), referencePlatform = "IlluminaHumanMethylationEPIC", referenceset = "FlowSorted.Blood.EPIC", # Specify the reference dataset. IDOLOptimizedCpGs = NULL, # Note*** returnAll = T) blood_ct <- ecc_output$prop # Output the estimated proportions 157 Note*: Normalization of reference and sample datasets minimize probe type bias and batch effects. The estimateCellCounts2 function vignette in the FlowSorted.Blood.EPIC R package mentions two normalization methods: preprocessNoob normalization, which corrects for background and dye bias, is recommended for cord blood; and preprocessQuantile, which adjusts the distribution of methylated and unmethylated intensity levels, is recommended for blood and brain458. Refer to the minfi package vignette for other preprocessing methods supported by the function (https://bioconductor.org/packages/devel/bioc/vignettes/minfi/inst/doc/minfi.html). Note**: The probes that are most differentially methylated for each given cell type (e.g. CD4T versus not CD4T) in the reference data are selected by largest magnitude of t test statistic. For estimateCellCounts2, the default is 100 for each cell type. Note***: As alternatives to automatic probe selection, as described in the previous note, probe sets optimized for cell type prediction have been curated by multiple research groups through literature searches or machine learning, such as the DHS (DNase hypersensitive sites)359 and IDOL (Identifying Optimal Libraries) probes254. We have found that some probes in the curated probe sets are occasionally removed in the quality control steps, and the general applicability of these probe sets has not been demonstrated by independent studies. Therefore, we recommend automatic probe selection for the prediction step. To use the IDOL probes, the probeSelect argument should be changed to âIDOLâ and the probe set should be specified in the IDOLOptimizedCpGs argument. The estimateCellCounts2 function only performs CP-based regression. To use RPC or SVR, the coefficient table (i.e. mean DNAme levels for predictive CpGs per cell type) should be exported and the prediction step can be performed with EpiDISH (Box 4-5). Box 4. Pseudocode for obtaining coefficient table of mean cell type-specific DNAme levels from estimateCellCounts2 blood_coef <- as.matrix(ecc_output$compTable) blood_ct <- ecc_output$prop 158 Example: Estimating cell type proportion in buccal swabs or saliva Box 5. Pseudocode for estimating buccal swabs or saliva cell type proportions The EpiDISH R package is commonly used to estimate cell type proportions in buccal swabs or saliva samples, because of the availability of epithelial and fibroblast references513. The hepidish function in this package specifically utilizes a hierarchical structure to facilitate predictions with increasingly finer levels of resolution. For example, it first estimates fibroblasts, epithelial and immune cell proportions in buccal swab samples, followed by deconvolution of the proportion of each immune cell subtype. While EpiDISH does not support normalization or probe selection, it allows specification of the regression method, i.e., CP, RPC, or CBS (Cibsersort / SVR). In our experience, due to the post hoc nature of RPC and SVR, these methods generate more predictions at the boundary conditions (i.e., more 0s and 1s). In tissues composed of mostly a single cell type, such as buccal swabs, this can translate to more accurate predictions. Example: Estimating cell type proportion in the brain The novel brain reference dataset and ISCH prediction package HiBED (Hierarchical Brain Extended Deconvolution) predicts brain cell populations with a hierarchical structure, first grouping them into endothelial and stromal, glial, and neuronal proportions, followed by finer predictions of 7 major cell types (i.e., GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells) 567. The package selects probes based on empirical Bayes moderated t statistics and performs CP-based regression. In the example code below, we have included a beta-mixture quantile library(EpiDISH) # Bioconductor package data(centEpiFibIC.m) # Reference for fibroblasts, epithelial cells, and immune cells. data(centBloodSub.m) # Reference for immune cell subtypes. buccal_ct <- hepidish(beta.m = yourBetaMatrix, ref1.m = centEpiFibIC.m, ref2.m = centBloodSub.m, h.CT.idx = 3, # Specify the column in ref1.m to which ref2.m applies. method = "RPC") 159 (BMIQ) normalization step, as implemented in the original publication. Alternatively, one can opt to skip or perform other normalization methods instead of the one in Box 6. Box 6. Pseudocode for estimating brain cell type proportions Multi-omics ISCH predictors Recently, some reference-based ISCH algorithms have implemented integration of data beyond DNAme to improve prediction accuracy or the range of estimated cell types. EpiSCORE (Epigenetic cell type deconvolution using Single Cell Omic REferences), for example, utilizes single-cell RNA sequencing (scRNA-seq) information for tissues without appropriate DNAme references.568 EpiSCORE identifies tissue-specific transcriptomic marker based on scRNA-seq atlas, followed by imputation of corresponding DNAme signatures using matched expression and DNAme datasets, which can then be applied in ISCH estimation. A detailed protocol has been developed, which provides step-by-step instructions regarding the process569. Similarly, the scDeconv package enables users to predict ISCH with scRNA-seq or RNA data corresponding to DNAme data without an appropriate reference550. The algorithm requires a matched RNAseq and DNAme dataset in the tissue of interest, where the estimated cell type proportion from the matched RNAseq data is used to train the DNAme-library(HiBED) # install with devtools::install_github("SalasLab/HiBED") library(minfi) # Bioconductor package library(wateRmelon) # Bioconductor package # Normalization brain_Mset <- preprocessNoob(yourRGChannelSet) # noob normalization for color correction brain_betas <- BMIQ(brain_Mset) # alternatively, do getBeta(brain_Mset) to extract beta matrix without BMIQ # Brain ISCH prediction HiBED_result <- HiBED_deconvolution(brain_betas, h = 2) # 2 layers of deconvolution yields 7 brain cell types 160 based cell type predictor. A detailed guide for scDeconv can be found on its github page (https://github.com/yuabrahamliu/scDeconv) 570. Note*: Although the MethylResolver package states that it estimates ISCH in 33 solid tumors, the package estimates the relative proportion of tumor and immune cells554. Similarly, the EpiDISH reference dataset includes only immune cells, epithelial cells, fibroblasts, and adipocytes. Although these are the major cell types in tissues such as the lung, there are also minor populations of unaccounted for cell types, including muscle cells, nerve cells, and other supporting cells543. Note**: A few of the reference datasets listed do not have corresponding functions for ISCH estimation. The cell type-specific probes can be selected with the IDOL or t test methods, followed by regression using, for example, the EpiDISH package. Some of these comprehensive DNAme tissue datasets were specifically utilized in studies of cell-free DNA to deduce the tissue of origin190,551,571. Note***: cfSort551, CelFiE552, and MethylBERT553 were developed in Python for next generation sequencing-based data such as whole genome bisulfite sequencing or reduced representation bisulfite sequencing. We included them here as they utilized novel machine-learning based algorithms for cell type deconvolution. 5.2.1.2 Reference-free ISCH prediction Reference-free prediction provides a solution to address ISCH if no applicable reference DNAme dataset is available for the tissue of choice. In general, reference-free prediction assumes that cell types produce a strong and consistent signal in DNAme across samples, which the algorithm is designed to estimate565. The reference-free methods have yet to be reviewed systematically. However, studies comparing a selection of reference-free methodsâsurrogate variable analysis (SVA) 377, FaST-LMM-EWASher572, ReFACTor573, RefFreeEWAS574, and RefFreeCellMix575âshowed that SVA consistently yielded high sensitivity and specificity across study designs564,576,577. 161 SVA requires a linear model that specifies the variables of interest and calculates the residuals of this linear regression model. SVA then estimates the major sources of variation through singular value decomposition (SVD). These sources of variation are designated as âsurrogate variablesâ (SVs), and their corresponding DNAme profiles are then calculated. Each estimated SV does not necessarily correspond to a single cell type, as highly correlated cell type populations, or any other major source of biological or technical variation, can be decomposed into a single SV. Box 7. Pseudocode for reference-free ISCH prediction with sva Note: The sva function does not allow missing values, so imputation is essential and can be done in a few different ways578â580. We show one example in the sample code with the impute.knn function from the impute package, which estimates the missing values with k nearest neighbor algorithm. We recommend starting with SVA for reference-free ISCH prediction. By examining the SVâs association with known variables, the SVs can be interpreted. Assume, for example, that we have a sample from a tissue that is expected to have 5 major cell types, and SVA identified 8 SVs. One can start off by generating a correlation matrix to examine the relationship with available meta data variables that may contribute to DNAme variability, including sex, age, genetic ancestry, and batch variables. If, in the example provided in Box 7, 5 SVs are correlated with these major contributors and 3 are not, one can imagine the variability of the 5 cell types to be captured in 3 SVs. On the other hand, if none of the SVs is correlated with any of the major contributor of DNAme variability, then observing 8 SVs when there are only 5 cell types suggests that SVA may be capturing variability attributable library(sva) # Bioconductor package library(impute) # Bioconductor package yourBetaMatrix <- impute.knn(yourBetaMatrix)$data # Note mod <- model.matrix(~as.factor(mainVar), data = yourPhenoData) # For mainVar, enter the column name in yourPhenoData. mod0 <- model.matrix(~1, data = yourPhenoData) yourBetaMatrix <- yourBetaMatrix[, rownames(mod)] # The model.matrix function removes samples with missing values, so ensure that the same samples are included in both data frames. n.sv <- num.sv(yourBetaMatrix, mod, method = "leek") # Estimate the number of SVs svaobj <- sva(yourBetaMatrix, mod, mod0, n.sv = n.sv) sv <- svaobj$sv 162 to unmeasured factors, in addition to ISCH arising from the expected major cell types. In these cases, users are encouraged to apply a few alternative algorithms and compare their prediction outputs. In addition to the number of predicted fractions, another way to roughly assess the prediction performance is by comparing the average adjusted R2 of regressing DNAme against each reference-free prediction method. Higher adjusted R2 indicates the model explains a higher proportion of the variability in DNAme, adjusted for the relative number of predictors included. If the number of major cell types in the tissue is not known, algorithms such as RefFreeCellMix and MeDeCom that optimize the number of potential cell types may be appropriate. A list of other currently available reference-free ISCH estimation algorithms is presented in Table 2. Box 8. Pseudocode for comparing mean variance explained, as measured by R2, by different ISCH estimation methods Table 5.2 Reference-Free ISCH Prediction Algorithms Algorithm Description SVA377 Estimates major sources of variation (i.e., surrogate variables) through singular value decomposition (SVD) RefFreeEWAS574 RefFreeEWAS adapts SVA to estimate ISCH specifically. It decomposes the total variance of the DNAme in a linear additive model to represent the cell mixture conditions. (Note*) library(dplyr) # CRAN package summaryR2 <- apply(yourBetaMatrix, 1, function(cpg){ mod1 <- lm(formula = cpg ~ ct1 + ct2 + ct3â¦, data = ISCH1) # predicted ISCH from method 1 mod2 <- lm(formula = cpg ~ ct1 + ct2 + ct3â¦, data = ISCH2) # predicted ISCH from method 2 return(c(mod1_R2 = summary(mod1)$adj.r.squared, mod2_R2 = summary(mod2)$adj.r.squared)) }) %>% t() %>% as.data.frame() mod1_R2_avg <- mean(summaryR2$mod1_R2) mod2_R2_avg <- mean(summaryR2$mod2_R2) 163 FaST-LMM-EWASher572 FaST-LMM-EWASher combines intersample correlation and principal component analysis (PCA) to approximate ISCH. (Note**) ReFACTor573 ReFACTor uses sparse PCA to select for a subset of cell type-associated differentially methylated probes. RefFreeCellMix575 RefFreeCellMix utilizes non-negative matrix factorization (NMF) on a beta matrix to iteratively find the number of cell types and estimate their DNAme profiles, called âlatent DNAme componentsâ (LMC). MeDeCom581 MeDeCom also applies NMF, but regularizes the LMCs so that they are more likely to have beta values closer to 0 or 1, as would be expected in a purified cell population. (Note***) CONFINED582 CONFINED attempts to differentiate biological from technical variance by applying canonical correlation analysis (CCA) across multiple datasets. CONFINED is not applicable to single-cohort studies, but may be effective in multi-cohort analysis. EDec583 The EDec package combines both reference-based CP and reference-free NMF. BayesCCE584 BayesCCE expands on ReFACTor to predict relative counts of cell types using a Bayes prior of known cell count ratio in the tissue of interest. ARIC585 ARIC aims to improve prediction accuracy for rare cell populations. In the feature selection step, ARIC removes features that lead to high collinearity across cell types. It also uses 164 weighted SVR to minimize the relative error of predicted components. Tsisal586 Tsisal transforms the DNAme data into a simplexâa linear programming algorithm with a geometric form. The user must specify the number of cell types present in the sample. Tsisal is also partially reference-based, as the user can input the reference DNAme of some cell types and allow Tsisal to estimate the proportions of cell types with and without references. debCAM587 The debCAM package uses Convex Analysis of Mixtures (CAM), which is also based on simplex identification. In addition to the reference-free ISCH estimation methods listed in Table 2, previous studies have applied PCA directly to DNAme data and used the first few PCs for ISCH estimation588. However, ISCH may not be captured in the first few PCs, such as in disease conditions, or the first few PCs may also include variance associated with the variables of interest. Similar to SVA output, evaluation of the PC and the variance explained prior to taking this approach is recommended. Note*: The RefFreeEWAS package has been removed from the CRAN R package repository, so we are unable to run either the RefFreeEWAS or RefFreeCellMix functions directly. Instead, alternative implementations of these two functions are available in the TOAST package, with a novel feature selection step that iteratively identifies the cell type-associated differential DNAme signature589. Please refer to the TOAST package vignette (https://github.com/ziyili20/TOAST) for detailed usage instructions. Note**: A guide to implementing FaST-LMM-EWASher was presented previously590. Note***: The MeDeCom user guide describes ways to speed up the prediction step and provides useful tools, such as comparison of the estimated cell type to reference profiles. 165 The authors have also provided a helpful preprocessing pipeline and downstream analysis visualization steps that are compatible with MeDeCom, RefFreeCellMix, and EDec591. 5.2.2 Accounting for intersample differences in cellular composition Once ISCH has been measured directly or estimated bioinformatically, the associations between it and the variables of interest can be tested. Depending on whether the ISCH measure is compositional or not, the methods for adjustment differ. It has been proposed that the compositional and interdependent nature of proportional data can lead to multicollinearity and violate linear regression assumptions592. We wish to clarify this conundrum here. Multicollinearity describes the linear dependency of predictor variables in a regression model, and it inflates standard error calculation and leads to unstable estimates593. The extent to which multicollinearity is plaguing each independent variable can be assessed with the variance inflation factor (VIF). VIF above 10 generally indicates high level of multicollinearity and unreliable estimate for the specific variable593,594. However, high VIF of some covariates in the model is not necessarily problematic, as long as the coefficients of covariates with high VIF are not interpreted. For example, even if the ISCH variablesâ VIFs are high, the coefficient estimates for the variables of interest are unaffected as long as their VIFs remain low (i.e. as long as the variables of interest are not highly correlated with ISCH variables). It is possible for the main variable of interest to be linked directly to changes in cellular composition363,364. In such cases, elevated VIF can pose a problem to interpretability of the regression results, as it can be impossible to determine whether DNAme differences may be attributed to the variable of interest or ISCH. Thus, we recommend researchers to thoroughly explore the relationships between all covariates prior to multivariate analyses. Including the estimated proportions of all cell types in EWAS models can sometimes lead to perfect multicollinearity (i.e. the proportion of one cell type can be explained by all the sum of all other cell types), where the effect of one cell type cannot be estimated.594 We therefore recommend removing one cell type intentionally to bypass the issue. The users 166 can, for example, remove the cell type with the lowest intersample variability, or alternatively, the one with the lowest likelihood of biological plausibility. After deciding which ISCH variables to account for, the procedure can be implemented in two ways: if applicable, ISCH can be accounted for as covariates in statistical tests, or by calculating ISCH-residualized DNAme profile and using the residualized value in the statistical tests (Figure 5.1). For example, ISCH can be accounted for as covariates EWAS models. Whereas in cases like a permutation test, where ISCH cannot be accounted for as covariates in the statistical test, the latter approach offers a viable method to still adjust for ISCH-associated variability. 5.2.2.1 Use robust linear regression and include ISCH information as covariates The ISCH variables can be accounted for in EWAS models, either directly or converted into principal components (PCs) of ISCH variables (ISCH PCs). Mathematically, they would generate the same model if all the PCs are included, and lead to the same coefficient and p value estimates for the variables of interest. The advantage of direct incorporation (e.g. reference-estimated monocyte proportion) is ease of interpretation. The association between DNAme and specific cell types can be investigated with this approach, but again, due to multicollinearity, we caution the readers on over-interpreting cell type-associated differential DNAme. On the other hand, the advantage of including a subset of the top ISCH PCs is the ability to capture most of the ISCH variability while having fewer variables in the model, thereby increasing the detection power of the model and prevent overfitting595. Additionally, PCA removes multicollinearity amongst the ISCH variables only and allows the users to explain a general ISCH-associated DNAme signal (e.g. general blood cell type associated DNAme signature), under the condition that the variables of interested are not highly correlated with the ISCH PCs. We recommend including all the ISCH PCs as 167 covariates, or at least sufficient PCs to account for 90% of the variance. Box 9 shows an example of PCA on the ISCH estimates. Box 9. Pseudocode for principal component calculations of ISCH variables Either the ISCH estimates or the ISCH PCs can then be accounted for in EWAS models as covariates. In Box 10, we give an example of robust linear regression using the rlm function from the MASS package, which dampens the influence of outlier observations596. Box 10. Pseudocode for accounting for ISCH variables in EWAS with robust regression 5.2.2.2 Adjusting for ISCH by residualization If ISCH cannot be included as a covariate in model fitting, perhaps because the user is not interested in fitting a traditional EWAS, an alternative approach to accounting for cell type proportions is to âresidualizeâ ISCH-associated variability (i.e. fit a linear model that includes only the ISCH variables, and then proceed to use the residuals of this model in downstream analyses). Similar to previous discussions, cell type proportions can be included directly or transformed with PCA before being included in linear models. We library(MASS) # CRAN package library(sfsmisc) # CRAN package library(dplyr) # CRAN package pd <- join(yourPhenoData, yourestimatedISCH, by = sampleID) # or yourISCHpcs ewas_result <- apply(yourBetaMatrix, 1, function(y) { mod <- rlm(formula = y ~ mainVar + celltypesvariables, data = pd) # Include all ISCH variables and all other relevant covariates here. out <- f.robftest(mod, var = 2) # The var = 2 argument refers to the mainVar. return(c(mod$coefficients[2], out$p.value)) }) %>% t() colnames(ewas_result) <- c("coefficient", "pvalue") pca <- princomp(yourEstimatedISCH) # replace with your data pca_var <- cumsum(pca$sdev^2 / sum(pca$sdev^2))# cumulative variance explained by PCs names(pca_var)[pca_var > 0.9][1] # the PC at which cumulative variance exceeds 90% yourISCHpcs <- as.data.frame(pca$scores) # the ISCH PCs 168 recommend only adjusting for ISCH when the cell type proportions or PCs cannot be included as covariates (described in section 5.2.2.1), as they can be partially correlated with variables of interest (e.g., age and disease status). In these cases, regressing the cell type PCs out may lead to model overfitting, thereby removing more variation attributable to ISCH alone377,597. Box 11. Pseudocode for residualizing ISCH variability on DNAme levels 5.2.2.3 Accounting for non-compositional estimates In cases where the estimated cell type effect is not in compositional form but in counts or as continuous variables (such as SVA output), the variables can be incorporated as covariates in linear regression-based EWAS models without PCA. Again, we do not suggest regressing out the variance associated with estimated cell type effect before EWAS, as it can remove more variation attributable to ISCH alone. It can also lead to underestimation of standard errors of coefficients (anti-conservative) as it treats the adjusted levels as observed, without accounting for the variability in the estimated ISCH effects. 5.2.3 Investigating cell type-specific associations of variables of interest and DNA methylation library(dplyr) # CRAN package residuals <- apply(yourBetaMatrix, 1, function(x){ x <- as.numeric(x) mod <- lm(x ~ celltypes, data = yourEstimatedISCH) # or replace with yourISCHpcs return(residuals(summary(mod))) # The residual represents the variance not associated with ISCH }) %>% t() adj.residuals <- residuals + matrix(apply(yourBetaMatrix, 1, mean), nrow = nrow(residuals), ncol = ncol(residuals)) # Add the residuals back to the mean DNAme level of each methylation site to obtain an adjusted beta matrix. This is optional, with the intention of creating an âimitation beta matrixâ with the same scale as the typical DNAme data distribution (i.e. [0, 1]). adj.residuals[adj.residuals <= 0] <- 0.0001 # Adjust the âbeta valuesâ such that they are bounded by 0 and 1 adj.residuals[adj.residuals > 1] <- 0.9999 169 While accounting for ISCH addresses the problem of cellular composition confounding with the variables of interest, the cell type-specific DMs (csDM) can still be masked. That is, the DNAme profile of each cell type can show different changes upon exposure to stimuli368, and it can be challenging to determine which cell types are driving the association detected at the bulk level. For example, epithelial cells in buccal swabs exhibit csDM with smoking behavior598. The csDM signal may not be identified when immune cells exist in high abundance in the original sample preparation. Three methods have been developed to address this issue by deconvoluting the cell type-specific DNAme associations with variables of interest: Cell type-specific Differential Methylation Calling (CellDMC), Cell-type-specific Differential Analysis using Regression (CeDAR), and Tensor Composition Analysis (TCA). CellDMC fits a model with an interaction term between the covariates and estimated cell type proportion598. Through the statistical significance of this term, the CellDMC algorithm ranks the sites that are differentially methylated across cell types. Box 12. Pseudocode for identifying cell type-specific associations with CellDMC Another novel method, CeDAR, also implements an interaction model to determine csDM. However, it uniquely adopts a Bayesian hierarchical model based on the correlation structure of some cell types599. The model assumes that differential DNAme patterns are likely to be correlated between closely related cell types. After estimating csDM for each cell type separately, the marginal likelihood of the differential methylation state is estimated based on the prior, which is the cell type hierarchy. The CeDAR algorithm by default estimates the cell type hierarchy based on the correlation structure of top differentially methylated DNAme sites across cell types. library(EpiDISH) # Bioconductor package mod <- model.matrix(~covariates, data = yourphenodata) # Create a model matrix of relevant covariates that are not the estimated ISCH or the main variable of interest. dmc_output <- CellDMC(yourBetaMatrix, mainVar, yourestimatedISCH, cov.mod = mod) dmc_csDM <- dmc_output$coe 170 Box 13. Pseudocode for identifying cell type-specific associations with CeDAR TCA uses matrix factorization and assumes the csDM signal of a particular variable of interest is shared across samples. The significance of an csDM effect size for variables of interest are then tested with generalized likelihood ratio test. While it has yet to be independently confirmed, the original publication suggested that TCA might have higher detection power, especially with increasing number of unique cell types and when csDM is present in multiple cell types368. Box 14. Pseudocode for identifying cell type-specific associations with TCA While the CellDMC algorithm runs the fastest and the model has a straightforward interpretation, the interaction component of the model, reduces its power to detect true positives when multiple cell types are present. CeDAR uses the same interaction model, but improves the confidence of its prediction with the hierarchical model assumption. The tradeoff is a longer runtime, depending on the sample size and the model complexity. Similarly, TCA requires more computational power, but may yield better performance under conditions with 3 or more cell types368. Using a test dataset of 3000 CpGs, 100 samples, 6 cell types, and 3 other covariates, we clocked CellDMC at 6.4 seconds, CeDAR at 15.7 seconds, and TCA at 30.1 seconds. The run time scales linearly with the number of library(TOAST) # Bioconductor package cedar_output <- cedar(Y_raw = yourBetaMatrix, prop = yourestimatedISCH, design.1 = yourphenodata, factor.to.test = 'mainVarName', cutoff.tree = c('pval',0.01), cutoff.prior.prob = c('pval',0.01)) cedar_csDM <- cedar_output$toast_res library(TCA) # CRAN package tca_output <- tca(X = yourBetaMatrix, W = yourestimatedISCH, C1 = covariatesassociatedwithISCH, C2 = covariatesnotassociatedwithISCH) tca_csDM_pvals <- tca_output$gammas_hat_pvals 171 DNAme sites tested. The user may decide on which method to use depending on the cohort characteristics and the number of cell types involved. 5.3 Conclusion In this primer, we presented a practical guide to estimate and account for ISCH in DNAme studies, with references to additional resources and authorsâ recommendations for best practices. Given the contribution of ISCH on DNAme variability in general, examination of measured or predicted ISCH variables can inform downstream analysis, and accounting for it in a principled manner will lead to more accurate and interpretable findings. There is no single optimal procedure for studying ISCH in DNAme research. Users must decide on the appropriate pipeline based on factors such as characteristics of the cohort in question, sample size, availability of cell count information, whether the variables of interest are associated with ISCH, and the intended analysis. As new methods leveraging ISCH information are continually being developed, future research can borrow ideas from other omics methodologies to expand the analytical repertoire. For example, deconvolution of cell type-specific signature was first performed in gene expression studies600 and now, the development of novel and robust methods for csDM signal detection can complement findings of single-cell DNAme research. Similarly, trajectory-based analysis, such as pseudotime, has been commonly used in transcriptomic studies to explore the transition of cell populations over time in longitudinal or interventional cohorts601â604. This can be applied in DNAme-based studies to examine immune system development, cellular response to external stimuli such as vaccines or smoking, or tracking tumor progression over time. These analytical tools with constantly advancing machine learning methodologies, enhance our ability to understand how DNAme responds to the environment at a cellular level. 172 6 Chapter 6: Conclusion and Future Directions 6.1 Summary of findings The overarching goal of this thesis was to evaluate and generate methods for delineating variability associated with biological variables, such as cellular composition, age, sex, and disease status in DNAme data. This was achieved through either accurately estimating the effect of given variables, as in the case of cellular deconvolution in pediatric DNAme samples, or establishing a comparable demographic background for signal detection, as in the case of detecting rare disease signals with population-specific DNAme reference sets. In Chapter 2, we described a case of a 16-year-old patient with ASXL1 deficiency, characterized by combined immunodeficiency and EBV-associated lymphoma. Detailed immunophenotyping revealed progressive loss of B cells with hypogammaglobinemia. Cytotoxic cell impairments included dysfunctional and senescent CD8 T cells, with defects in NKT and mature NK cells. In addition to the immune skewing, the patient's T cells exhibited a marked loss of DNA methylation with increased epigenetic aging. Similarly, I observed differential DNAme and gene expression in NCAM1 linked to altered ASXL1 functions, corroborating with the loss of NKT cells in the patient. The aberrations in the methylome and the transcriptome were partially ameliorated by lentivirus-mediated transduction with WT ASXL1, confirming the pathogenicity of ASXL1 variants. This study defines a human IEI caused by ASXL1 deficiency, a diagnosis that should be considered in individuals with chronic viral infections, virus-associated hematologic malignancies, and combined immunodeficiency. Furthermore, our findings provide fresh insights into the epigenetic mechanisms underlying the roles of human ASXL1 in T cell function and in the development and maintenance of lymphomas. In Chapter 3, I outlined a method for the creation of population-specific DNAme reference datasets that untangle demographic influences (age, sex) from disease-related signals. I compiled control samples with no known medical conditions from GEO to evaluate the typical methylation level at each DNAme loci. Three statistical methods, Grubbsâ outlier 173 detection test, permutation test, and one-sample t-test, were assessed for their performance in detecting disease-associated signatures by comparing the control DNAme against that from the ASXL1 patient sample (Chapter 2). One-sample t-test and Grubbsâ test demonstrated high overlap in differentially methylated loci, and all three methods were sensitive to global changes in DNAme patterns beyond the typical variability observed in the HC populations. In Chapter 4, I critically evaluated the reference-based deconvolution procedure and optimized its prediction accuracy for longitudinal birth cohorts using DNAme data from the CHILD cohort. The optimized algorithm, CellsPickMe, integrates cord and adult references and picks DNAme features for each population of cells with machine learning algorithms. It demonstrated improved deconvolution accuracy in cord, pediatric, and adult blood samples compared to existing benchmark methods. CellsPickMe supports blood cell deconvolution across early developmental periods under a single framework, enabling cross-time-point integration of longitudinal DNAme studies. Given the increased resolution of cell populations predicted by CellsPickMe, this R package empowers researchers to explore immune system dynamics using DNAme data in population studies across the life course. In Chapter 5, I compiled current literature on the best practices for estimating and accounting for ISCH, to guide researchers through the process in DNA methylation studies. This chapter outlines the procedure of bioinformatic ISCH prediction, including using reference-based and reference-free algorithms. It then follows with descriptions of several methods to account for ISCH in downstream analyses, including robust linear regression and principal component analysis-based adjustments. Finally, I outlined three methods for estimating differential DNAme signals in a cell type-specific manner. Throughout the chapter, statistical and biological justifications were provided for the recommendations, along with R code examples for ease of implementation. 174 6.2 Considerations for epigenetic studies: limitations and future directions 6.2.1 Understanding the mechanisms driving RDEOs 6.2.1.1 Interpretability of EWAS findings In Chapters 2 and 3, EWAS identified tens of thousands of DMs in blood and PBMC, and hundreds of thousands of DMs in T cells when comparing between the patient samples against the HCs. From this, there were two main takeaways. One, the DMs were highly enriched in Polycomb-repressed genomic regions, suggesting the DNAme dysregulation can be at least partially attributed to ASXL1âs role in H2AK119 deubiquitination as a part of PR-DUB. Two, the global loss of DNAme in T cells was reminiscent of the epigenetic landscape of senescent or malignant cells, and we established ASXL1 as a driver of this DNAme aberration. Both findings were interesting but broad. From the numerous DMs, it was difficult to isolate key differentially regulated genes that might mediate the patientâs susceptibility to viral infections. This is one major caveat of EWAS. It is useful as a tool to highlight unexpected associations and guide researchers in forming more informed experimental designs, but not sufficient to clarify mechanisms of action. 6.2.1.2 Mechanisms: why and how Understanding the mechanism is not always necessary for a study. However, for studying RDEOs, gaining insights into the underlying molecular pathways is particularly important. If we know how the patientâs ASXL1 variants drive DNAme dysregulation, there is the potential to prevent it, treat it, or reverse it. There was one instance where our findings partially contributed to medical decision making. One concern the physicians had when the ASXL1 patient underwent treatment for their lymphoma was the potential of impaired DNA damage repair. As many cases of rubella granuloma have been linked to defects in this pathway, which would influence the physiciansâ decisions on whether to pursue radiotherapy, it was informative to see that none of the top DMs were associated with DNA repair and that this pathway was not enriched. Further evidence from DNA repair assay measuring pSMC1 and gammaH2A.X demonstrated the patient exhibited typical repair response in T and NK cells, encouraging the deployment of radiotherapy (data not shown). 175 Increasing interpretability of EWAS findings can potentially illuminate the relationship between altered epigenetic landscape and downstream molecular implications. The method for investigating RDEO mechanisms should be context dependent. In the case of the ASXL1 patient, one outstanding query remains: how does ASXL1 instruct DNAme landscape? It is reasonable to hypothesize that the relationship is mediated by H2AK119ub. Despite no significant difference being observed between the patient and HC bulk level H2AK119ub, it is possible that the deposition pattern is altered. Complementing our findings, I would hypothesize that the H2AK119ub distribution at PRC-regulated regions are impacted by the patient variants. Implementing genome-wide histone profiling with techniques such as mass spectrometry, chromatin immunoprecipitation followed by sequencing (ChIP-seq) or many other alternatives would facilitate the understanding of the genesis of patient phenotypes605,151,606. Another interesting avenue to explore is the link between altered epigenetic landscape and immune cell dysfunctions. EWAS findings can inform future research into potential immune pathway impairment beyond IL2-STAT5 signaling. Specifically, it would be interesting to further investigate the role of ASXL1 on hematopoiesis, as the maintenance of bivalency is particularly vital in early development48,607. Animal models and tissue culture are often used to study RDEOs in a developmental time-point-specific manner. Some of the most significant strides in RDEO research have been made in model organisms608â610,288,22,611,275, but not all of this research can be readily translated to humans. For example, mGluR1 signaling blockers showed therapeutic potential for Fragile X syndrome (FXS) in animal trials, but the effects failed to replicate in clinical trials in adolescents or adults with FXS612. These discrepancies may be due to differences in the trajectories of mouse and human brain development and dynamics of key signaling molecules609. Patient-derived induced pluripotent stem cells (iPSCs) or human ESCs with engineered variants are also valuable for studying RDEOs with cell type specificity. In the case of iPSCs, a cocktail of transcription factors are used to convert somatic cells into pluripotent 176 stem cells, which can then be further differentiated to address the research question with tissue specificity613,614. As iPSCs retain the donor genotype, they have been used widely in studying RDs of genetic origin408. Monolayer iPSCs are more scalable, so they serve as a better platform for genetic and drug screening in a high-throughput manner615,616. 6.2.2 Cell type and tissue specificity 6.2.2.1 Challenges with studying the cell types of relevance As a throughline of the thesis, it is not surprising that the interconnectedness of DNAme and cell type continues to be a major challenge in epigenetic investigations. In Chapter 2, we directly observed both cell-type and tissue-specific DNAme dysregulation due to the patientâs ASXL1 variants. We observed a discrepancy among the epigenetic age predictions based on patient buccal swabs, whole blood, PBMC, and T cells. Specifically, while the patientâs buccal epithelial cells showed comparable aging trajectory to the HCs, their cultured T cells were predicted to have a highly advanced EA (average difference of chronological age was 27.4 years). Additionally, the shorter telomere length was only observed in the lymphocytes. This was mirrored in the EWAS findings. Whereas a skew towards DNAme loss was observed in patient whole blood and T cells compared to the HCs, the reverse was true for PBMCs. The totality of evidence demonstrated that the effect of ASXL1 variants on epigenetic regulation was highly cell-type-specific. These discrepancies observed across the tissues / cells examined are biologically interesting, but limit the generalizability of our findings, and also require researchers to be strategic when selecting the sample of interest, as my findings demonstrated that this decision could have major impact on research outcomes. Theoretically, ASXL1 plays an important role in B-cell function and proliferation, given the patientâs clinical history of hypogammaglobulinemia and B-cell cytopenia prior to the development of EBV-associated B-cell lymphoma. However, the patient's B cell count was marginal prior to the lymphoma diagnosis, to the point that we were unable to immortalize their B cells for further profiling617,618. This experience highlights the challenge in studying 177 epigenetics in relevant cell types in RDEOs, as many of them feature altered immune landscape280,303,312. Despite the persistent low T cell count, we were able to successfully culture the patient and HC T cells for cell-type-specific comparisons. The findings were illuminating. We observed a global loss of DNAme reminiscent of senescence signature. The advanced EA was further ameliorated with the introduction of WT ASXL1. These observations suggest that patient T cellsâ senescent phenotype was driven by the patientâs ASXL1 variants. Along with the IL2-STAT5 dysregulation in expanded CD8 T cells, this alludes to a functional link to an apparent lack of antitumor cytotoxicity. However, the fact that we only observed STAT5 phosphorylation defect in CD8 T cells implied that CD4 and CD8s were differentially affected in their epigenetics. The findings call into question whether a finer sorting of cell populations was necessary, and if so, what resolution of sorting is optimal. 6.2.2.2 Differences in in vivo and ex vivo experimental findings As the ex vivo culture environment favors the expansion of CD8 T cells and the selective blasting of effector populations407,619, it was surprising that we also observed a preferential proliferation of CD4 cells in the patientâs expanded T cells. The skewing of CD4/CD8 ratio was partially rescued with the transduction of WT ASXL1. This phenomenon suggests that ASXL1 also contributes to the modulation of CD4 and CD8 proliferation, potentially linked to the CD8 defects in IL2-STAT5 signaling. Intriguingly, we did not observe this skewing towards CD8 lineage in the patient PBMCs. Certainly, in vitro cell culture has been shown to induce differential DNAme620â623. However, given that the altered DNAme landscape and CD4-CD8 skewing were rescued by WT ASXL1, the observed effect was not driven by the culturing condition alone. The observed senescence phenotypes of T cells in culture, including the upregulation of CD57, global DNAme loss, and advanced epigenetic age, can potentially illicit targeted immune responses in the human body. This can occur in a similar fashion to the process described in section 1.2.4, where the global DNAme loss enables the transcription of transposable elements, leading to increased immune surveillance and CD8-mediated cell lysis243. It is tempting to hypothesize that the patientâs immune system 178 suppresses the expansion of CD4 in vivo, yielding different T cell profiles as observed in culture. 6.2.2.3 How to study the ârightâ cell populations moving forward? To examine the DNAme changes in the desirable cell populations, two main approaches have been developed. The first is a bioinformatics-based approach. Many groups have constructed algorithms for deconvoluting cell type-specific effects of diseases, as described in section 5.2.3368,598,599. As previously noted, these methods have high computing power requirements and are thus more appropriate in cohorts with larger sample sizes or for dimension-reduced data. In the case of the ASXL1 patient, these methods are, unfortunately, not applicable. The extreme group imbalance (one case and hundreds of controls) produced errors when the algorithms attempted to evaluate csDM effects. Inherently, this case-control imbalance design is prone to false positive findings439. However, estimation of csDMs bioinformatically is viable for other RDEOs with more patient cases. Future studies are needed to examine the sensitivity and specificity of applying these algorithms in RDEO research. In addition to deconvolution, which may suffer from predictor errors or the lack of appropriate reference data sets, many studies have opted to analyze sorted cell samples or to use a single-cell approach. Cell sorting requires the isolation of relevant cell types using techniques such as fluorescence-activated cell sorting (FACS)624,625. Alternatively, examination of the patientâs epigenome at the single-cell level can easily distinguish whether the differential regulation is due to compositional changes, cell-specific dysregulation, or a mixture of both17,345,626â628. One potential caveat of FACS for purified cells or single-cell analysis is that the tissue dissociation and sorting procedures have been shown to alter transcriptomic profiles629. These effects of the sorting process on epigenetic profiles warrant further investigation. However, as I have already described the capacity of ex vivo culturing in influencing DNAme via alteration of T cell composition, it is likely that most experiments interfere with a cellâs DNAme profile in one way or another. Single-cell DNAme profiling remained limited 179 at the moment due to high missingness, high variability, and an absence of standardized preprocessing and analysis pipeline24,630. As technology matures, in RDEO cases like the ASXL1 patient, single-cell resolution would be extremely helpful for identifying DNAme disturbances even in rare cell populations (such as B cells in the patient). It would also address outstanding queries such as whether there are any immune cell populations that are unique to or absent in the index patient, whether the observed DNAme loss was present in all T cell subsets, and which cell types exhibit differential DNAme and likely regulated by ASXL1 activity. In general, single-cell approaches can be used to distinguish the sources of epigenetic variations, facilitating a more in-depth characterization of RDEOs. 6.2.3 Epigenetics as a key marker of cell type in future immunology research In Chapter 4, I observed an interesting neonatal epigenetic signature in pediatric blood samples, and to a lesser extent in the adult blood samples. As discussed in section 4.4, the algorithm might be detecting the signals from cells that are more prominent in neonatal time point but persist in the periphery at later developmental time points, such as LDNs or transitional B cells. The presence of this neonatal signature in other cell types potentially highlights cell populations with altered function in the pediatric time point. The finding suggests that epigenetics is highly sensitive to cell state differences, and may be useful in supplementing current immunology techniques in profiling rare cell populations. However, one major challenge remains, and that is the validation of our observations. While our prediction output showed high alignment with the CBC, the existence of these predicted neonatal cell types and their presence in pediatric samples cannot be truly validated without matching immune profiling of surface markers, as that is the basis of cell-to-cell communication. Two layers of validation are required: 1) evaluation of prediction accuracy with high-resolution cell marker profiling against predicted proportion; and 2) characterization of neonatal cell surface markers in pediatric time points. In Chapter 4, we compared our prediction results to the best available data in CHILD â the CBC â yet that data does not inform lymphocyte subtypes, such as T and B cells, or beyond 180 that, naïve and memory cells. Therefore, I need to first validate our CellsPickMe accuracy in another cohort with high resolution immune cell profiling, and the Kobor Lab is in the process of acquiring access to a pediatric dataset with finer immune cell profiling, the Generation R dataset474,484. In order to address the second point, matched DNAme assay with comprehensive cell marker profiling, with methods like mass cytometry, on samples collected at multiple pediatric time points are necessary. If the observed epigenetic state is validated with immune cell marker changes, then it demonstrates the sensitivity of epigenetics in detecting distinct cell populations or cell states. In joint consideration with the findings in Chapter 2 demonstrating the sensitivity of immune cell profiles to epigenetic disturbance, DNAme has immense potential as a major omics that can supplement immunology to understand immune cell dynamics. While several papers have ambitiously profiled immune cells at high resolution, even with multiomics integration, DNAme is often overlooked477,631. Currently, transcriptomics is the standard omics for characterizing functions of a given cell population. Yet, the transcriptomic state of a cell can be subjected to external stimulations, whereas DNAme is more stable in contrast. As such, it would be exciting to see future research that examines DNAme as a supplement to immune profiling for in-depth cell population characterization. 6.3 Concluding remarks This thesis presents bioinformatic tools and analytical strategies to dissect the complexity of DNA methylation variability in the context of rare epigenetic disorders and immune dysregulation. 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Thesis/Dissertation
eng
Genome Science and Technology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Kobor, Michael S. (Geneticist)
Untangling sources of DNA methylation variability in the context of development and rare diseases
Text