Circadian Hallmarks of the Aging Epigenome

by

Sasha Ebrahimi

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Institute of Medical Sciences University of Toronto

© Copyright by Sasha Ebrahimi 2017

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Circadian Hallmarks of the Aging Epigenome

Sasha Ebrahimi

Doctor of Philosophy

Institute of Medical Sciences University of Toronto

2017

Abstract

Circadian rhythmicity governs a remarkable array of fundamental biological functions in

eukaryotes which is mediated by cyclical metabolomic, transcriptomic, and proteomic activities.

Histone modifications and other epigenetic factors are also involved in the circadian machinery;

however, despite significant efforts detection of circadian DNA (cytosine) modification has

remained elusive. In this study, we aimed to document and explore the features of circadian

oscillations of cytosine modifications. We discovered that a substantial number of cytosines

show a circadian pattern in their modification status in mouse liver and lung, as well as human

neutrophils. Furthermore, we found that circadian oscillations of cytosine modification are linked

to circadian transcription. Our findings also indicate that circadian oscillations explain a portion

of the epigenetic variation which traditionally has been called random or stochastic.

There are numerous clinical and molecular studies showing strong associations between

declining circadian rhythmicity and aging, but the molecular underpinnings of this association

are not clear. Since epigenetic alterations accompany aging, we aimed to investigate the

relationship between daily cytosine modification oscillations and the aging epigenome. We

discovered that cytosines which exhibited circadian epigenetic oscillations significantly

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overlapped with the cytosines exhibiting age-related modification changes. Furthermore, the

amplitude of these oscillations strongly correlated with the degree of age-dependent changes.

Finally, the day or night timing of circadian peaks were accurate predictors of whether a specific

cytosine will lose or gain modification in the aging tissue.

Epigenetic changes, circadian dysregulation, and age have all been implicated in a number of

complex diseases. Consequently, our third aim was to examine the contributions of DNA

modification oscillations to the epigenetic changes identified in epigenome-wide association

studies of complex disease. We tested leukemia, Alzheimer’s disease, and schizophrenia, and in

all cases, we found a significant overlap between cytosines exhibiting daily oscillations and those

identified in disease studies. We therefore propose that circadian epigenetic oscillations may

contribute not only to aging but also to disease. Based on our findings, further studies are

warranted for a more complete understanding of how evolutionary advantageous processes like

circadian rhythmicity can also mediate an organism’s demise.

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Acknowledgments I would like to thank everyone who made it possible for me to pursue my love of science and

complete my doctoral thesis. First and foremost, I would like to thank my supervisor and mentor,

Dr. Art Petronis. Your scientific expertise is only exceeded by your kindness and generosity. I

am grateful to members of my advisory committee, Dr. Rod Bremner, Dr. Andrew Paterson, and

Dr. Dan Durocher, for their continued guidance and support over the years – I am a better

scientist because of you.

To all the members of the Petronis team, past and present, thank you. I would especially like to

thank Dr. Gabriel Oh, my co-conspirator in this project, for his insights and greatly improving

my grasp of statistics. I specifically thank Dr. Aiping Zhang, Akhil Nair, Matthew Carlucci, and

Daniel Groot for their invaluable contributions to this project. I would also like to thank Miki

Susic, Dr. Viviane Labrie, Dr. Peixin Jia, Edward Oh, Richie Jeremian, Andrew Kwan, Dr.

Carolyn Ptak, and Dr. Mrinal Pal for all their support, assistance, and fun memories over the

years. I thank our animal model collaborators, Dr. Martin Ralph and Choden Shrestha, for their

dedication and expertise. I also thank the “Lithuanian gang”, Dr. Juozas Gordevičius and Karolis

Koncevičius for their contributions to this project and for never running out of ideas for us to

test.

I could not have navigated the rigors of graduate school without the incredible support of my

friends and family. I am grateful for their unwavering faith in me over the years. Most

importantly, I am thankful to my mother and role model, Susan. Your sacrifice, resilience, and

unconditional love have made all of this possible. I dedicate this thesis to you.

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Contributions Dr. Art Petronis, Dr. Gabriel Oh and I conceived the theoretical framework and experimental

designs. My contributions to the project were to lead and conduct all of the experimental work

with input and support from all collaborators. Dr. Gabriel Oh led the bioinformatic and statistical

elements of this project with input from others in the team.

Dr. Aiping Zhang assisted with all aspects of the bisulfite and oxidative bisulfite padlock probe

experiments. She was also instrumental in performing the mRNA analysis in chapter 3.1.3 and

3.1.4. Akhil Nair assisted with all aspects of the MRE-chip experiments in chapter 3.3.2, with

input from Dr. Tarang Khare, Dr. Peixin Jia, and Dr. Aiping Zhang.

Miki Susic and Dr. Mrinal Pal were involved in all of the sequencing reactions performed in this

thesis. Dr. Martin Ralph and Choden Shrestha were responsible for entraining and hosting our

mice in their facilities. Dr. Gabriel Oh, Akhil Nair, Choden Shrestha, Dr. Viviane Labrie, Dr.

Tarang Khare, Dr. Peixin Jia, and other current and former students and fellows of Dr. Art

Petronis’s lab assisted with sample collections from the animals. Dr. Peixin Jia was responsible

for neutrophil isolations from peripheral blood.

Matthew Carlucci and Daniel Groot contributed to all of the bioinformatic analyses performed in

this thesis. Dr. Juozas Gordevičius and Karolis Koncevičius assisted with the MRE-chip and

Infinium HumanMethylation450 BeadChip analyses in chapter 3.3.

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Table of Contents Acknowledgments .......................................................................................................................... iv

Contributions ................................................................................................................................... v

Table of Contents ........................................................................................................................... vi

List of Tables .................................................................................................................................. x

List of Figures ................................................................................................................................ xi

List of Appendices ....................................................................................................................... xiv

List of Abbreviations .................................................................................................................... xv

Chapter 1 Introduction .................................................................................................................... 1

1.1 Epigenetics .......................................................................................................................... 2

1.1.1 DNA methylation and demethylation ..................................................................... 3

1.1.2 The structural and functional features of genome-wide DNA modification .......... 7

1.1.3 The relationship between modifications of DNA and histones ............................ 13

1.1.4 Development, differentiation and cellular identity ............................................... 18

1.1.5 Epigenetic syndromes ........................................................................................... 22

1.1.6 Epigenetic factors in human complex disease ...................................................... 24

1.1.7 The temporality and malleability of DNA modifications ..................................... 29

1.2 Circadian Rhythmicity ...................................................................................................... 32

1.2.1 The molecular circuitry of the circadian clock ..................................................... 34

1.2.2 The epigenetic components of circadian rhythmicity ........................................... 37

1.2.3 Circadian rhythmicity and aging ........................................................................... 47

1.3 The aging epigenome ........................................................................................................ 49

1.3.1 Epigenetic regression to the mean ........................................................................ 50

1.3.2 Epigenetic drift ...................................................................................................... 51

1.3.3 Epigenetic clock .................................................................................................... 53

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1.4 DNA modification profiling techniques ........................................................................... 54

1.4.1 Restriction enzyme- based methods ...................................................................... 56

1.4.2 Antibody enrichment-based methods ................................................................... 57

1.4.3 Bisulfite-based methods ........................................................................................ 58

1.5 Research Aims .................................................................................................................. 61

Chapter 2 Materials and Methods ................................................................................................. 63

2.1 Samples ............................................................................................................................. 64

2.2 DNA and RNA extraction ................................................................................................. 66

2.3 Bisulfite and oxidative bisulfite conversion ..................................................................... 67

2.4 Bisulfite padlock probes (BPP) ......................................................................................... 67

2.4.1 Probe design .......................................................................................................... 67

2.4.2 Probe preparation .................................................................................................. 68

2.4.3 Library preparation ............................................................................................... 70

2.4.4 Preprocessing and analysis of the BPP-seq data ................................................... 71

2.5 Methylation-sensitive restriction enzyme enrichment (MRE) .......................................... 72

2.5.1 MRE-chip protocol ............................................................................................... 72

2.5.2 Pre-processing and analysis of MRE-chip ............................................................ 73

2.6 Infinium HumanMethylation450 BeadChip assay ............................................................ 74

2.7 mRNA analysis of circadian and tissue specific transcripts ............................................. 74

2.8 Detection and analysis of oscillating modified cytosines (osc-mCs) ............................... 77

2.9 Analysis of the transcriptomic datasets ............................................................................. 78

2.10 Oscillating cytosine modification and mRNA phase shift analysis .................................. 79

2.11 Aging and motif analyses .................................................................................................. 79

2.12 Analysis of public DNA modification datasets ................................................................ 80

2.13 Analysis of association between circadian, aging, disease and variable CpGs ................ 81

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2.14 Ethics approval .................................................................................................................. 82

Chapter 3 Results .......................................................................................................................... 83

3.1 Circadian oscillation of DNA modification profiles ......................................................... 84

3.1.1 Oscillating modified cytosines in the mouse liver and lung ................................. 84

3.1.2 Oscillations of DNA modification involve both methylation and demethylation ........................................................................................................ 99

3.1.3 Circadian variation of DNA modification is not simulated by the oscillatory influx of white blood cells .................................................................................. 101

3.1.4 Steady state mRNA of DNA modification enzymes display tissue-specific and circadian patterns of expression .......................................................................... 103

3.1.5 osc-mCs are overrepresented in highly-expressed genes, genes encoding circadian mRNA transcripts, and E-box motifs .................................................. 105

3.2 Circadian oscillations of DNA modification and the aging epigenome ......................... 108

3.2.1 osc-mCs show differential properties with respect to their acrophase timing .... 108

3.2.2 osc-mCs are strongly associated with age-correlated cytosine modification changes ................................................................................................................ 110

3.2.3 Circadian and aging transcriptomes are associated ............................................ 118

3.3 Circadian oscillations of DNA modification in humans ................................................. 120

3.3.1 White blood cell fractions exhibit circadian variations ...................................... 120

3.3.2 Finding osc-mCs in the mouse liver and human neutrophils using MRE-chip .. 122

3.3.3 osc-mCs in human neutrophils ............................................................................ 127

3.3.4 The association between circadian and aging- dependent DNA modification in humans ................................................................................................................ 129

3.3.5 Contribution of osc-mCs to epigenetic variability .............................................. 130

3.3.6 osc-mCs are linked with DNA modification hallmarks of complex diseases .... 132

Chapter 4 Discussion, conclusions and future directions ........................................................... 135

4.1 Widespread oscillations of DNA modification ............................................................... 136

4.2 osc-mCs are associated with aging ................................................................................. 141

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4.3 osc-mCs are associated with disease ............................................................................... 144

4.4 Recommendations for future studies .............................................................................. 146

4.5 Conclusions ..................................................................................................................... 151

References ................................................................................................................................... 153

Appendices .................................................................................................................................. 184

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List of Tables Table 1.1. Selected disorders caused by loss of imprinting

Table 1.2. List of circadian oscillations in components of DNA modification machinery

Table 2.1. Sample information for the animal study

Table 2.2. Samples information for the measurement of white blood cell oscillations

Table 2.3. Primer list for the RT-qPCR analysis

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List of Figures Figure 1.1. The molecular circuitry of the circadian clock

Figure 1.2. The landscape of the circadian chromatin

Figure 1.3. Overview of techniques used in DNA modification studies

Figure 1.4. Circadian hallmarks of the aging epigenome

Figure 2.1. Schematic drawing of the bisulfite padlock probe approach

Figure 3.1. Locomotor activity verifying circadian entrainment of mice

Figure 3.2. Bisulfite and oxidative bisulfite padlock sequencing of mouse chromosome 7

Figure 3.3. Experimental workflow summary for the mouse experiments

Figure 3.4. Chromosome-wide analysis of circadian oscillations in DNA modification

Figure 3.5. Representative oscillating and non-oscillating CpGs in the 9-mo mouse liver detected

by padlock probes

Figure 3.6. Representative oscillating and non-oscillating CpGs in the 9-mo mouse lung detected

by padlock probes

Figure 3.7. osc-mC properties of liver and lung DNA from 9-month-old mice

Figure 3.8. Acrophase synchrony of osc-mCs common to both lung and liver

Figure 3.9. Characterization of oscillating modified cytosines in the 15-mo and 25-mo mouse

liver and lung

Figure 3.10. osc-mC properties of 5-methyl- and 5-hydroxymethylcytosines (5-mC and 5-hmC)

in 9-mo mouse liver tissue

Figure 3.11: RT-qPCR analysis of non-oscillating tissue-specific mRNA and oscillating positive

control mRNA in mouse liver and lung tissues

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Figure 3.12. RT-qPCR analysis of mRNAs of enzymes and proteins involved in the

demethylation- remethylation pathway in the 9-mo mouse liver and lung

Figure 3.13: The relationship between circadian transcription and osc-mCs

Figure 3.14. Average modification density of osc-mCs within wake and sleep acrophases in 9-mo

mouse liver and lung

Figure 3.15. Representative CpGs displaying age-associated changes in the 9-mo mouse liver

Figure 3.16. Representative CpGs displaying age-associated changes in the 9-mo mouse lung

Figure 3.17. Age- dependent cytosine modification changes in mouse tissues

Figure 3.18. The relationship between oscillating and aging-correlated cytosine modification in

mice

Figure 3.19. The direction of the association between osc-mCs and age-mCs in the mouse lung

Figure 3.20. osc-mC overlap with age-mC after adjusting for sample size or proportion of osc-

mCs in the mouse lung

Figure 3.21. Density contour plots of the magnitude of the age-associated mRNA changes as a

function of the amplitude of mRNA oscillations in the mouse liver and lung

Figure 3.22. Circadian rhythmicity of WBC counts and composition in peripheral blood

Figure 3.23. Quality control of the tiling microarrays

Figure 3.24. Scatterplot of the epigenetic variability p-values (one way ANOVA) in human

neutrophils and 9-mo mouse livers as measured by MRE-chip

Figure 3.25: DNA modification surface plots of all epigenetically variable microarray probe

signals

Figure 3.26. osc-mC properties in human neutrophils (tiling microarray experiment)

Figure 3.27. osc-mC properties in the mouse liver (tiling microarray experiment)

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Figure 3.28. osc-mC properties of individual cytosines in human neutrophils tested on 450K

microarrays

Figure 3.29. Age-dependent cytosine modification changes in populational study of human blood

Figure 3.30. Properties and distributions of osc-mCs within neutrophil promoters

Figure 3.31. Association of neutrophil osc-mCs with differentially modified cytosines identified

in published EWAS

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List of Appendices Appendix 1. List of primers used in padlock probe sequencing and library preparations

Appendix 2. Summary of the number of CpGs and fragments investigated in this thesis

Appendix 3. Quantile-quantile plot of observed harmonic regression p values for BPP-seq

experiments in mouse liver and lung

Appendix 4. MEME output for the oscillating cytosines in the mouse 9 mo liver

Appendix 5. MEME output for the oscillating cytosines in the mouse 9 mo lung

Appendix 6. MEME output for the oscillating cytosines in the human neutrophil

Appendix 7. Top 20 Gene Ontology terms significantly enriched in genes with significant

oscillation and aging in the mouse liver (terms with dispensability score < 0.7 merged)

Appendix 8. Top 20 Gene Ontology terms significantly enriched in genes with significant

oscillation and aging in the mouse lung (terms with dispensability score < 0.7 merged)

Appendix 9. Scree plots of principal component analyses

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List of Abbreviations

Infinium HumanMethylation450 BeadChip 450K array 5-carboxylcytosine 5-caC 5-formylcytosine 5-fC 5-hydroxymethylcytosine 5-hmC 5-methylcytosine 5-mC Alzheimer's disease AD Age-correlated cytosine modifications age-mC Cytidine deaminases of the activation-induced cytidine deaminase AID Albumin Alb Acute myeloid leukemia AML Ankyrin 1 ANK1 Apolipoprotein a1 Apoa1 Apolipoprotein B mRNA editing enzyme, catalytic polypeptide APOBEC Aryl Hydrocarbon Receptor Nuclear Translocator Like ARNTL Brain derived neurotrophic factor Bdnf Brain and Muscle ARNT-Like 1 BMAL1 Bipolar disease BPD Bisulfite padlock probe BPP Bisulfite BS Complement c3a receptor 1 C3ar1 CREB-binding protein CBP Clock-controlled genes Ccg C-c motif chemokine ligand 2 Ccl2 CxxC finger protein 1 CFP1 CpG islands CGI Chromatin immunoprecipitation ChIP Confidence Interval CI Casein kinase 1ε/δ CK1ε/δ Chronic lymphocytic leukemia CLL Circadian Locomotor Output Cycles Kaput CLOCK A dinucleotide of a cytosine followed by a guanine CpG A dinucleotide of a cytosine followed by an adenine, thymine, or cytosine CpH Cryptochrome CRY Circadian Time CT CCCTC-binding factor CTCF Discoidin domain receptor family, member 1 DDR1 DNA methyltransferase DNMT Estrogen receptor α ERα Embryonic stem cells ESC Epigenome-wide association studies EWAS Enhancer of zeste homolog 2 EZH2

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Fibroblast growth factor 1b Fgf-1B Fragile X mental retardation 1 FMR1 Frequency frq Fragile-X syndrome FXS Glyceraldehyde-3-phosphate dehydrogenase Gapdh Genomic DNA gDNA Genome-wide association studies GWAS Histone H3 lysine 14 H3K14 Histone H3 lysine 27 H3K27 Histone H3 lysine 36 H3K36 Histone H3 lysine 4 H3K4 Histone H3 lysine 9 H3K9 Histone H3 serine 10 H3S10 Histone deacetylases HDAC Histone methyltransferase HMT High-Performance Liquid Chromatography HPLC Inter-array correlation IAC Immunodeficiency, centromeric instability, facial anomalies syndrome ICF Insulin-like growth factor 2 Igf2 Interleukin-2 IL-2 Immunoprecipitation IP JumonjiC and ARID domain-containing histone lysine demethylase 1A JARID1A Long interspersed nuclear element 1 LINE1 Microtubule affinity-regulating kinase 4 MARK4 Methyl-CpG binding domain MBD IGHV-mutated CLL (favorable prognosis) M-CLL Methyl-CpG binding protein complex 2 MeCP2 Methylated DNA immunoprecipitation MeDIP Months old mo DNA modification (sum of 5-mC and 5-hmC) modC Methylation-sensitive restriction enzyme MRE Methyltransferase-directed transfer of activated groups mTAG Nuclear receptor corepressor 2 NCOR2 Next generation sequencing NGS Nucleosome remodeling deacetylase NuRD Octamer-binding transcription factor 4 Oct4 Odds ratio OR Oscillating modified cytosines osc-mC Oxidative bisulfite OxBS Oxytocin receptor OXTR Principal component PC p300/CBP-associated factor PCAF Period PER

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Polycomb repressive complex 2 PRC2 RNA polymerase II RNAPII Retinoic acid-related orphan receptor ROR Reduced representation bisulfite sequencing RRBS Quantitative reverse transcription PCR RT-qPCR S-adenosylhomocysteine SAH S-adenosylmethionine SAM Secretoglobin family 3a member 2 Scgb3a2 Suprachiasmatic nuclei SCN Schizophrenia SCZ Standard error of the mean SEM Surfactant protein c Sftpc Sirtuin 1 SIRT1 TET-assisted bisulfite TAB Thymine-DNA glycosylase TDG Ten-eleven translocation TET Transcription Factor TF Tumor protein p53 TP53 A dinucleotide of a thymine followed by a guanine TpG Tumor-suppressor gene TSG Transcription Start Site TSS IGHV-unmutated CLL (poor prognosis) U-CLL Ubiquitin-like plant homeodomain and RING finger domain 1 UHRF1 Whole-genome bisulfite sequencing WGBS Zeitgeber time ZT

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Chapter 1 Introduction

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1.1 Epigenetics

The term “epigenetics” originates from “epigenesis”, which can be traced back to the very

beginnings of embryological speculations. Historically, embryologists were divided between

those that believed in pre-formed elements within each cell (i.e. “preformationism”) and those

who thought that emergent characteristics were generated by the reactions of soluble components

(i.e. “epigenesis”) (Felsenfeld, 2014). Different iterations of that same question continue to

define epigenetics today, namely how does a single fertilized egg give rise to thousands of

subtypes of cells with differing patterns of gene expression in the same organism?

Epigenetics is now broadly defined as a set of heritable and reversible modifications of the

chromatin template that direct biological functions without any alteration of the underlying DNA

sequences (Berger, Kouzarides, Shiekhattar, & Shilatifard, 2009). The principal epigenetic

modalities are DNA modifications and histone variants and modifications. These are

(re)programmable mechanisms that determine the accessibility of the genetic template to the

cellular machinery in response to a large number of internal and external cues (ibid). This creates

countless possible readouts of a single template that substantially increases the informational

potential of the genome. Consequently, epigenetics makes differential utilization of the genetic

blueprint, across both time and space, feasible. This thesis will mainly focus on DNA

modifications with some discussion regarding histone modifications, particularly in the context

of their relationship to DNA modification and their regulation of circadian rhythmicity.

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1.1.1 DNA methylation and demethylation

DNA methylation is the most well established form of DNA modification. It involves the

covalent addition of a methyl group (-CH3) to the C5 position of the cytosine pyrimidine ring by

the DNA methyltransferases (DNMTs) to yield a 5-methylcytosine (5-mC). DNA methylation

occurs mainly on the palindromic CpG dinucleotide. Non-CpG, or more accurately CpH,

modifications have also been found in mammalian embryonic stem cells (ESC) and brain cells -

although their biological function is not yet clear (Lister et al., 2013; Xie et al., 2012). CpG

methylation, on the other hand, is a critical component of a number of developmental processes

including genomic imprinting, X-chromosome inactivation, and tissue-specific gene expression

(Bartolomei & Tilghman, 1997; Nagase & Ghosh, 2008; Panning & Jaenisch, 1998). The

remainder of this section is dedicated to the molecular underpinnings of DNA methylation.

1.1.1.1 DNA methyltransferases catalyze DNA methylation

There are three categories of DNMTs with a total of five enzymes that catalyze the DNA

methylation process. DNMT1 is a maintenance methyltransferase that mediates the conservative

replication of 5-mC through its preferential binding and methylation of hemi-methylated

cytosines (Probst, Dunleavy, & Almouzni, 2009). It is constitutively expressed with peaks during

the S phase of cell division (Kishikawa, Murata, Ugai, Yamazaki, & Yokoyama, 2003). Its

function requires the interaction of the accessory protein ubiquitin-like plant homeodomain and

RING finger domain 1 (UHRF1), and the homozygous deletion of either DNMT1 or UHRF1 is

lethal (Bostick et al., 2007; Sharif et al., 2007).

DNMT3A and 3B mediate de novo methylation of unmodified cytosines, particularly during

epigenetic reprogramming of germ cells and early embryogenesis (E. Li, 2002; Saitou,

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Kagiwada, & Kurimoto, 2012). While both enzymes have similar domain arrangements, they

exhibit the highest divergence in the target recognition region of their catalytic domains (Chedin,

2011). Consequently, distinct preferences for certain flanking sequences, regions, and nuclear

localizations have been identified for the two enzymes (ibid). There are also shorter isoforms of

both enzymes with distinct properties and patterns of expression. For example, while DNMT3A

is concentrated within the transcriptionally silent heterochromatin, DNMT3A2 is associated with

the transcriptionally active euchromatin (T. Chen, Ueda, Xie, & Li, 2002).

DNMT3L, another de novo DNMT, does not bind DNA very strongly and lacks a catalytic

domain but physically interacts with both DNMT3A and 3B and stimulates their activity (Z. X.

Chen, Mann, Hsieh, Riggs, & Chedin, 2005). DNMT3L is essential for genomic imprinting and

its deletion can lead to growth and placental defects (Arima et al., 2006). DNMT3L also plays a

structural role as DNMT3A is reported to form a tetrameric complex with it (3L-3A-3A-3L) and

methylate 2 CpGs separated by one helical turn, or a distance of 8-10 bp, within one binding (Jia,

Jurkowska, Zhang, Jeltsch, & Cheng, 2007; Y. Zhang et al., 2009). Lastly, DNMT2 is the most

evolutionary conserved methyltransferase, but its role is unclear as there are reports of its

function as both a transfer RNA (tRNA) methyltransferase and a weak distributive DNA

methyltransferase (Phalke et al., 2009; Schaefer et al., 2010).

1.1.1.2 TET enzymes and active demethylation

“Heritable and reversible” indicate a concurrent state of stability and malleability within the

epigenome. The original discovery of DNA methylation as a potential mechanism of cell

memory in mammals was based on the stability of the profiles of methylated cytosines (Holliday

& Pugh, 1975; Riggs, 1975). Lack of evidence for demethylation enzymes and active

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demethylation pathways fostered a traditional view of DNA methylation as a relatively stable

epigenetic mark. The erasure of 5-mC in mammals was previously believed to be possible

through two mechanisms: 1) passive demethylation through non-maintenance of 5-mC during

replication, and 2) enzymatic deamination of 5-mC via cytidine deaminases of the activation-

induced cytidine deaminase (AID)/apolipoprotein B mRNA editing enzyme, catalytic

polypeptide (APOBEC) followed by the repair of the resulting T:G mismatch (J. K. Zhu, 2009).

The recent discovery of 5-hydroxymethylcytosine (5-hmC) and the ten-eleven translocation

(TET) family of enzymes has triggered a shift in this understanding (Kriaucionis & Heintz, 2009;

Tahiliani et al., 2009). The demethylation process is facilitated by the TET enzymes (TET1, 2, or

3). These enzymes are oxoglutarate and iron dependent dioxygenases that arose from the

triplication of a common ancestor (Iyer, Anantharaman, Wolf, & Aravind, 2008; Iyer, Tahiliani,

Rao, & Aravind, 2009). The oxidation of 5-mC by TET enzymes generates the first and most

stable intermediate, 5-hmC, in the demethylation process (Ito et al., 2010). 5-hmC is then

oxidized by the TET enzymes in an iterative process to generate 5-formylcytosine (5-fC) and 5-

carboxylcytosine (5-caC), respectively, before ultimately being converted to an unmodified

cytosine via thymine-DNA glycosylase (TDG) and the base excision repair pathway (He et al.,

2011; Ito et al., 2011).

Since their discovery as a DNA demethylase, TET enzymes have emerged as significant factors

in development: TET1 and TET2 regulate both the pluripotency circuitry of embryonic stem

cells (ESC) and the reprogramming of primordial germ cells, while TET3 facilitates the rapid

demethylation of the paternal genome within the zygote (Gu et al., 2011; Hackett et al., 2013; Ito

et al., 2010). 5-hmC levels are 10-100 fold higher than those of 5-fC and 5-caC, suggesting 5-

hmC may have a potential biological role beyond a transient metabolite (Song et al., 2011).

6

Additionally, the genome-wide levels of 5-mC are fairly consistent across different tissues (4-5%

of all cytosines), while 5-hmC levels vary between 0.1% and 0.7% in different tissues (Globisch

et al., 2010). This may be due to tissue-specific TET/5-hmC functions or possibly due to tissue

specific demethylation processes (Branco, Ficz, & Reik, 2011). The latter explanation may be

why 5-hmC levels are the highest in mitotically-inactive neurons (Guo, Su, Zhong, Ming, &

Song, 2011). While active regulatory and transcribed regions exhibit higher levels of 5-hmC, a

conclusive picture regarding the role and dynamics of 5-hmC variation in terminally

differentiated somatic tissues is still lacking (Schubeler, 2015).

There are technical complexities involved in separating 5-mC and 5-hmC modifications that will

be discussed in detail in chapter 1.4. If unaccounted for this can result in a summation of 5-mC

and 5-hmC signals. Consequently, for the remainder of this thesis the terms DNA modification

and cytosine modification will be used to refer to the sum of 5-mC and 5-hmC modifications in

instances where the relative contribution of each is unclear.

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1.1.2 The structural and functional features of genome-wide DNA

modification

1.1.2.1 Genomic distribution of DNA modification

The uneven distribution of CpGs in the genome underlies how CpG-rich and CpG-poor regions

mediate different regulatory programs (Schubeler, 2012). In general, CpG dinucleotides are

present at less than a quarter of their expected frequency in the human genome (0.99% as

opposed to the expected 4.2%) (Scherer, 2008). This is due to a high rate of spontaneous

deamination of methylated cytosines that over the course of evolution has yielded numerous

CpG-to-TpG transitions (Duncan & Miller, 1980; Ehrlich, Zhang, & Inamdar, 1990). The

deamination of 5-mC generates a thymine, which is less efficiently repaired by the DNA

mismatch repair machinery compared to the uracil resulting from the deamination of an

unmodified cytosine. In fact, transitions of CpG dinucleotides are 18-30 fold more common in

germline mutations than mutations of other dinucleotides (Kong et al., 2012). This CpG

hypermutability is also asymmetric across the genome with a reported reduction in the rate of

CpG mutation inside compared to outside CpG islands (CGIs) (Cohen, Kenigsberg, & Tanay,

2011).

CGIs represent the CpG-rich stretches of the mammalian genome, on average about 1 kb in

length and containing G + C content of 50% or more (Illingworth & Bird, 2009). CGIs comprise

<1% of the genome but contain ~10% of CpGs, and typically mark the promoters and 5′ ends of

genes; although orphan CGIs have also been found in gene bodies and gene deserts (Patel, 2016).

60-70% of human genes contain these CpG-rich stretches within their promoters, while the rest

of the genome is relatively CpG-poor (P. A. Jones, 2012). These genes correspond to 93% of the

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genes expressed during embryogenesis, 40% of tissue-specific genes, and nearly all of the

housekeeping genes (Larsen, Gundersen, Lopez, & Prydz, 1992; Ponger, Duret, & Mouchiroud,

2001; J. Zhu, He, Hu, & Yu, 2008). Promoter-associated CGIs are mostly unmethylated, with

less than 10% methylated in normal cells (Illingworth & Bird, 2009). This generally

unmethylated state coincides with the observed reduction in CGI mutagenicity. The regions

flanking the CGIs within the promoter region, namely CGI shores (0-2 kb from CGI), shelves (2-

4 kb from CGI) and seas (remainder of promoter) may also have regulatory roles, but their

functions remain to be fully elucidated (Price et al., 2013). One study indicates that CGI shores,

for instance, are highly conserved regions that are enriched for tissue-specific and cancer-

associated DNA modification differences (Irizarry et al., 2009).

CpGs outside of CGI are mostly modified in human somatic cells (A. Bird, 2002). A major

component of these modified CpGs are the heavily modified CpGs of repetitive elements.

Retrotransposons, LINEs, satellite and other repeats represent half to two-thirds of the human

genome (de Koning, Gu, Castoe, Batzer, & Pollock, 2011). DNA methylation facilitates the

long-term, transcriptional silencing of these parasitic elements, and it has been suggested that

DNA methylation may have originally evolved as a host defense mechanism before acquiring its

role in gene regulation (Slotkin & Martienssen, 2007). Modification of repetitive elements is

significant in the formation of constitutive heterochromatin (e.g. pericentromeric

heterochromatin) and its disruption can lead to genome instability (Dejardin, 2015; Eden,

Gaudet, Waghmare, & Jaenisch, 2003). The remaining CpG modification patterns are

developmentally dynamic and tissue specific and will be discussed in the context of their

function.

9

1.1.2.2 DNA modification and gene expression

There are two types of associations between position of DNA methylation and transcription of a

gene: promoter methylation inversely correlates with transcriptional activity while gene body

methylation directly correlates with transcription (P. A. Jones, 1999). The methylation of CpGs

within promoter CGI is thought to either directly inhibit the binding of transcription factors (TF)

or to serve as a specific binding motif for other inhibitory regulators (E. Li & Zhang, 2014). The

role of DNA methylation inside gene bodies is more complex and is likely to serve a multitude

of functions.

Nearly 6% of the total number of protein-coding genes encode TFs, and a majority of them are

DNA sequence-specific TFs (Vaquerizas, Kummerfeld, Teichmann, & Luscombe, 2009). These

proteins are essential to the formation of the transcriptional machinery at transcription start sites

(TSS) and reductions in their activity can lead to transcriptional repression. DNA methylation at

binding sites of some TFs has been shown to inhibit the binding of TFs and consequently

downregulate the gene (Campanero, Armstrong, & Flemington, 2000; Choy et al., 2010; J. Kim,

Kollhoff, Bergmann, & Stubbs, 2003). It is also suggested that the observed widespread DNA

methylation outside promoters may be serving to silence transcriptional “noise” from

inappropriate TF binding (A. P. Bird, 1993).

There are, however, TFs whose binding is not influenced (Harrington, Jones, Imagawa, & Karin,

1988; Tate & Bird, 1993) or is even enhanced by CpG methylation (Rishi et al., 2010; Spruijt et

al., 2013). This complexity has led to some studies questioning the role of DNA methylation in

the inhibition of TF binding (Medvedeva et al., 2014). In an alternative model, TF binding is

proposed to precede DNA methylation and its binding is believed to sterically block association

10

and subsequent methylation by DNMTs (L. Han, Lin, & Hsieh, 2001; Stadler et al., 2011). This

controversy represents an important gap in our current knowledge of DNA methylation function:

is DNA methylation a cause or a consequence of transcriptional regulation (Bestor, Edwards, &

Boulard, 2015; Ptashne, 2013; S. C. Williams, 2013)?

A second mode of repression involves the indirect inhibition of transcription by the recruitment

of other proteins. 5-mC is recognized by the methyl-CpG binding domain (MBD) family of

proteins (Baubec, Ivanek, Lienert, & Schubeler, 2013). These proteins can facilitate

transcriptional downregulation through their association with other corepressors. For example,

Methyl-CpG binding protein complex 2 (MeCP2) and MBD2 recruit histone deacetylases

(HDAC) while MBD1 associates with the histone methyltransferase (HMT) SETDB1 to

facilitate silencing (Nan et al., 1998; Ng et al., 1999; Sarraf & Stancheva, 2004). The interactions

between DNA modifications and histone modifications are quite significant in the regulation of

the genome and will be discussed in more detail in chapter 1.1.3.

The exact role of DNA modification within gene bodies is less clearly defined. The extensive

modification of gene bodies in transcribed genes serves to silence the latent transcriptional

potential of repetitive DNA while allowing the transcription of the host gene to continue (Yoder,

Walsh, & Bestor, 1997). Additionally, gene body methylation may be involved in regulating

splicing, as there is a reduction of methylation from exons to introns at the exon-intron boundary

(Laurent et al., 2010). While gene bodies are relatively CpG-poor, they do contain CGIs that

likely represent “orphan promoters” that were potentially active in the early stages of

development (Illingworth et al., 2010). In fact, most genes contain alternative promoters and

gene body methylation may be blocking the formation of alternative transcripts from

downstream promoters (Maunakea et al., 2010).

11

TET1 and 5-hmC have also been implicated in regulating transcription in a context dependent

manner. TET1 has been shown to promote activity-induced demethylation and gene expression

in human brains (Guo et al., 2011) while others have conversely found the presence of 5-hmC in

the promoter to be transcriptionally repressive (Robertson, Robertson, & Klungland, 2011; K.

Williams et al., 2011). On the other hand, 5-hmC found within gene bodies has been shown to be

positively correlated with gene expression (Colquitt, Allen, Barnea, & Lomvardas, 2013; Madzo

et al., 2014). The concurrent enrichment of 5-mC and 5-hmC in transcribed genes is an intriguing

finding. One study found a bias for 5-mC on the antisense and 5-hmC on the sense strand (Wen

et al., 2014), suggesting the balance between the two may be rooted in transcription.

Interestingly, the ratio of 5-mC to 5-hmC within gene bodies may be a stronger predictor of

transcription than either marker by itself (Mellen, Ayata, Dewell, Kriaucionis, & Heintz, 2012).

This relationship is also significant in the context of splicing. Constitutive exons contain higher

levels of 5-hmC than the alternatively spliced ones (Khare et al., 2012), while the 5′ splicing sites

of the exon-intron boundaries show an enrichment of exonic 5-hmC in the brain (Wen et al.,

2014). Further research into competing methylation/demethylation processes within promoters

and intragenic regions is required to clarify the activities of 5-mC and 5-hmC within these

genomic elements.

1.1.2.3 DNA modification of enhancers and insulators

Enhancers are a group of cis-regulatory elements that remotely (dozens to hundreds of kb away)

activate gene expression through the recruitment of TFs – often in a cell-type specific manner

(Calo & Wysocka, 2013). There are approximately 400,000 enhancers in the human genome and

the active ones are annotated by the concurrent presence of histone 3 lysine 4 monomethylation

(H3K4me1) and histone 3 lysine 27 acetylation (H3K27ac) (Creyghton et al., 2010; Zentner,

12

Tesar, & Scacheri, 2011). The role of DNA methylation within enhancers is not fully understood.

While active enhancers often exhibit low levels of DNA methylation (Schmidl et al., 2009;

Thurman et al., 2012), a portion of active enhancers also display bivalency of both DNA

methylation and H3K27ac (Charlet et al., 2016). 5-hmC and 5-fC have also been found to be

enriched within active and poised enhancers, particularly in ESCs (Song et al., 2013; Yu, Hon,

Szulwach, Song, Zhang, et al., 2012). All these findings indicate that epigenetic regulation of

enhancers is dynamic and subject to DNA demethylation and re-methylation.

Insulators are another group of cis-regulatory element that block the interaction between an

enhancer and a promoter (P. A. Jones, 2012). DNA sequences bound by the CCCTC-binding

factor (CTCF) are one of the most extensively studied examples of insulators, and the binding of

CTCF can be inhibited by DNA methylation (Phillips & Corces, 2009; Renda et al., 2007).

Within the imprinted insulin-like growth factor 2 (Igf2) - H19 locus, the binding of CTCF

controls the enhancer-promoter interaction. Methylation of the CTCF binding site blocks its

binding and maintains transcription in the paternal allele (Schoenherr, Levorse, & Tilghman,

2003). CTCF can also mediate the pausing of RNA polymerase II at the exon-intron boundary

and facilitate exon inclusion, and it is thought that the variable inhibition of CTCF binding may

be a potential mechanism through which DNA methylation can regulate splicing (Shukla et al.,

2011). Genome-wide studies, however, have suggested that the 5-mC/CTCF mechanism may

also be operating in reverse, namely that the binding of CTCF is initiating local demethylation

(Stadler et al., 2011). More research is needed to fully elucidate the role of DNA methylation in

regulating the function of insulators.

13

1.1.3 The relationship between modifications of DNA and histones

1.1.3.1 Histone modifications

DNA is packaged into nucleosomes: 147 base pairs wrapped around an octamer of four core

histone proteins (H2A, H2B, H3, and H4) that can be chemically modified and exchanged with

other histone variants (Kornberg & Lorch, 1999). Histones contain amino terminal tails that

extend outwards, and some amino acids on these tails can be modified post-translationally

through phosphorylation, ubiquitination, sumoylation, acetylation, and methylation (Vaquero,

Loyola, & Reinberg, 2003). Methylation and acetylation of lysine residues are amongst the most

well-established epigenetic elements.

Within the tail of histone H3, the methylation of lysine (K) residues at positions 4 (H3K4) and 36

(H3K36), are associated with active gene transcription (Zhou, Goren, & Bernstein, 2011). H3

lysine 4 trimethylation (H3K4me3) and dimethylation (H3K4me2) are associated with an open

chromatin state and RNA polymerase II binding (Guenther, Levine, Boyer, Jaenisch, & Young,

2007; Z. Wang et al., 2008). Similarly, the acetylation of lysines at numerous positions is

associated with increased gene expression – through either the charge neutralization and

increased accessibility of the DNA template or through the binding and recruitment of other

proteins (Clayton, Hazzalin, & Mahadevan, 2006; Z. Wang et al., 2008). Within gene bodies,

H3K36me3 modifications mark transcribed genes, peaking near the 3′ end (Barski et al., 2007).

Methylation of lysine at positions 9 (H3K9me2, H3K9me3) and 27 (H3K27me3) operate in the

other direction as they are associated with transcriptional repression, heterochromatin formation,

and the recruitment of polycomb repressive complexes (Kondo et al., 2008; Ku et al., 2008).

Interestingly, another layer of information is encoded by the combination of histone marks that is

14

thought to be significant in the context of differentiation and cell fate (Vastenhouw & Schier,

2012). One instance of these bivalent modifications is the co-occupation of the repressive

H3K27me3 and the permissive H3K4me3 marks within the enhancers and promoters of lineage-

regulatory genes of ESCs (Bernstein et al., 2006; Rada-Iglesias et al., 2011). These modifications

keep developmental genes in a poised state such that lineage specific expression is repressed (by

H3K27me3) in a pluripotent state but primed for expression (by H3K4me3) upon differentiation

(Vastenhouw & Schier, 2012). Topics on development and differentiation are further discussed

in chapter 1.1.4.

The preceding examples of histone modifications are meant to provide a brief synopsis of this

vast field. While histone modifications and DNA modifications involve different pathways and

enzymes, there is a biological relationship between the two that underlies important aspects of

chromatin dynamics and genomic function (Roadmap Epigenomics et al., 2015).

1.1.3.2 DNA methylation and histone deacetylation

DNA methylation inversely correlates with histone acetylation. This is consistent with the fact

that most MBD proteins associate with HDACs (A. P. Bird & Wolffe, 1999). MeCP2 facilitates

transcriptional repression through its interaction with the SIN3A HDAC complex, MBD2 is

associated with the nucleosome remodeling deacetylase (NuRD) complex, while MBD1 recruits

HDAC3 to mediate transcriptional silencing (Q. Feng & Zhang, 2001; Nan et al., 1998; Villa et

al., 2006).

15

1.1.3.3 DNA methylation and H3K9 methylation

DNA methylation and H3K9 methylation correlate with each other – and negatively correlate

with H3K4 methylation (Cheng, 2014). Studies in Neurospora and Arabidopsis have shown a

direct dependence of DNA methylation on H3K9 methyltransferases in these species (ibid). In

mammals, there is also evidence for an interaction between these two epigenetic marks. The 5-

mC binding protein, MBD1, can form a complex with H3K9 methyltransferases SETDB1 and

SUV39H1 (Fujita et al., 2003; Sarraf & Stancheva, 2004) while cells deficient in SUV39H show

a reduction in heterochromatic DNA methylation (Lehnertz et al., 2003). There is also a link to

the maintenance of DNA methylation. DNMT1 has been shown to interact with the H3K9me2

methyltransferases G9a and G9a-like protein (Esteve et al., 2006), while the DNMT1 accessory

protein, UHRF1, binds directly to a methylated H3K9 histone to maintain DNMT1 stability

(Rothbart et al., 2012). Lastly, it is suggested that the maternal pronucleus is protected from the

post-fertilization global DNA demethylation of the zygote due in part to the presence of H3K9

methylation marks on the maternal genome (Nakamura et al., 2012).

1.1.3.4 DNA methylation and H3K4 methylation

As previously described, H3K4 methylation is enriched within active promoters, possibly due to

the unmethylated status of CGIs within these promoters. The H3K4 methyltransferase SETD1 is

recruited to CGIs in neuronal progenitor cells through its interaction with the CxxC finger

protein 1 (CFP1) - a protein that only binds unmethylated CpGs (Thomson et al., 2010).

Similarly, another H3K4 methyltransferase named mixed-lineage leukemia 1 (MLL1) has also

been shown to bind unmethylated CpGs (Cierpicki et al., 2010). It is thought that MLL1 prevents

DNA methylation as its genetic disruption in mice leads to loss of H3K4 methylation and de

16

novo methylation of Hox gene promoters (Terranova, Agherbi, Boned, Meresse, & Djabali,

2006). Lastly, DNMT3L, part of the de novo DNA methylation machinery, binds the extreme

amino terminus of H3 and this interaction is inhibited only with the methylation of H3K4 (Ooi et

al., 2007). This suggests that DNMT3L may be probing for histone modifications and is

initiating de novo DNA methylation in the absence of H3K4 methylation.

1.1.3.5 DNA methylation and H3K27 methylation

While both DNA methylation and H3K27 methylation account for transcriptional repression,

their presence is mutually exclusive. CGI promoters devoid of DNA methylation are potential

sites of H3K27 methylation (Kondo et al., 2008) and nucleosomal, methylated DNA is resistant

to the imposition of H3K27 methylation marks (Bartke et al., 2010). This antagonism is

evidenced by the fact that the inhibition of DNMTs can cause the spreading of H3K27

methylation into loci that are normally DNA methylated (Lindroth et al., 2008). Moreover,

global DNA demethylation during the epigenetic reprogramming of both primordial germ cells

and the paternal pronucleus is followed by a global increase in H3K27 methylation (Puschendorf

et al., 2008; Santos, Peters, Otte, Reik, & Dean, 2005). Conversely, TET1 and 5-hmC are

enriched at bivalent (H3K27me3 and H3K4me3) genes of ESCs and the depletion of TET1

diminishes polycomb repressive complex 2 (PRC2; catalyzes H3K27 trimethylation) binding

within these loci (Pastor et al., 2011; Wu et al., 2011). The depletion of TET1 in ESCs also leads

to an upregulation of many PRC2 targets, suggesting that the failure to remove 5-mC may be

inhibiting PRC2 binding (Wu et al., 2011). There is, however, a developmental crosstalk

between the two modifications as many promoters marked by H3K27me3 in ESCs acquire DNA

methylation during differentiation (Mohn et al., 2008). The developmental trajectory and how it

17

influences cellular identity is an important aspect of the epigenetic machinery and will be further

discussed in the following chapter.

18

1.1.4 Development, differentiation and cellular identity

Conrad Waddington famously described the process of differentiation as a ball rolling from an

undifferentiated state at the top of a hill down an epigenetic landscape and falling into

increasingly more lineage-specific, inescapable valleys – with the ridges of this landscape

serving as demarcations of different cell types (Waddington, 1957). While recent discoveries

have shown this dogma to be reversible through cellular reprogramming (see (K. Takahashi &

Yamanaka, 2006) for an example), the ridges remain an accurate depiction of the epigenetic

determinants of cellular identity.

1.1.4.1 Genomic imprinting and epigenetic reprogramming in

mammalian development

One of the most extensively studied functions of DNA methylation is its role in the epigenetic

reprogramming observed in early mammalian development. Immediately following fertilization,

the male pronucleus undergoes rapid, global, active demethylation (Smith et al., 2012). The

maternal genome is, conversely, demethylated passively and gradually by the loss of

maintenance of DNA methylation during DNA replication (Reik & Surani, 2015).

Demethylation continues until the morula stage and is then followed by the reestablishment of

the DNA methylome concurrent with differentiation and formation of somatic cells (ibid).

There is one noted exception to this preimplantation demethylation process – imprinted genes.

Genomic imprinting refers to parent-of-origin specific DNA methylation and expression of

several hundred genes (Barlow & Bartolomei, 2014). Numerous imprinted gene clusters and

their differentially methylated regions have been identified and gene expression is typically

regulated downstream of a methylation-dependent transcription of a long non-coding RNA

19

emerging from within these clusters (Liu, Yu, Litman, Chen, & Weinstein, 2000; Shiura et al.,

2009; Takada et al., 2002). These imprints are: i) established during the development of gametes,

ii) maintained in the developing organism, iii) erased in the establishment of primordial germ

cells, and iv) re-established again at a later stage of germ cell development (Reik & Walter,

2001). Primordial germ cells are derived from neighbouring somatic cells in the developing

embryo in a process that represses the somatic circuitry and yields a reacquired pluripotent state

(Reik & Surani, 2015). This is concomitant with a global demethylation process – similar, with

the exception of imprinted loci, to what occurs post-fertilization. In both instances, DNA

demethylation precedes an increase in cellular potency or “stemness”.

1.1.4.2 Differentiation circuitry and cellular identity

An interplay of TFs, epigenetic regulation, and external signaling yields lineage decisions in

mammalian development (Hemberger, Dean, & Reik, 2009). Parts of the transcriptional network

associated with pluripotency, mediated by TFs like octamer-binding transcription factor 4 (Oct4)

and Nanog, are silenced upon differentiation by DNA methylation (Meissner, 2010; Smith,

Sindhu, & Meissner, 2016). This is a necessary process as differentiation is inhibited in the

absence of DNA methylation (Jackson et al., 2004). Similarly, a complex interaction between

transcriptional and epigenetic regulations is thought to direct cellular identity and progression

through adult cell lineages.

DNA methylation plays a large role in the determination of lymphoid/myeloid fates in

hematopoiesis. DNMT1 deficient mice suffer from both stem cell self-renewal defects and an

upregulation of the more ‘default’ myeloid line (Broske et al., 2009; Trowbridge, Snow, Kim, &

Orkin, 2009). Expectedly, demethylation is also a vital part of differentiation as it can mediate

20

upregulation of cell-specific transcriptional networks. For instance, the progression of a

granulocyte/macrophage progenitor is associated with the promoter demethylation and

upregulation of numerous genes, and active demethylation has been implicated in the

progression of monocytes into macrophages (Ji et al., 2010; Klug et al., 2010). Furthermore,

mutations of TET2 are commonly implicated in myeloid leukemia (Moran-Crusio et al., 2011;

Quivoron et al., 2011).

Similar trends are also observed in the brain, a notoriously heterogeneous tissue where more than

70% of genes are expressed in only 20% of its cells (Lein et al., 2007). Studies have shown

differing patterns of DNA modifications in different layers and neuronal sub-populations of the

brain (Grayson & Guidotti, 2013; Olson, Zachariah, Ezeonwuka, Liyanage, & Rastegar, 2014).

Furthermore, the depletion of DNMT1 yields an upregulation of glial, but not neuronal,

associated TFs in neuronal progenitor cells – suggesting DNA methylation plays a role in

determination of lineage specificity in the brain (Fan et al., 2005).

All the above examples highlight significant differences between the genetic and epigenetic

codes. All the tissues within one individual will have nearly identical genetic information and

display genetic differences with respect to other individuals. Any one tissue, however, will

exhibit a higher degree of epigenetic similarity across individuals compared to different tissues

within one individual. In other words, intra-individual DNA modification variation is higher

across different tissues than inter-individual variation for the same tissue (Byun et al., 2009).

Cerebral cortices of unrelated individuals, for instance, contain DNA modification patterns more

similar to each other than those of the cerebellum from their corresponding brains (Ladd-Acosta

et al., 2007).

21

These are important considerations and have led to an increasing awareness of cellular

heterogeneity in epigenetic studies. Failure to account for differences in cellular composition of a

tissue can provide false results that are more indicative of differences in proportion than genuine

epigenetic differences. Whole blood, for instance, is a heterogenous collection of nucleated white

blood cells, each with a unique DNA modification profile (Houseman et al., 2012; Reinius et al.,

2012). This is a significant confounder, for instance, in studies that use whole blood to

investigate DNA modification changes associated with aging as there is strong evidence of

enrichment of myeloid cells with age (Jaffe & Irizarry, 2014). Likewise, brain studies that have

used bulk brain tissue may be similarly confounded as there have been reports of changes in

neuronal composition of brain in Alzheimer’s disease, schizophrenia, bipolar disorder, and major

depression (Cotter, Hudson, & Landau, 2005; Cummings & Cole, 2002; Uranova, Vostrikov,

Orlovskaya, & Rachmanova, 2004). Corrections of compositional variation in blood and bulk

brain are possible and algorithms which use cell-specific methylation patterns are available for

an in silico rectification of cellular effects (Guintivano, Aryee, & Kaminsky, 2013; Houseman et

al., 2012; Jaffe & Irizarry, 2014).

22

1.1.5 Epigenetic syndromes

The best evidence for the role of DNA methylation in non-malignant diseases comes from those

caused by loss of genomic imprinting (Table 1.1). Parent-specific imprinting defects within the

same locus can yield different syndromes, sometimes with opposite symptoms (Zoghbi &

Beaudet, 2016). For instance, defective imprinting within the imprinting center of the 15q11-q13

region can lead to Prader-Willi (PWS, paternal defect) or Angelman (AS, maternal defect)

syndromes (Nicholls & Knepper, 2001).

There are also a number of human diseases associated with mutations related to the epigenetic

machinery. Rett syndrome is a dominant X-linked disease characterized by motor abnormalities,

ataxia, seizures, and language regression that is caused by mutations within MECP2 (Amir et al.,

1999). Immunodeficiency, centromeric instability, facial anomalies syndrome (ICF) is another

genetic disease of the DNA methylation machinery, appropriately named after its most

significant phenotypes. This disease is caused by the loss-of-function mutations in DNMT3B

concurrent with a loss of methylation within the centromeric tandem repeats (Xu et al., 1999).

Lastly, fragile-X syndrome (FXS) is one of the most common causes of mental retardation and

its cytogenetic profile is associated with a fragile, constricted region on the X chromosome

(Sutherland, 1977). The affected people exhibit aberrant methylation and subsequent

transcriptional repression of the fragile X mental retardation 1 (FMR1) gene, mediated through

the expansion of a noncoding CGG repeat (6-60 in normal vs 200+ in full mutation) within the

FMR1 5′ region (Verkerk et al., 1991). The diseases listed thus far are examples of non-

malignant disorders with recognized and accepted causes within the DNA methylation process.

There are other diseases with more subtle and multifactorial contributions from the methylome

that need to be summarized.

23

Table 1.1. Selected disorders caused by loss of imprinting

Disorder Imprinting cluster Symptoms include References

Prader-Willi Syndrome 15q11-q13 (PWS cluster)

Hypotonia, mild mental retardation, and hyperphagia

(Nicholls & Knepper, 2001)

Angelman Syndrome 15q11-q13 (PWS cluster)

Seizures, severe mental retardation, and lack of speech

(Nicholls & Knepper, 2001)

Beckwith-Wiedemann Syndrome

11p15.5 (KCNQ1 and IGF2 cluster)

Fetal and postnatal overgrowth

(Bliek et al., 2009; DeBaun, Niemitz, & Feinberg, 2003)

Silver-Russell Syndrome 7p11.2 (GRB10 cluster)

11p15.5 (KCNQ1 and IGF2 cluster)

Growth retardation (Gicquel et al., 2005)

Pseudohypoparathyroidism 20q13.2 (GNAS cluster)

Hypocalcemia and insensitivity to the parathyroid hormone

(Bastepe, 2008; Bastepe et al., 2005)

24

1.1.6 Epigenetic factors in human complex disease

The potential role of DNA methylation in cancer was speculated as early as 1983 (Feinberg &

Vogelstein, 1983) and nearly two decades later in non-malignant common disease (Petronis,

2001). A wealth of research, including case-control, twin, and longitudinal studies undertaken

since these early publications have now established epigenetics as a third group of risk factors,

along with DNA mutations/polymorphisms and, environmental risk factors, that may contribute

to the etiology of complex non-Mendelian diseases. The epigenetic theory of complex non-

Mendelian disease is based on three postulates: i) epigenetic regulation is as important as DNA

sequence and both contribute to the phenotypic outcomes; ii) epigenetic factors are heritable in

somatic cells and may also be heritable meiotically; and iii) epigenetic modifications of DNA

and histones are dynamic and can change during development, aging, under the influence of

environmental factors, and stochastically (Petronis, 2010).

1.1.6.1 DNA modifications and cancer

Cancer can be described as a disease of dysregulated cellular control pathways leading to

uncontrollable growth and division of cells without regard for the needs of the organism (Baylin

& Jones, 2016). Consequently, genetic alterations, epigenetic abnormalities, and their

interactions have emerged as critical factors in oncogenesis (Baylin & Jones, 2011; Shen &

Laird, 2013). There are three groups of findings pointing at the role of DNA modification in

carcinogenesis: i) global loss of DNA modification, ii) CGI gain of modification, and iii)

mutagenicity of 5-mC.

Genomes of malignant cells are characterized by global loss of modified cytosines, which is

thought to contribute to genomic instability and aneuploidy (Bert et al., 2013; Ehrlich & Lacey,

25

2013). Non-CGI CpGs are ~80% modified in normal somatic cells, but this number drops to

~40-60% modified in cancerous cells (Berman et al., 2011; K. D. Hansen et al., 2011; Hon et al.,

2012). This loss of modification is concentrated in blocks of 28 Kb – 10 Mb and can include

focal modification of CGIs within promoters of various genes including tumor-suppressor genes

(TSGs) (ibid). This is where the second trend of malignant epigenomes emerges: cancer-

associated CGI gain of modification has been reported in 5-10% of normally unmodified CGI

promoters (Baylin & Jones, 2011). The importance of this gain of modification is evident by that

fact that nearly half of TSGs implicated in familial cancers undergo CGI modification in

sporadic forms of cancer (Baylin & Herman, 2000).

Lastly, the mutagenicity of 5-mC may also play a role in oncogenesis. Early examples of 5-mC

mutagenicity in cancer were documented in the tumor protein p53 (TP53) gene (Greenblatt,

Bennett, Hollstein, & Harris, 1994). Nearly 30% of all mutations of TP53, implicated in >50% of

all human tumors, occur within CpG dinucleotides (Olivier et al., 2002). Additionally, 38%,

43%, and 48% of point mutations in pancreatic, brain, and colorectal cancers also occur within a

CpG context (S. Jones et al., 2008; Parsons et al., 2008). In addition to the increased rate of

spontaneous deamination, cytosine methylation can also increase the formation of DNA adducts

after exposure to carcinogens and the formation of pyrimidine dimers in skin cells exposed to

sunlight (Pfeifer, Tang, & Denissenko, 2000).

Blood-based malignancies frequently involve alterations in the key players of DNA modification

machinery. 44% of cases of acute myeloid leukemia (AML) showed at least one nonsynonymous

mutation within the genes encoding DNA modification enzymes (Cancer Genome Atlas

Research, 2013). This corroborates previous findings of genetic alterations of DNMT3A (Ley et

al., 2010; X. J. Yan et al., 2011) and TET2 (Abdel-Wahab et al., 2009; Delhommeau et al., 2009)

26

in AML patients. Studies have also identified potential contributions of DNA modification

alterations in chronic lymphocytic leukemia (CLL) (Kanduri et al., 2010; Tong et al., 2010). One

recent study identified gene body and enhancer modification as the most frequent source of DNA

modification difference between normal and leukemic B-cells (Kulis et al., 2012). They

identified DNA modification signatures that can discriminate between two clinical subtypes of

chronic lymphocytic leukemia, one with favorable prognosis and another one of poor prognosis.

While extensive research has significantly improved our understanding of DNA modification

markers of cancer, the hierarchy and organization of epigenetic events leading to oncogenesis

remains an active area of research. It is important to note that the epigenome is physiologically

and environmentally reprogrammable and this can contribute to cancer risk states and cancer

progression (Feil & Fraga, 2012; O'Hagan et al., 2011). This dynamic nature of DNA

modifications will be discussed in chapter 1.1.7.

1.1.6.2 Neuropsychiatric disorders

Neuropsychiatric diseases often display monozygotic twin discordance, sexual dimorphism,

parent-of-origin effects, fluctuating disease course, and partial recovery – factors that are

difficult to explain without an underlying epigenetic dysregulation (Labrie, Pai, & Petronis,

2012; Petronis, 2004). Furthermore, recent studies have established the role of epigenetics as a

critical factor in synaptic plasticity, learning, memory and overall cognitive function (Day &

Sweatt, 2011).

Initial epigenome-wide association studies (EWAS) of schizophrenia (SCZ) and bipolar disease

(BPD) provided a significant set of differentially modified loci, including sex-specific, X-

chromosome, and neurodevelopmental genes (Dempster et al., 2011; Mill et al., 2008; Rosa et

27

al., 2008). Similarly, the upregulation of DNMTs and variations in methyl-harbouring metabolic

precursors were identified in SCZ and BPD (Guidotti et al., 2007; Kale et al., 2010; Veldic,

Guidotti, Maloku, Davis, & Costa, 2005; Zhubi et al., 2009). Nevertheless, the success of the

first round of epigenomic studies was limited, with only moderate changes in DNA modification

reported - perhaps in part due to usage of bulk brain and/or older technology (Akbarian, 2010;

Guintivano et al., 2013).

More recently, studies have sought to incorporate data from large-scale genome-wide association

studies (GWAS) of SCZ alongside more precise and controlled analysis of DNA modification in

order to better guide the interpretation of both genetic and epigenetic markers (Hannon, Spiers, et

al., 2016; Jaffe et al., 2016; Pidsley et al., 2014). In one study, Hannon et al. analyzed genome-

wide patterns of DNA modification in 1714 individuals from 3 cohorts, and found strong

evidence for the co-localization of genetic association and differential modification in SCZ

(Hannon, Dempster, et al., 2016). Another study used a total of 1831 samples to identify a list of

172 SCZ-associated CpGs (FDR < 0.2) that were reproducible in a replication dataset (Montano

et al., 2016). Modification differences were observed in genes implicated in neuronal function

(e.g. nuclear receptor corepressor 2 (NCOR2)), SCZ (e.g. discoidin domain receptor family,

member 1 (DDR1)), and also Alzheimer’s disease (e.g. microtubule affinity-regulating kinase 4

(MARK4)). The association with Alzheimer’s disease (AD) is either due a common artefact or

potentially due to dysregulation of a common network between the two diseases (Douaud et al.,

2014).

AD is the most common form of dementia and it is believed that 1 in 85 people will suffer from

it by 2050 (Brookmeyer, Johnson, Ziegler-Graham, & Arrighi, 2007). Old age is the major risk

factor for AD, and studies have identified epigenetic similarities between aging and AD brains

28

(Bennett et al., 2015; Coppede, 2014; G. Oh et al., 2016). Similar to psychosis, EWAS studies of

AD have also not proven conclusive with different subsets of genes emerging from each study

(Sanchez-Mut & Graff, 2015). One exception appears to be the transcriptionally repressive

increase in DNA modification of the Ankyrin 1 (ANK1) gene, reported by two independent

studies published at the same time (De Jager et al., 2014; Lunnon et al., 2014).In particular, DNA

modification profiles in 708 subjects revealed 71 CpGs significantly associated with AD – 11 of

which were validated in a different cohort, along with the altered expression of 7 nearby genes

(De Jager et al., 2014). The potential role of ANK1 is currently not known – although it may be a

part of the signalling cascade modulating microglia activation (ibid). It is important to consider

that all the studies summarized in this section are based on correlations and not causation. It

remains to be seen if any of the identified markers are an underlying driver of pathology or an

important consequence of other dysregulated biological processes.

29

1.1.7 The temporality and malleability of DNA modifications

Traditionally, the covalent attachment to DNA and convenient accessibility of the methyl group,

combined with its presumed indelibility in the absence of an active demethylation process, made

DNA methylation a suitable epigenetic mark in large-scale epidemiological studies (Kriaucionis

& Tahiliani, 2014; Talens et al., 2010). However, recent studies, including the identification of

an active demethylation pathway, suggest a potentially more dynamic role for DNA

modifications.

In one study of the temporal stability of DNA modification in blood and buccal cell samples,

only 5 out of 8 loci that were tested years apart displayed significant correlations in their

modification patterns, and the authors discussed the need for careful planning of experiments as

only a proportion of the genome may be informative for epidemiological studies (Talens et al.,

2010). Similarly, blood samples collected 9 months apart from 24 women revealed that only 36%

of the modification variance observed could be explained due to their inter-individual variability

– with the remainder likely due to a mixture of technical and intra-individual variations

(Shvetsov et al., 2015).

DNA methylation is, in fact, only partially stable. While some loci like the differentially

methylated regions of imprinted genes can remain stable throughout life, others like the promoter

of the interleukin-2 (IL-2) gene can exhibit dynamic DNA modification differences within 20

minutes of a stimulus (Bruniquel & Schwartz, 2003; Hoyo, Murphy, & Jirtle, 2009). For

instance, acute gene activation and promoter hypomethylation can be seen in skeletal muscles

shortly after exercise or caffeine exposure (Barres et al., 2012). Access to a running wheel and

subsequent voluntary exercise for 7 days has also been shown to stimulate DNA demethylation

30

and gene expression of brain derived neurotrophic factor (Bdnf) in the rat hippocampus –

concurrent with increases in Mecp2 levels (Gomez-Pinilla, Zhuang, Feng, Ying, & Fan, 2011).

Furthermore, TET1-mediated promoter demethylation and subsequent expression of Bdnf and

fibroblast growth factor 1b (Fgf-1B) in the rat hippocampus can be seen within 4 hours of

electroconvulsive stimulation (Guo et al., 2011; Ma et al., 2009), while loss of DNA

modification of Bdnf are observed within two hours of contextual fear conditioning (Lubin, Roth,

& Sweatt, 2008).

In humans, modification of the oxytocin receptor gene (OXTR) in whole blood is observed

within 10 minutes of exposure to psychosocial stress, and these levels were shown to recover

within 90 min – although variation in cellular composition could not be ruled out (Unternaehrer

et al., 2012). In one study, significant DNA modification changes were observed in a number of

gene promoters of 63 individuals whose blood samples were collected three days apart (Byun et

al., 2012). These dynamic changes are not limited to animals. Chilling tomatoes for 8 days

increases global methylation rates, with one third of the ~30,000 identified differences coming

from promoters (B. Zhang et al., 2016). Interestingly, 5-mC levels reversed after only 1 day in

warmer temperatures. What fraction of the methylome is malleable or what separates the

dynamic from the stable loci is currently unknown. One study suggests that this plasticity may be

limited, with only 2% of the observed changes in gene expression over the course of 3 months

explained by intra-individual changes in DNA modification levels (Furukawa et al., 2016). Their

analysis, however, was mainly restricted to CGIs and did not account for potential cyclical

changes in DNA modification profiles.

The plasticity of DNA modification is now well-established for longer time intervals such as

years and decades. Three trends can be distilled from the limited number of longitudinal studies

31

of intra-individual DNA methylation changes tracked over years: i) global and gene-specific

modifications change, ii) many of these changes overlap with known age-associated loci from

cross-sectional studies, and iii) many of these changes can already be observed in childhood

(Bjornsson et al., 2008; Gervin et al., 2016; Madrigano et al., 2012; Tan et al., 2016). There are

also four possible, but not necessarily independent, sources based on cross-sectional studies that

potentially explain these variations: i) genetic factors (Bjornsson et al., 2008; Heijmans, Kremer,

Tobi, Boomsma, & Slagboom, 2007), ii) environmental factors (Busche et al., 2015; B. C.

Christensen et al., 2009), iii) stochastic variation, or epigenetic drift (Feinberg, 2014; Talens et

al., 2012), and iv) predictable and therefore deterministic changes, or epigenetic “clock”

(Horvath, 2013). These topics will be discussed in more depth in the context of aging.

Recent studies suggest that there is another type of variation in DNA modifications - periodicity.

These typically involve programmed biological processes in short duration, such as daily and

seasonal oscillations. Seasonal phenotypes such as the breeding of hamsters and the diapause

response of wasps, for instance, are mediated by reversible changes in DNMT levels, promoter

methylation, and gene expression (Pegoraro, Bafna, Davies, Shuker, & Tauber, 2016; Stevenson

& Prendergast, 2013). Similar seasonal variations in DNA modification profiles have also been

reported in humans (Lockett et al., 2016; Ricceri et al., 2014). Shorter cycles can also be

mediated by DNA modifications. For example, cyclical and strand-specific DNA modifications

regulate the coordinated ~100 min transcriptional cycling of numerous gene promoters in

response to estrogen receptor α (ERα) activation (Kangaspeska et al., 2008; Metivier et al.,

2008). There are now reports of a potential role for the DNA modification pathway in daily, or

circadian rhythmicity, and section 1.2 will focus on this well-established of biological

oscillations and explore how DNA modifications may be implicated in this process.

32

1.2 Circadian Rhythmicity From an evolutionary perspective, spatial and temporal compartmentalization of biochemical

reactions have proven to be invaluable. Spatial compartmentalization, from organelles to organs,

allows the species to perform numerous incompatible reactions in varied microenvironments.

Periodic oscillations, on the other hand, allow biochemical reactions to occur only during

optimal conditions in a predictably changing environment (Chaix, Zarrinpar, & Panda, 2016).

Daily oscillations of environmental conditions, caused by Earth’s daily rotation, have created a

constant selective pressure that has led to the evolution of intrinsic circadian timekeeping in all

eukaryotes and many prokaryotes in order to anticipate these temporal cycles (Roenneberg &

Merrow, 2002; Yerushalmi & Green, 2009). Circadian (circa diem which means “about a day”)

clocks are therefore defined as intrinsic timekeeping processes that allow an organism to exhibit

certain behavioural (e.g. sleep-wake cycles) and physiological (e.g. body temperature

fluctuations) outputs at the appropriate time of the day (Schibler & Sassone-Corsi, 2002). These

processes are characterized by their dual ability to persist in the absence of environmental cues,

or zeitgebers, while still remaining malleable to synchronization, or entrainment, by such cues

(Merrow, Spoelstra, & Roenneberg, 2005). In mammals, this is made possible by the two-layered

organization of circadian systems.

The self-sustained, core molecular mechanism of circadian clocks is cell-autonomous and

is conserved in nearly all cells of the body (Brown & Azzi, 2013; Stratmann & Schibler, 2006).

There is an additional organism-level organization orchestrated by the central “master clock” in

the suprachiasmatic nuclei (SCN) of the hypothalamus, which entrains peripheral tissues to

distinct phases in response to the primary zeitgeber, light (Slat, Freeman, & Herzog, 2013; Yoo

et al., 2004). Direct entrainment of peripheral clocks is achieved through a combination of

33

nervous stimuli (e.g. autonomous nervous system) and humoral signals (e.g. glucocorticoids),

while indirect entrainment can occur via temperature fluctuations and feeding (Brown & Azzi,

2013).

Ablation of SCN eliminates coordinated rhythms of brain and behaviour (Ralph, Foster, Davis,

& Menaker, 1990; Stephan & Zucker, 1972) while SCN transplants restore these outputs with the

periodicity of the donor (Ralph et al., 1990; Sujino et al., 2003). Interestingly, the periodicity of a

skin fibroblast culture will be remarkably similar to the periodicity of the SCN-mediated

behaviours of the donor (Pagani et al., 2010). This is in part due to the conserved core

transcriptional and translational mechanisms that can be measured even at the single-cell level

(Nagoshi et al., 2004; Welsh, Yoo, Liu, Takahashi, & Kay, 2004). The following section will

explain this core mechanism in more detail.

34

1.2.1 The molecular circuitry of the circadian clock

The molecular clock mechanism is best characterized as a transcriptional feedback loop (see Fig

1.1). The positive arm of this feedback loop is composed of the Circadian Locomotor Output

Cycles Kaput (CLOCK) and the Brain and Muscle ARNT-Like 1 (BMAL1; also known as Aryl

Hydrocarbon Receptor Nuclear Translocator Like (ARNTL)) TFs (Lowrey & Takahashi, 2011).

These two proteins form a heterodimer and initiate transcription by binding to DNA elements

known as E-box (5′-CACGTG-3′) and E’-box (5′-CACGTT-3′) in the promoters of target genes

– commonly referred to as clock-controlled genes (Ccg) (Gekakis et al., 1998; Ohno, Onishi, &

Ishida, 2007; Yoo et al., 2005). While these genes are typically protein-coding genes, 43% of

which oscillate in at least one tissue, oscillations of non-coding RNAs has also been detected (R.

Zhang, Lahens, Ballance, Hughes, & Hogenesch, 2014). The set of activated genes includes

those involved in the negative feedback of this loop, namely the period (Per1, 2, and 3) and

cryptochrome (Cry1 and Cry2) genes (Gekakis et al., 1998; Hogenesch, Gu, Jain, & Bradfield,

1998; Kume et al., 1999). PER and CRY dimerize and translocate into the nucleus to inhibit

CLOCK and BMAL1 and subsequently repress their own transcription (Griffin, Staknis, &

Weitz, 1999; T. K. Sato et al., 2006). PER and CRY are subsequently phosphorylated,

ubiquitinated and ultimately degraded by proteasomes so that the cycle can restart again (Eide et

al., 2005; Lamia et al., 2009). There is an additional accessory loop that involves the

upregulation of the nuclear receptors retinoic acid-related orphan receptors (RORs) and REV-

ERBs which compete for the same binding site within the Bmal1 gene in order to activate or

repress its transcription, respectively (Guillaumond, Dardente, Giguere, & Cermakian, 2005;

Preitner et al., 2002). All of this takes around 24 hours to complete.

35

Figure 1.1. The molecular circuitry of the circadian clock. CLOCK:BMAL complex mediate the transcriptional activation of a number of E-box harbouring clock-controlled genes (Ccg) including Per, Cry, Ror, and Rev-erb. PER and CRY dimerize and reverse this process by physically interacting with and inhibiting the activity of CLOCK:BMAL1. The kinases, casein kinase 1ε/δ (CK1ε/δ) mediate phosphorylation and subsequent polyubiquitination. RORs and REV-ERBs compete for the same binding site (ROR response element or RRE) within the Bmal1 gene in an accessory loop. RORs initiate Bmal1 transcription while REV-ERBs inhibit it. Lastly, PER and CRY are phosphorylated, ubiquitinated, and ultimately degraded by proteasomes in order to restart the cycle. Adapted from (J. S. Takahashi, 2015).

36

The downstream clock-controlled genes are quite diverse and tissue-specific. While around 10%

of transcripts oscillate in a given tissue, only 5-10% of these genes are common between any two

tissues (Masri & Sassone-Corsi, 2010). This is thought to be mediated by the presence of tissue-

specific TFs that interact with the core circadian machinery (ibid). The extent of tissue-specific

rhythmicity varies widely from liver (16%), kidney (13%), and lung (12%) to brain, muscle, and

white fat cells which only exhibit circadian oscillations in 3-4% of their transcripts (R. Zhang et

al., 2014). The reduced levels of oscillation in the brain are likely due to the diversity of phases

in the oscillatory networks of the different regions of the brain (Abe et al., 2002; Harbour, Weigl,

Robinson, & Amir, 2013).

The canonical mechanism explained in this section does not capture the entirety of a dynamic

network of oscillating cogs within the cell. For instance, only 22% of mRNA oscillations are due

to nascent transcription while both cycling and non-cycling expressed genes exhibit circadian

occupancy of RNA polymerase II and histone marks (Koike et al., 2012). This indicates that

circadian clocks likely involve both transcriptional and post-transcriptional regulations. In this

connection, post-transcriptional elements such as RNA splicing, RNA methylation,

polyadenylation, and translation have all been implicated in the regulation of circadian clocks

(Fustin et al., 2013; Kojima, Sher-Chen, & Green, 2012; Kojima, Shingle, & Green, 2011).

Nevertheless, transcriptional regulation is an integral part of the circadian clock. It is mediated

through chromatin remodeling and epigenetic factors and the role of these components in the

circadian clock is an area of ongoing research that will be discussed in the following chapter.

37

1.2.2 The epigenetic components of circadian rhythmicity

1.2.2.1 Histone modifications and circadian rhythmicity

The first evidence for the involvement of histone modifications in circadian dynamics was the

reported global increase in the phosphorylation of histone H3 serine 10 (H3S10) in response to a

light-induced phase shift (Crosio, Cermakian, Allis, & Sassone-Corsi, 2000). Numerous studies

have since reported the involvement of other histone modifications in the regulation of the

circadian clockwork. Acetylation, of both histones and non-histones, oscillates with a circadian

periodicity (Etchegaray, Lee, Wade, & Reppert, 2003; Masri et al., 2013). Interestingly, CLOCK

itself possesses intrinsic histone acetyltransferase (HAT) activity, which is enhanced by BMAL1

and can remodel chromatin to mediate expression at promoters of clock-controlled genes (M.

Doi, Hirayama, & Sassone-Corsi, 2006). CLOCK can also acetylate non-histone substrates such

as its own binding partner and this is thought to be critical for subsequent repression by CRY

(Hirayama et al., 2007). Other enzymes with HAT activity that are associated with the circadian

clock include p300, CREB-binding protein (CBP), and p300/CBP-associated factor (PCAF) and

alongside CLOCK they modulate acetylation levels of histone H3 at lysines 4, 9, 14, and 27

(H3K4/K9/K14/K27) (Eckel-Mahan & Sassone-Corsi, 2013). Conversely, there are a number of

HDACs that are implicated in the circadian clock: HDAC3/nuclear receptor corepressor 1

(NCoR1) complex (Alenghat et al., 2008; D. Feng et al., 2011), HDAC1/SIN3A

complex(Duong, Robles, Knutti, & Weitz, 2011), NuRD (J. Y. Kim, Kwak, & Weitz, 2014), and

the NAD+-dependent sirtuin 1 (SIRT1) (Belden & Dunlap, 2008; Nakahata et al., 2008).

Histone methylation has also been implicated in the regulation of the clock network. The H3K27

methyltransferase, enhancer of zeste homolog 2 (EZH2), is required for H3K27me2/3 mediated

38

repression of Per genes (Etchegaray et al., 2006). Conversely, JumonjiC and ARID domain-

containing histone lysine demethylase 1A (JARID1A) has been shown to form a complex with

CLOCK:BMAL1 and enhance the acetylation and transcriptional activation of Per2 and other

clock-controlled genes – although its function appears to be demethylase-independent

(DiTacchio et al., 2011). HP1γ-SUV39H is also recruited to the Per promoters to mediate their

circadian H3K9 methylation and this is essential for the feedback repression loop (Duong &

Weitz, 2014). MLL1- and MLL3-mediated H3K4 methylation is a strongly rhythmic

modification, and along with H3K9ac, is one of the most robust epigenetic markers of the

circadian clock (Aguilar-Arnal, Katada, Orozco-Solis, & Sassone-Corsi, 2015; Katada &

Sassone-Corsi, 2010; Valekunja et al., 2013). Similarly, oscillations in H3K4me1 and

H3K36me3 have also been reported (Vollmers et al., 2012).

Many histone modifications colocalize together and have related functions (Ruthenburg, Li,

Patel, & Allis, 2007; Z. Wang et al., 2008). It is likely that the combinatorial patterns of these

modifications may provide a more informative synopsis of the role of histone modifications in

regulating the core circadian mechanism. In a landmark study, Koike and colleagues explored

the transcriptional and chromatin landscape of the circadian clock by investigating the binding of

core circadian effectors, histone markers, RNA transcription, and DNA polymerase binding in

the mouse liver (Fig 1.2) (Koike et al., 2012). They describe three phases of the circadian cycle:

i) a poised state, ii) a transcriptionally active phase, and iii) a transcriptionally repressed phase.

The poised state is characterized by the binding of CLOCK:BMAL1 complexed with CRY1 to

E-box sites of clock-controlled genes in a transcriptionally silent manner that is associated with

the phosphorylation of RNA polymerase II at serine 5 of the C-terminal domain (RNAPII-

Ser5P). This represents circadian time (CT) 22-4. The peak expression of Clock and Bmal1,

39

alongside the replacement of CRY1 with the transcriptional activator and HAT p300 in the

complex, initiates the active phase that includes the H3K9 acetylation and H3K4 trimethylation

of promoters and H3K4 monomethylation of enhancers at CT 5-12. Nascent transcription,

identified by hypo-phosphorylated RNAPII, occurs at CT14.5 and ends the active phase. The

repression phase is characterized by the peak of Per2 and Cry2 expression followed by H3K4

trimethylation of enhancers and H3K36 trimethylation of gene bodies at CT 16-22.

In addition to all the histone modifications summarized in this section, DNA methylation is

another epigenetic regulator that has been implicated in the circadian clock. These studies are

summarized in the following chapter.

40

Figure 1.2. The landscape of the circadian chromatin. 3 distinct phases characterize the transcription and epigenetic regulation of the circadian clock. The poised phase is characterized the transcriptionally silent binding of CLOCK:BMAL1-CRY1 at clock-controlled genes alongside the inactive RNAPII-Ser5P (CT22-4). The active phase is characterized by the replacement of CRY1 with p300 alongside peak levels of Clock and Bmal1. This is followed by H3K9 acetylation and H3K4 trimethylation of promoters and H3K4 monomethylation of enhancers. Nascent transcription occurs just prior to the start of the repressive phase. Sequencing reads that aligned to introns were used in order to eliminate the effects of post-transcriptional modifications. Repression is characterized by peaks of Per1/2 and Cry2 and is followed by the H3K27 acetylation and H3K4 trimethylation of enhancers as well as H3K36 trimethylation of gene bodies (CT16-22). The peaks are colour-coded and labelled for the oscillating component. The numbers indicate the mean circular phase of peak binding; approximating the mid-point of the observed peak. Figure adapted from (Koike et al., 2012).

41

1.2.2.2 DNA modifications and circadian rhythmicity

Monozygotic twin studies have demonstrated that diurnal preferences, namely “morningness” or

“eveningness”, are heritable but re-programmable traits (Koskenvuo, Hublin, Partinen, Heikkila,

& Kaprio, 2007). Diurnal preferences also change during life, with increasing preference for

evenings during adolescence followed by a switch to a morning chronotype in adulthood

(Randler, 2011). As people grow older there is also an increase in the tendency to go to bed

earlier and wake up earlier (Adan et al., 2012). These observations are potential indicators of an

underlying epigenetic circuitry that is modulating circadian rhythmicity during development and

aging.

DNA modification has been shown to facilitate the adaptation of the internal rhythm in response

to new external challenges. For instance, one night of sleep deprivation can alter DNA

modification patterns of core circadian genes, namely BMAL1, CRY1, and PER1, in adipose

tissue and skeletal muscles (Cedernaes et al., 2015). In a recent study in mice, it was observed

that re-entrainment of mice from a 24-hr rhythm (12 hr light: 12 hr dark) to a 22-hr rhythm (11

hr light: 11 hr dark) is mediated by global changes in promoter DNA methylation levels within

the SCN genome and the inhibition of this process precludes light-based re-entrainment (Azzi et

al., 2014). Additionally, 2 hr of light exposure during the dark phase was associated with the

altered expressions of Dnmt3a, Dnmt3l, Tet1, and Tet3. Interestingly, in older mice where SCN

Dnmt3a levels were reported to decline by as much as 50%, re-entrainment was reported to be

much less effective.

The reduced ability to resynchronize is a well-characterized phenomenon in older mammals

(Davidson, Yamazaki, Arble, Menaker, & Block, 2008; Kolker et al., 2003; von Gall & Weaver,

42

2008). Aging-like circadian rigidity can also be induced in much younger mice using a folate-

deficient diet (Challet et al., 2013). Folate is a potential source of the coenzymes that facilitate

the remethylation of homocysteine to methionine and subsequent synthesis of the methyl-donor,

S-adenosylmethionine (SAM) (Selhub, 1999). SAM is further converted into S-

adenosylhomocysteine (SAH) upon removal of the methyl group in biological reactions like

DNA methylation. Reduced folate intake and the resultant hyper-homocysteinemia are often

associated with DNA hypomethylation, particularly in older people where folate deficiency is

prevalent (Mandaviya, Stolk, & Heil, 2014; Rampersaud, Kauwell, Hutson, Cerda, & Bailey,

2000). Therefore, the folate-induced lack of circadian plasticity may occur in part due to a

compromised DNA methylation pathway.

DNA modification also mediates the association between circadian machinery and cancer. Many

clock-controlled genes are implicated in cancer and PER genes themselves operate as tumor

suppressors (Fu, Pelicano, Liu, Huang, & Lee, 2002; Miki, Matsumoto, Zhao, & Lee, 2013;

Storch et al., 2002). This may be one reason why circadian dysregulation in shift workers is

associated with a higher incidence of cancer (Davis, Mirick, & Stevens, 2001; J. Hansen, 2001;

Schernhammer et al., 2003). Consequently, studies have reported aberrant modification of key

circadian genes in a number of malignancies, including breast cancer (S. T. Chen et al., 2005),

hematologic malignancies (Taniguchi et al., 2009; M. Y. Yang et al., 2006), cervical cancer

(Hsu, Huang, Choo, & Huang, 2007), and endometrial carcinoma (Shih, Yeh, Tang, Chen, &

Chang, 2006; Yeh et al., 2005).

While the preceding examples indicate a likely role for DNA modifications in the plasticity,

developmental trajectory, and oncogenic dysregulation of circadian rhythmicity, the role of DNA

modifications in regulating daily transcriptional oscillations is still poorly understood. The first

43

evidence was reported in the model organism Neurospora crassa. A circadian pattern of DNA

methylation marks the promoter of a key circadian gene in Neurospora, named frequency (frq),

and the depletion of interacting circadian regulators disrupts this pattern (Belden, Lewis, Selker,

Loros, & Dunlap, 2011). Meanwhile, DNA methyltransferase deficient strains display altered

circadian rhythm periods, indicating that DNA methylation is necessary for the maintenance of

rhythmicity in Neurospora (ibid).

Circadian oscillations of Dnmt3a, Dnmt3b, Mecp2, Tet2, Tet3, homocysteine, SAM and SAH

have all been recorded in a number of mammalian tissues (see Table 1.2). Core circadian

proteins CLOCK, BMAL1, PER1/2, and CRY1/2 bind both Dnmt3a and Tet2 in a circadian

manner in liver (Koike et al., 2012). Circadian oscillations were also detected in total genomic 5-

mC content in mouse liver, measured by both High-Performance Liquid Chromatography

(HPLC) and methylation levels of long interspersed nuclear element 1 (LINE1) (Xia et al.,

2015). This oscillation was lost in Per1-/-; Per2-/- double-knockout mice along with the

oscillations of Dnmt3a and 3b. Concurrent with this loss of oscillation was an increase in the

average levels of genomic 5-mC, Dnmt3a and Dnmt3b. Interestingly, the same study also

revealed that Dnmt3a and Dnmt3b are in antiphase with one another, which is corroborated by

another publication (Hughes et al., 2009).

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Table 1.2. List of circadian oscillations in components of DNA modification machinery

Oscillating component

Tissues References

Dnmt3a Liver, heart, adrenal gland

(Hughes et al., 2009; Koike et al., 2012; Tsimakouridze et al., 2012; Xia et al., 2015; J. Yan, Wang, Liu, & Shao, 2008)

Dnmt3b Liver, heart, colon, adrenal gland

(Hoogerwerf et al., 2008; Hughes et al., 2009; Hughes et al., 2012; Koike et al., 2012; Maekawa et al., 2012; Vollmers et al., 2012; Xia et al., 2015; J. Yan et al., 2008; R. Zhang et al., 2014)

Mecp2 Brain, lung, colon (Alvarez-Saavedra et al., 2011; Hoogerwerf et al., 2008; Martinez de Paz et al., 2015)

Tet2 Liver, cerebellum (Xia et al., 2015; R. Zhang et al., 2014)

Tet3 Liver, lung (Hughes et al., 2012; Xia et al., 2015; R. Zhang et al., 2014)

Homocysteine Plasma (Human) (Bonsch et al., 2007; Lavie & Lavie, 2004)

SAM/SAH Liver (Chagoya de Sanchez et al., 1991; Xia et al., 2015)

*This non-exhaustive list was curated manually and with the aid of http://circadb.hogeneschlab.org (Pizarro, Hayer, Lahens, & Hogenesch, 2013). Studies were conducted in mice unless otherwise indicated.

45

There are conflicting reports regarding the existence of genome-wide oscillations in DNA

modifications. In the first genome-wide effort, Vollmers and colleagues failed to find DNA

modification oscillations in mouse liver (Vollmers et al., 2012). Their analysis, however, was

restricted to promoters identified in whole genome bisulfite sequencing of liver samples

collected from only two time points (circadian time 9 and 21) with low sequencing depth (~5×).

This is a rather crude analysis and would only be able to identify, on average, changes of 20% or

more within a select number of regions. The authors concede accordingly, stating “although we

could not conclusively detect oscillations in DNA methylation, circadian changes at specific

nucleotide positions or in response to different metabolic states cannot be ruled out” (Vollmers et

al., 2012). A later study also reported no circadian oscillations within the SCN methylome, but

the scope of their analysis was similarly limited (Azzi et al., 2014). They used the much less

sensitive methylated DNA immunoprecipitation (MeDIP) enrichment technique for the DNA

methylation analysis, and while they do not explicitly indicate the number of time-points

measured, other experiments in their study suggest this may also be limited to only two time

points.

The first evidence for circadian oscillations in DNA modifications in mammals was reported in

2014. In this study, the authors used a sensitive bisulfite-based microarray assay to characterize

modification densities of ~420K CpGs in 738 post- mortem human dorsolateral prefrontal cortex

samples as a function of time of death (Lim et al., 2014). Approximately 5% of the interrogated

CpGs exhibited high amplitudes of oscillation, defined as a peak-to-trough amplitude of ≥10% of

the mean value (Bonferroni-Holm corrected p < 0.002). These CpGs were observed to be

positioned within 2 Kb of 15,091 oscillating genes and preceded transcription by 1-3 hours. This

study benefitted from having measurements from every hour of the day, with a minimum of 50

46

samples per hour. While these are notable results, circadian oscillations explained less than 0.3%

of the total DNA modification variance. Interestingly, the authors also reported a decrease in the

amplitude of oscillation associated with age and dementia. The following section will explore the

association of circadian rhythmicity with age and age-associated diseases in more detail.

47

1.2.3 Circadian rhythmicity and aging

The influence of circadian rhythmicity on aging and lifespan is an extensively studied

phenomenon (Froy, 2011). Changes in phase and decreasing amplitudes of oscillations in

circadian outputs typify the aging process in mammals (Gibson, Williams, & Kriegsfeld, 2009;

Hofman & Swaab, 2006; Yamazaki et al., 2002). In humans, aging is associated with

dysregulated melatonin secretion and body temperature oscillation and older individuals exhibit

earlier sleeping and waking times as well as sleep disturbances (Duffy et al., 2002; Yoon et al.,

2003). Rodents experience changes in body temperature oscillations, activity-wakefulness,

locomotor activity patterns, and drinking behavior as they age (D. Weinert, 2000; Witting,

Mirmiran, Bos, & Swaab, 1994). Moreover, endogenous, entrainment-free, periods of rodents

also change as they age (Mayeda, Hofstetter, & Possidente, 1997; McAuley, Miller, Beck, Nagy,

& Pang, 2002). Lastly, reduced sensitivity to entrainment cues is a characteristic of aged animals

(Davidson et al., 2008; von Gall & Weaver, 2008) and recurrent jet-lag can significantly increase

mortality in older mice (Davidson et al., 2006). Interestingly, transplantation of fetal SCN into

older mice can restore circadian outputs (H. Li & Satinoff, 1998) and extend lifespan (Hurd &

Ralph, 1998).

Significant alterations of clock genes are also associated with aging and lifespan. Clock and

Bmal1 expressions, for example, are reduced in the brains of aged animals (Kolker et al., 2003;

Wyse & Coogan, 2010). Deficiency of both proteins in mice significantly reduces the average

lifespan (by 15% in Clock−/−and nearly 70% in Bmal1−/−) and increases the incidence of early-

onset age-related pathologies (Dubrovsky, Samsa, & Kondratov, 2010; Kondratov, Kondratova,

Gorbacheva, Vykhovanets, & Antoch, 2006). Per2 levels are similarly diminished in the SCN of

older mice and Per knockout mice often exhibit premature aging phenotypes (Lee, 2005; H.

48

Weinert, Weinert, Schurov, Maywood, & Hastings, 2001). Altogether, these studies provide

substantive evidence for the association of aging with circadian dysregulation. Unfortunately, the

mechanisms underlying this association remain unclear (Froy, 2011).

Given the ubiquitous presence of the circadian clock, it is unsurprising that a multitude of

diseases have a circadian component in their etiology (Reddy & O'Neill, 2010). In fact, there are

currently 884 disease-associated genes that are both transcriptionally circadian and potential

targets of commonly sold drugs (R. Zhang et al., 2014). As discussed in the previous section,

circadian components have been identified in cancer (see (Masri, Kinouchi, & Sassone-Corsi,

2015)). Neuropsychiatric disorders also contain circadian elements. Sleep disorders are comorbid

in 30-80% of SCZ patients while ‘sundowning’, defined as an increased tendency for confusion

and agitation in the late afternoon, is a well-known symptom of AD patients (see (Wulff, Gatti,

Wettstein, & Foster, 2010) for an in-depth review).

Aging and the associated loss of physiological integrity and function are the biggest risk factor

for many diseases (Lopez-Otin, Blasco, Partridge, Serrano, & Kroemer, 2013). It has been

suggested that the process of aging is characterized by nine biochemical hallmarks, including

genomic instability, telomere attrition, and epigenetic alterations (ibid). The next chapter will

focus on this epigenetic hallmark of aging.

49

1.3 The aging epigenome

Why do we age? This has been a challenging question for evolutionary biologists over the years.

Numerous theories have been postulated since its initial discussion as a potentially beneficial

trait for the species in 1891 (Kowald & Kirkwood, 2016). Traditional theories propose that age-

associated deleterious alleles often affect fitness post-reproduction and are weakly selected

against while alleles that are beneficial early in life may become detrimental post-reproduction

without an evolutionary penalty (i.e. antagonistic pleiotropy) (Kirkwood & Austad, 2000). At the

molecular level, it is now thought that while aging may be caused by the gradual loss of

molecular fidelity, longevity and lifespan are heritable traits with genetic determinants (K.

Christensen, Johnson, & Vaupel, 2006; Hayflick, 2007). Similar themes of stochasticity and

determinism also define the aging epigenome.

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1.3.1 Epigenetic regression to the mean

Aging is associated with a gradual loss of global modification in mammalian cells (Fuke et al.,

2004; Ono, Shinya, Uehara, & Okada, 1989; Wilson & Jones, 1983). This is mediated by the

gradual loss of modification at both repetitive DNA and single copy genes (Barbot, Dupressoir,

Lazar, & Heidmann, 2002; Ravindran & Ticku, 2005; Z. Zhang, Deng, Lu, & Richardson, 2002).

Centenarians and their offsprings, for instance, display unusually ‘young’ maintenance of higher

global modification levels with age (Gentilini et al., 2013). Age-associated loss of modification

within subtelomeric regions has also been implicated in the maintenance of telomere length and

integrity, a well-known hallmark of aging cells (Gonzalo et al., 2006).

Conversely, CGIs of many developmental, tumor-suppressor genes, and bivalently marked genes

gain modification with age (B. C. Christensen et al., 2009; Rakyan et al., 2010; Teschendorff et

al., 2010). These changes are both widespread and tissues specific (Maegawa et al., 2010).

Consequently, the aging epigenome can be described best as a regression to the mean (i.e. 50%

modified): the heavily modified non-CGI fraction loses modification with age while the CGI

CpGs gain modification with age. This regression to the mean is mediated, in part, by an

imperfectly maintained DNA modification profile (see below). For most of the genome, this

would yield a loss of modification density within the non-CGI CpGs over time. CGIs, on the

other hand, are rarely modified and a lack of maintenance would result in gain of modification

over time. The epigenomes of centenarians, for instance, contain lower levels of DNA

modification in CpG-poor promoters while exhibiting higher levels of modification at CGI

promoters, in comparison to newborns (Heyn et al., 2012). Interestingly, the concurrent state of

global loss of modification and CGI-specific gain of modification is also observed in the

epigenetic changes associated with cancer (Zane, Sharma, & Misteli, 2014).

51

1.3.2 Epigenetic drift

The accuracy of DNMTs is orders of magnitude less than that of DNA polymerases. DNA

replication fidelity is considered to be around 1 mistake in 107–108 bases (Kunkel, 2004),

whereas DNA methylation error rate can be as high as 1 for every 25 methylated sites copied (A.

Bird, 2007). Additionally, de novo methylation is estimated to be around 3-5% per mitosis

(Riggs, Xiong, Wang, & LeBon, 1998). Altered activity of DNMTs has also been reported in

both aging and neoplastic transformations. Activities of DNMT1 and DNMT3A decline, while

DNMT3B increases in activity in senescent and immortalized fibroblasts (Casillas, Lopatina,

Andrews, & Tollefsbol, 2003; Lopatina et al., 2002). Similarly, the age-associated decrease of

Dnmt3a steady-state mRNA levels have been reported in the brain (Azzi et al., 2014; Oliveira,

Hemstedt, & Bading, 2012). This imperfectly maintained mechanism can lead to stochastic, or

random, variation that alongside environmental factors can accumulate epigenetic changes over

time – a process referred to as epigenetic drift (M. J. Jones, Goodman, & Kobor, 2015;

Teschendorff, West, & Beck, 2013).

The distinguishing characteristic of epigenetic drift is the increase of epigenetic divergence with

age. This can be observed in cross-sectional studies of monozygotic twins. While these twins are

genetically identical and epigenetically more similar to each other than dizygotic twins,

epigenetic differences exist and accumulate during their lifetime (Fraga et al., 2005; Kaminsky et

al., 2009). Older twins (>74 years), for instance, show higher variances in their measure of global

DNA methylation than do younger twins (<30 years old, 1.5-fold) and the degree of discordance

between monozygotic twins can increase by 8-16% per decade (Talens et al., 2012).

52

Many of the changes accrued through the aging process can be explained by the stochastic nature

of the methylome. Genetically identical mice, from the same litter and aged in the same

environment, still exhibit stochastic epigenetic differences in a number of developmental genes

(Feinberg, 2014). Similarly, clones of a single cell line can become divergent upon multiple

passages (Humpherys et al., 2001). This is also evident in humans, where centenarians exhibit a

reduced degree of correlation between neighbouring CpGs in comparison to newborns (Heyn et

al., 2012). Simulations demonstrate that predisposition to epigenetic stochasticity can increase

the variance of a phenotype, without alterations of the genotype, and this in turn will increase

evolutionary fitness in a changing environment (Feinberg & Irizarry, 2010).

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1.3.3 Epigenetic clock

In contrast to the stochasticity described in the previous section, certain loci experience age-

associated changes in a more deterministic and predictable manner – so much so that they can

predict age with a much higher accuracy than telomere attrition (Horvath, 2013). These CpGs are

referred to as epigenetic clock sites – but they are not associated with circadian rhythmicity.

While numerous CpGs have been identified as part of this epigenetic clock, 11 of them appear to

be consistently reproducible (M. J. Jones et al., 2015). Eight of these sites are found within CGIs

and gain modification with age, and three are found in CpG island shores and lose modification

with age – consistent with the regression to the mean trend (ibid).

The discordance between chronological age and epigenetic age has emerged as the most exciting

aspect of the epigenetic clock. Such discordances have shown associations with increased frailty

and mortality (Breitling et al., 2016; Marioni et al., 2015), neurodegenerative disorders (Horvath

et al., 2016; Horvath & Ritz, 2015) and longevity (Horvath et al., 2015) amongst an increasing

number of aging related phenotypes.

The extent of these deterministic loci or what properties predispose one locus to stochastic vs.

programmed changes over time are currently unknown. One limitation of the epigenetic clock is

that the associated algorithm is only applicable to one platform that is exclusively used in human

studies. Epigenetic studies rely on a wide range of techniques and there are significant

differences in specificity, sensitivity, and costs associated with each method. These topics will be

discussed in the next section.

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1.4 DNA modification profiling techniques

Molecular techniques investigating DNA modification profiles can be generalized into three

main categories: i) methylation-sensitive restriction enzyme (MRE) methods, ii) antibody

enrichment methods, and iii) bisulfite conversion methods (Laird, 2010; Yong, Hsu, & Chen,

2016). These methods are typically compatible with both single-cells or pooled-cells and the

resultant DNA can be used in a locus-specific or genome wide manner, by means of microarrays

(-chip) or next generation sequencing (NGS) platforms (-seq) (Fig 1.3). This section will mostly

address methods more typical of genome-wide approaches. Each of these methods has intrinsic

strengths and weaknesses and can be evaluated in terms of their resolution, quantitation, and

cost.

55

Figure 1.3. Overview of techniques used in DNA modification studies. There are currently three general approaches: i) methylation-sensitive restriction enzyme (MRE) methods rely on the inherent sensitivity of a multitude of restriction enzymes to DNA modifications in order to generate hypo- or hyper- methylated fragments. ii) antibody enrichment methods use antibodies to immunoprecipitate unmethylated (i.e. mTAG) or methylated (i.e. MeDIP/MBDCap) fragments. iii) bisulfite conversion methods rely on the bisulfite-induced deamination of C/5-caC/5-fC to uracil while 5-mC/5-hmC are protected from this conversion. In order to distinguish between 5-mC and 5-hmC, a parallel assay needs to be performed. In OxBS, pictured above, 5-hmC is chemically oxidized into 5-fC while TAB relies on the enzymatic conversion of 5-mC into 5-caC (5-hmC is protected from this conversion in TAB). A comparison between standard BS (signal = 5-mC + 5-hmC) and oxidation-based BS (signal = 5mC OR 5-hmC) will yield specific densities for both modifications. In all cases the final DNA can be analyzed in a locus specific (i.e. PCR and/or sanger sequencing) or genome-wide manner (i.e. microarray or NGS platforms). C: cytosine, M: 5-mC, H: 5-hmC, X: 5-caC and F: 5-fC. Figure adapted from (Vu et al., 2000; Yong et al., 2016).

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1.4.1 Restriction enzyme- based methods

Restriction enzymes are very powerful research tools owing to their sequence specificity. The

actual biological role of these enzymes is to protect the host (e.g. bacteria and archaea) against

invading viruses by targeting foreign DNA for endonuclease digestion. This is accomplished by

incorporating specific DNA modifications that distinguish host DNA and protect it from

cleavage (Arber & Linn, 1969). Consequently, a wide range of restriction enzymes exist that are

sensitive to methylation within their recognition sequence and they have been used for decades

for DNA methylation studies (Irizarry et al., 2008; Schumacher et al., 2006; van der Ploeg &

Flavell, 1980). These methods rely on the digestion of genomic DNA (gDNA) with MREs to

enrich the unmethylated (or methylated) fraction which are then amplified and interrogated on

microarrays or NGS platforms (D. Li, Zhang, Xing, & Wang, 2015; Schumacher et al., 2006).

MRE-based methods provide a moderate resolution and their analysis is limited by the genomic

distribution of the MRE recognition sequence. They are, however, cost effective and can

interrogate CpGs across a number of genomic elements (Yong et al., 2016).

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1.4.2 Antibody enrichment-based methods

These techniques can be best summarized as modified chromatin immunoprecipitation (ChIP)

methods – similar to what is typically used in histone modification analyses (Soldi, Bremang, &

Bonaldi, 2014). The immunoprecipitation (IP) can be divided based on the targeting of the

methylated or unmethylated fraction. 5-mC/5-hmC specific antibodies (e.g. methylated DNA

immunoprecipitation (MeDIP)) or the use of MBD proteins (e.g. MBDCap) can facilitate the

isolation of the methylated fragments (Brinkman et al., 2010; Taiwo et al., 2012). These are cost-

effective methods that can typically target 5-mC/5-hmC separately and do not introduce any

sequence alterations in the process. They are, however, biased towards hypermethylated regions

and offer poor resolutions (Yong et al., 2016). Conversely, at least one method allows the

isolation of the unmethylome via immunoprecipitation of biotin-labeled unmethylated cytosines

in a process called methyltransferase-directed transfer of activated groups (mTAG) (Kriukiene et

al., 2013; Lukinavicius, Lapinaite, Urbanaviciute, Gerasimaite, & Klimasauskas, 2012). This

method is thought to be more sensitive since the isolation of the smaller, unmethylated fraction

reduces the number of comparisons and can detect more subtle changes (Schumacher et al.,

2006). Nevertheless, it still provides an incomplete picture of the genome, offers moderate

resolution, and can be technically challenging to perform.

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1.4.3 Bisulfite-based methods

Treatment of denatured gDNA with sodium bisulfite (BS) deaminates unmethylated cytosines

into uracil while methylated cytosines are protected (Clark, Harrison, Paul, & Frommer, 1994).

This discovery changed the nature of DNA modification analysis and has undergone numerous

improvements and modifications since its discovery (Laird, 2010). BS conversion can yield

digital (modified/unmodified) densities at a single-nucleotide resolution and is not confounded

by potential unevenness of coverage across the genome. It is typically considered as the most

sensitive and robust measurement of modification densities with the highest resolution. In its

standard application, however, BS conversion cannot distinguish between 5-mC and 5-hmC

modifications as they are both resistant to deamination (Huang et al., 2010). Modified assays that

incorporate chemical oxidation (i.e. oxidative bisulfite (OxBS)) or enzyme-mediated oxidation

(i.e. TET-assisted bisulfite (TAB)) can replace 5-hmC with 5-fC or 5-mC with 5-caC,

respectively (Booth et al., 2013; Yu, Hon, Szulwach, Song, Jin, et al., 2012). These methods

leverage the reactivity of the hydroxy group or the enzymatic activity of TET to generate 5-fC

and 5-caC, which are subsequently converted to uracil during the BS treatment. Two separate

reactions need to be performed and analyzed since a comparison between the standard and

oxidation-based results is required in order to distinguish 5-mC from 5-hmC densities. OxBS

treatment is disadvantaged with its oxidative degradation of gDNA and the need for extended

bisulfite treatments to fully convert 5-fC, while the incomplete enzymatic conversion of 5-mC to

5-caC is a potential source of error in TAB protocols (Song, Yi, & He, 2012; Yu, Hon,

Szulwach, Song, Jin, et al., 2012).

There are important caveats with respect to the broadest application of BS methods, namely

whole-genome bisulfite sequencing (WGBS). Approximately 70-80% of the WGBS reads are

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not informative due to lack of CpGs or alignment difficulties caused by the reduced complexity

of the sequences post conversion (Krueger & Andrews, 2011; Ziller et al., 2013). Consequently,

it is often necessary to sequence BS DNA in great depth to yield informative data, which can be

cost-prohibitive. There are currently three solutions available to address this issue. Reduced

representation bisulfite sequencing (RRBS) utilizes size selection (40-220 bp) of gDNA digested

by a specific methylation-insensitive restriction enzyme prior to BS conversion. RRBS allows

the isolation of approximately 1-3% of the genome containing fragments highly enriched with

CGIs (Meissner et al., 2008). While this method combines the sensitivity of BS methods with the

cost-effectiveness of enzyme-based methods, it suffers from a lack of coverage at intergenic and

regulatory elements, as well being biased by the genomic distribution of the enzyme recognition

sequence.

Illumina’s Infinium HumanMethylation450 BeadChip (450K array) protocol is a widely-used

microarray-based approach that similarly allows selective interrogation of more than 450,000

CpGs from CGIs, CGI shores and CGI shelves of BS-converted gDNA (Bibikova et al., 2011).

While the most recent version of this technology, the Infinium MethylationEPIC BeadChip, has

nearly doubled the number of CpGs to 850,000 and now incorporates many enhancers (Moran,

Arribas, & Esteller, 2016), the method overall is still limited by the sparse coverage of the non-

CGI part of the genome (with nearly 28 million CpGs) and is currently only available for human

samples.

The third solution offers the sensitivity of the bisulfite method alongside a customizable

enrichment of CpGs based on the needs of a given study. Bisulfite padlock probe (BPP) utilizes

custom-designed molecular inversion probes that can target and capture a range of pre-selected

loci of BS-converted gDNA for sequencing on NGS platforms (Deng et al., 2009; Diep et al.,

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2012). This method is highly scalable and the experimenter can rely on informed or arbitrary

preselection criteria to gain a reasonable sampling of the epigenome in a cost-effective manner.

BPP is, however, not commercially available and requires extensive expertise to optimize and

perform.

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1.5 Research Aims This thesis is dedicated to the analysis of DNA modification along three axes: circadian

rhythmicity, aging, and common complex human disease. Circadian rhythmicity appears to

confer an adaptive advantage to those species that display them (Yerushalmi & Green, 2009) and

is ubiquitous in nature, operating at metabolic, transcriptomic, and proteomic levels (E. E. Zhang

& Kay, 2010).

From an epigenetic point of view, a number of histone modifications have emerged as important

cogs of the circadian machinery (J. S. Takahashi, 2017), however, the role of DNA modification

is not clear yet. Key elements of the DNA modification machinery, such as writers and erasers

and 5-mC content, exhibit daily oscillations. Genome-wide DNA modification changes occur in

response to re-entrainment, but very little evidence for robust locus-specific circadian DNA

modification oscillations has been documented. Therefore, the first aim of this thesis was to

explore the occurrence and extent of epigenome-wide circadian oscillations of DNA

modification.

In mammals, old age is associated with weaker circadian regulation and sleep disturbances while

transgenic mice deficient in key circadian genes exhibit diminished lifespans. The molecular

mechanisms of this association are poorly understood. Relatedly, age-associated DNA

modification changes are well characterized. If DNA modification profiles are demonstrably

oscillating in a circadian manner, it is conceivable that incomplete cycling may lead to the type

of gradual accrual of changes observed in aging epigenomes. Therefore, the second aim of this

thesis was to investigate the relationship between DNA modification oscillations and the aging

epigenome. This would unify two different, but related, temporal dimensions of the epigenome:

62

the cyclical changes observed over the course of a day, and the linear accumulation of age-

dependent changes observed over a lifespan (Fig 1.4).

Lastly, epigenetic changes, circadian dysregulation, and age have all been implicated in a

number of complex diseases, primarily cancer. In fact, DNA modification changes associated

with cancer emulate the aging epigenome with global loss of modification punctuated by CGI

gain of modification. Similar to what was postulated for aging, deviations in daily oscillations

can accumulate over time and gradually surpass some critical threshold to generate a diseased

state. Therefore, the third aim of this thesis is to explore the contributions of DNA modification

oscillations to the epigenetic changes identified in large epigenome-wide association studies of

several complex disease, namely leukemia, Alzheimer’s disease, and schizophrenia.

Figure 1.4. Circadian hallmarks of the aging epigenome. Accumulating asymmetric or incomplete oscillations in DNA modification can potentially foster the types of changes observed in aging epigenomes. This schematic drawing depicts how the loss of robustness and incomplete cycling of DNA modification profiles can lead to the accrual of age-associated and disease-associated epigenetic changes over time.

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Chapter 2 Materials and Methods

64

2.1 Samples For animal studies, male non-breeder C57BL/6JRj mice were obtained from Janvier Labs (Le

Genest-Saint-Isle; France) in three different age groups (Table 2.1). Animals were previously

group-housed in a sterile environment but upon arrival were singly housed in polypropylene

cages (44 × 22 × 19 cm) for approximately 7 months. They were given ad libitum access to food,

water, and a 17-cm diameter running wheel mounted in the cage was used to register their

individual physical activity. The animals were entrained to a 24-h light-dark cycle maintained at

12 h light and 12 h dark (LD 12:12), where Zeitgeber time 0 (ZT0) refers to the light onset.

Wheel running activity was monitored using Vitalview (Phillips-Respironics), and circadian

entrainment (i.e., wheel running activity onset) was verified using Actiview (Phillips-

Respironics). At ages 9, 15, or 25 months, a subsample of the mice were euthanized by cervical

dislocation every 2 h over the course of 24 h. Animals from the three cohorts were sacrificed on

the same day. Harvested tissues were snap-frozen in liquid nitrogen and stored at -80°C.

Table 2.1. Sample information for the animal study.

Cohort Age - purchase (weeks)

Entrainment in individual cages (weeks)

Age - death (weeks)

Number of Animals

9-mo 9 30 39 36

15-mo 35 30 65 30

25-mo 78 30 108 30

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For the human neutrophil study, 20 total venous peripheral blood samples were collected every 3

h for 60 h, starting at circadian time (CT; corresponding to the 24 hours in a day) 13, with one

missing time point, from a 52-yr-old Caucasian male. Two 4-mL EDTA Vacutainer tubes were

filled at each collection time. The collected blood was processed immediately if possible, or

otherwise placed on ice and processed within 12 h. The subject typically sleeps from 12AM to

8AM and did not report significant changes to his sleeping habits during the experiment, with the

exception of waking up for blood collection. Neutrophils were isolated from whole blood by

immunomagnetic negative selection with an EasySep Direct Human Neutrophil Isolation Kit

(STEMCELL Technologies, Vancouver, BC, Canada) according to the manufacturer’s protocol.

This negative selection for neutrophils was repeated three times, and the cells were resuspended

in phosphate-buffered saline. The neutrophils were stored at -80°C before DNA extraction.

In order to measure white blood cell oscillations, we used a point-of-care machine, the HemoCue

WBC DIFF System (Ängelholm, Sweden), which allows fast, real-time analysis of WBC

fractions using 10 μL of blood in pre-stained microcuvettes. This machine measures relative and

absolute levels of 5 different WBC fractions: neutrophils, lymphocytes, monocytes, eosinophils

and basophils as well as total WBC count in a few minutes. Measurements from 4 male

individuals were gathered every 3 hours for a minimum of 48 hours (with the exclusion of 3 AM

collections) in their homes following the manufacturer’s recommendations (Table 2.2). For each

time of day, the mean of all measurements for an individual was taken followed by taking the

mean between individuals. These values were fit to a harmonic function (details of fitting

described below).

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Table 2.2. Samples information for the measurement of white blood cell oscillations

Number of days represented at each collection time

Subject Trial # Age (years)

12:00 AM

6:00 AM

9:00 AM

12:00 PM

3:00 PM

6:00 PM

9:00 PM

S1 1 52 5 5 5 5 5 5 5

2 4 4 4 5 5 4 4

S2 1 27 2 2 2 2 2 2 3

S3 1 51 2 2 2 2 2 2 3

S4 1 27 2 2 2 2 2 2 3

2.2 DNA and RNA extraction Frozen mouse tissues were digested in lysis buffer (35 mM EDTA, 75 mM NaCl, 10 mM Tris-

HCl pH 8.0, 1% SDS, and 2 mg/ml Proteinase K) overnight and extracted using the standard

phenol:chloroform method. DNA extraction from the neutrophils was performed with a

NucleoSpin Blood XL (Macherey-Nagel) kit according to the manufacturer’s protocol. Genomic

DNA quality and quantity were examined on a 1% agarose gel, a NanoDrop 2000

Spectrophotometer (Thermo Fisher Scientific), and a Qubit 2.0 fluorometer (Invitrogen).

For RNA studies, frozen mouse tissues were treated with RNAlater-ICE Frozen Tissue

Transition Solution (Ambion), and total RNA was extracted with an RNeasy Mini Kit (Qiagen).

Total RNA was quantified using a NanoDrop 2000 Spectrophotometer (Thermo Fisher

Scientific) and an Agilent 2100 Bioanalyzer with a RNA 6000 Nano Kit (Agilent). The

investigated RNA samples had minimum RNA Integrity Number (RIN) of 7.5.

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2.3 Bisulfite and oxidative bisulfite conversion A total of 750 ng of genomic DNA was bisulfite-converted using an EZ DNA Methylation Kit

(Zymo Research, Irvine, CA, USA) according to the manufacturer’s protocol for the

HumanMethylation450 BeadChip (Illumina), with the following modifications suggested by the

manufacturer for a more stringent conversion: 7.5 µL of M-dilution buffer was used for the

reaction, which was incubated at 42°C for 30 min prior to addition of the CT-Conversion

Reagent. A total of 185 µL of the M-dilution buffer was used in the preparation of the CT-

Conversion Reagent, and only 97.5 µL of the reagent was added per reaction. Oxidative bisulfite

was performed using a TrueMethyl kit (Cambridge Epigenetix, Cambridge, UK) following the

manufacturer’s recommendations.

2.4 Bisulfite padlock probes (BPP)

2.4.1 Probe design

Bisulfite padlock probes were designed using the ppDesigner 2.0 software (Diep et al., 2012).

Briefly, the reference mouse genome (mm10) was masked for genetic variations and repeats

(dbSNP 138, Microsatellites, RepeatMasker, Segmental Dups, Simple Repeats, and

WindowMasker+SDust). For the remaining (non-repetitive) chr 7 sequences, all possible probe

arms were designed and filtered for the presence of at least one CpG and less than 10% of the

masked genomic sequence within the targeted region. Seven base-pair unique molecular

identifiers (UMI; degenerate ‘N’ nucleotides) were added to the common sequence of each probe

immediately adjacent to the probe annealing arms and later used for removal of PCR duplicates

(Fig 3.1). The probes were printed by CustomArray (Bothell, WA, USA).

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2.4.2 Probe preparation

Padlock probes were prepared according to the published protocol with minor modifications

(Diep et al., 2012). Briefly, 1-100 nM of the synthesized probes were amplified with the

StepOnePlus Real-Time PCR System (Thermo Fisher Scientific) using 400 nM of pAP1V61U

(G*G*G TCATATCGGTCACTGTU) and AP2V6 (5Phos-CACGGGTAGTGTGTATCCTG)

primers and 1× KAPA SYBR Fast qPCR mix in four separate 50 µL reactions. The following

cycling conditions were used: 95°C for 30 sec, 15 cycles of 95°C for 10 sec, 55°C for 20 sec, and

70°C for 30 sec, with a final extension of 70°C for 2 min. The amplicons were pooled and

purified using QIAquick PCR purification kit (Qiagen) following the manufacturer’s

recommendation. 0.2 nM of the purified product was used as template for a large-scale

production PCR involving a minimum of four 96-well plate reactions amplified in identical

conditions to the first round. The amplicons were subsequently pooled and concentrated using

standard ethanol precipitation methods. The concentrated amplicons were re-purified using

QIAquick PCR purification kit (Qiagen) following the manufacturer’s recommendation.

Amplification adaptors were removed using three enzymatic digestions. 15-20 µg of the purified

amplicons was mixed with 50 units of lambda exonuclease (New England Biolabs) in a 150 µL

reaction containing 1× lambda exonuclease buffer and incubated for 1 h at 37°C to remove the

bottom strand (top strand contains phosphorothioate bond and is protected from digestion). The

digested amplicons were purified using ssDNA/RNA clean & concentrator kit (Zymo Research,

Irvine, CA, USA). 3-5 µg of the single-stranded probes was then digested with 5 units of USER

(New England Biolabs) in an 80 µL reaction containing 1× DpnII buffer (New England Biolabs)

and incubated for 1 h at 37°C. Next, 5 µL of 100 µM guide oligo (GTGTATCCTGATC), 2 µL

of 10× DpnII buffer and 8 µL of water were added to the mix and the reaction was incubated at

69

94°C for 2 min, followed by 3 min at 37°C. Subsequently, 250 units of DpnII (New England

Biolabs) were added and the reaction was incubated at 37°C for 2 h followed by heat inactivation

at 75°C for 20 min. The probes were then purified using a TBE-Urea denaturing polyacrylamide

gel extraction of the band corresponding to ~117 bp.

Figure 2.1. Schematic drawing of the bisulfite padlock probe approach. A) An example of oligonucleotides generated by ppDesigner and synthesized by CustomArray. The amplification arms (depicted in white boxes) were used for mass production of the probes and were subsequently cleaved and removed during preparation. The ligation arms (depicted in blue boxes) were complementary to the (bisulfite converted) DNA sequences flanking a region of interest. The linker sequence allows the ligation-free preparation of the sequencing library. B) BS treatment of gDNA results in two non-complementary template strands which were targeted by padlock probes. Extension and ligation reactions capture regions of interest in circularized ssDNA while linear DNA is digested using exonucleases. Each circularized sequence contains a unique molecular identifier (UMI, depicted in yellow) that allows the removal of PCR duplicates during analysis. The final library was prepared via amplification of the circularized DNA using sample-barcoded primers. Samples were pooled and sequenced together. Figure adapted from (Diep et al., 2012; Pal et al., 2016).

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2.4.3 Library preparation

Padlock probe library preparation was performed in four steps: probe annealing, extension and

ligation, digestion of linear DNA, and PCR amplification (Fig 2.1 B). 1.5 ng of the purified

probes was mixed with 200 ng of BS or oxidative BS treated genomic DNA (quantified using

Qubit ssDNA Assay Kit) in a 20 µL reaction containing 1× ampligase buffer and covered with

20 μL of mineral oil to prevent evaporation. The reaction was then incubated at 94°C for 30 s

and gradually lowered (-0.5°C /25 sec) to 55°C and incubated for an additional 20 h. Next, a 6.5

µL mixture containing 2.5 µL 10× NAD+ (New England Biolabs), 0.85 µL dNTP (1 mM), 0.85

µL ampligase (Epicentre, Madison, WI, USA), 0.85 µL 10× ampligase buffer, and 1.5 µL of

preheated (95°C for 5 min and placed on ice) PfuTurbo Cx Hotstart DNA Polymerase (Agilent

Genomics) was added to the reaction while it remained on the thermocycler and at 55°C. The

reaction was further incubated for an additional 16-20 h at 55°C for the extension and ligation to

complete. The reaction was then heat inactivated at 94°C for 2 min. The linear, unligated DNA

was digested by the addition of 20 units of exonuclease I and 200 units of exonuclease III

(Epicentre, Madison, WI, USA) to the reaction followed by incubation at 37°C for 2 h and heat

inactivation at 95°C for 5 min. The circularized DNA was amplified by PCR using 5 µL of the

reaction as template in a 50 µL volume containing 200 nM of Amp_F_SE and SE_Amp_IndX

indexing primers (Appendix 1) and 1× KAPA SYBR Fast qPCR master mix using a

StepOnePlus™ Real-Time PCR System (Thermo Fisher Scientific) at the following cycling

conditions: 95°C (30 s); 8 cycles of 95°C (10 s), 58°C (30 s), and 72°C (20 s); 15 cycles of 95°C

(10 s) and 72°C (20 s); and a final extension at 72°C (3 min). The PCR products were purified

using 0.7× volume of AMPure magnetic beads (Beckman Coulter) with 2× 200 µL 70% ethanol

washes, and quantified using a Qubit dsDNA HS assay (Thermo Fisher Scientific). Equal

amounts of each sample were pooled, and the band at ~360 bp was excised and purified using

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standard agarose gel extraction methods. The purified libraries were quantified for sequencing

using KAPA Library Quantification kits.

2.4.4 Preprocessing and analysis of the BPP-seq data

The libraries were sequenced on a HiSeq 2500 platform (Illumina) at 2 × 125 paired-end reads

by using custom sequencing primers (Appendix 1). For each FASTQ file generated, the UMIs

were removed from the start of the reads and added into the unique read ID for later processing.

The FASTQ reads were quality trimmed using Trimmomatic (Bolger, Lohse, & Usadel, 2014)

for trailing bases with a phred score <30 and all reads with post-trimming length <50 bp. The

trimmed reads were then aligned to a masked genome, where all nucleotides in the genome were

masked, except those within a 100-bp window of the known probe locations, using Bismark

v0.14.3 (Krueger & Andrews, 2011) and Bowtie 2 v2.2.2 (Langmead & Salzberg, 2012). The

aligned reads were further filtered to only include reads that had start and end positions matching

the padlock probe annealing arm sequence with no more than one mismatch. The filtered reads

were subsequently PCR de-duplicated using the UMIs. The filtered read pairs were then

converted to modification calls using the Bismark methylation extractor tool (Krueger &

Andrews, 2011).

Individual CpGs were required to have a minimum coverage of 30 reads in each sample for

inclusion in the analysis. Beta values were calculated as the proportion of cytosines and thymines

for a given CpG (beta = M/(M+U), where M = number of cytosines (modified cytosines) and U

= number of thymines (unmodified cytosines)). All samples for a given age and tissue were

internally correlated to identify outliers, and samples with an average inter-sample correlation

value 3 SD from the mean were excluded from further analysis.

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In 9 month-old mouse liver samples, the hydroxymethylation and methylation levels from each

sample were estimated by subtracting the oxidative bisulfite sequencing densities from the

standard bisulfite sequencing densities following the TrueMethyl kit (Cambridge Epigenetix,

Cambridge, UK) recommendations.

2.5 Methylation-sensitive restriction enzyme enrichment (MRE)

2.5.1 MRE-chip protocol

Aliquots of genomic DNA (300 ng for mouse tissue and 100 ng for human neutrophils) were

placed in randomized positions in a PCR plate. Genomic DNA samples were separately digested

with methylation sensitive enzymes HpaII (5′ C↓CGG 3′) or HpyCH4IV (5′ A↓CGT 3′) (New

England Biolabs) in a 15 µL reaction volume at 37°C for 8 h, then heat inactivated at 65°C.

These enzymes will only cleave in the absence of modification on the central CpG. Two

oligonucleotides (CG1a (CGTGGAGACTGACTACCAGAT) and CG1b

(AGTTACATCTGGTAGTCAGTCTCCA)) were mixed in equimolar amounts (100 µM each in

EB buffer) and heated to 80°C for 5 min, then gradually cooled (-1°C/min) to 20°C to create

double-stranded CG1 adaptors with a CG overhang. 7.5 µL from each of the aforementioned

digestion reactions was pooled and added to 1 µL of CG1 adaptor (50 µM), alongside 3 µL of

ATP (10 mM), and 1.5 µL of 10× T4 ligation buffer, to a total reaction volume of 28 µL. The

mixture was then incubated at 45°C for 10 min and was immediately placed on ice. Ten units of

T4 DNA ligase (Thermo Fisher Scientific) were added. Reactions were incubated at 4°C for 16

h, then at 65°C for 20 min, and were gradually cooled (-1°C/10 s) to 20°C. The ligated products

were amplified by PCR in a 100 µL reaction containing 10 µL of ligation reaction, 6.67 µL of

MgCl2 (25 mM), 9.62 µL of 10× Taq buffer with (NH4)2SO4, 5.87 µL of dNTP mix (10 mM),

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1.5 µL of the CG1b primer, and 6.25 units of recombinant Taq DNA polymerase (Thermo Fisher

Scientific). The following cycling conditions were used: 72°C for 5 min, 95°C for 1 min, 15

cycles of 94°C for 40 s and 67°C for 2.5 min, and a final extension of 72°C for 5 min. To

increase the amount of amplicon necessary for the microarray hybridization experiment, a

second PCR was performed using 10 µL of the first PCR in identical conditions except that

1.125 µL of the dNTP/dUTP mix (25 mM, 5:5:5:4:1 ratio of dATP:dCTP:dGTP:dTTP:dUTP)

was used instead of dNTPs. The amplicons were then purified using a QIAquick PCR

Purification Kit (Qiagen). A total of 2 μg of the amplicon was labeled and hybridized on to the

Affymetrix tiling arrays 2.0R (human array A (covering chr 1 and 6) for human neutrophils and

mouse array B (covering chr 2, X, and Y) for mouse liver) and scanned using a GeneChip

Scanner 3000 7G (Affymetrix).

2.5.2 Pre-processing and analysis of MRE-chip

Genomic coordinates of probes were mapped to newer versions of the reference genomes (mm10

for mice and hg19 for human) by using the liftover tool (Kent et al., 2002). Microarrays with

visible artefacts on their scanned image were excluded from further analysis. The signal

intensities of the remaining arrays were log2-transformed and scale-normalized. Each probe was

mapped to a DNA fragment between two restriction enzyme target sites, and all signals within

one fragment were averaged. Fragments less than 200 bp or more than 700 bp in length, as well

as fragments consisting of less than three probes, were removed. Microarrays from different lots

were mean-centered separately by subtracting the lot-specific mean from each fragment.

Microarrays from each lot were inspected for outliers by using an inter-array correlation (IAC)

metric. Samples with an average IAC more than 3 standard deviations (SD) from the mean were

removed.

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2.6 Infinium HumanMethylation450 BeadChip assay Infinium HumanMethylation450 BeadChip assay was performed in technical duplicates using

500 ng of the bisulfite converted neutrophil gDNA at The Centre for Applied Genomics (The

Hospital for Sick Children, Toronto, ON, Canada). Raw data for the 20 neutrophil DNA samples

were processed using the minfi package (Aryee et al., 2014). Quality control using the control

probes showed that all arrays were within recommended BS conversion and hybridization

parameters. Normalization was performed using SWAN (Maksimovic, Gordon, & Oshlack,

2012), and signals from the methylated (modified) and unmethylated (unmodified) channels

were combined to obtain the beta values. Signal intensities were mean-centered by subtraction of

the microarray-specific mean beta value from each sample hybridized on the specific microarray.

The samples were inspected for outliers by using an IAC metric, and samples with a mean IAC

that was more than 3 SD from the mean were removed.

2.7 mRNA analysis of circadian and tissue specific transcripts Quantification of relative expression levels of mRNA were performed using quantitative reverse

transcription PCR (RT-qPCR) on an ViiA 7 Real-Time PCR System (Thermo Fisher Scientific).

First-strand cDNA synthesis was performed with the SuperScript III First-Strand Synthesis

System (Thermo Fisher Scientific) on DNase I-treated RNA according to the manufacturer’s

protocol. Real-time PCR were carried out in triplicates with Power SYBR Green PCR Master

Mix (Thermo Fisher Scientific), with 400 nM primers (Table 2.3) in a total reaction volume of

10 µl, using standard cycling conditions recommended by the manufacturer for 40 cycles.

Glyceraldehyde-3-phosphate dehydrogenase (Gapdh) mRNA levels were used as endogenous

RNA controls. Ct values were normalized to the endogenous genes and then subsequently

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normalized to the lowest expression level in the cohort. Results were analyzed using ViiA 7

software and R (R Development Core Team, 2011).

mRNA of two key circadian genes, Per2 and Arntl, were used to confirm circadian entrainment

of mice. Liver, lung, and macrophage specific mRNA targets were selected using a public

microarray expression dataset from BioGPS (GEO accession #: GSE1133(Su et al., 2004)). The

data were queried for genes whose expression exhibited the highest fold change between the

tissue of interest and other tissue types. The tissue specific genes (hepatocyte, pneumocyte, and

macrophage) were verified for lack of circadian variation using the CircaDB database (Pizarro et

al., 2013). Consequently, albumin (Alb) and apolipoprotein a1 (Apoa1) in hepatocytes,

secretoglobin family 3a member 2 (Scgb3a2) and surfactant protein c (Sftpc) in pneumocytes,

complement c3a receptor 1 (C3ar1) and c-c motif chemokine ligand 2 (Ccl2) in macrophages

were selected.

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Table 2.3. Primer list for the RT-qPCR analysis

Gene Forward Primer Reverse Primer

Gapdh TGCACCACCAACTGCTTAGC GCATGGACTGTGGTCATGAG

Per2 TCACCCCAGCCCTGATGAT ACTGCTCACTACTGCAGCC

Arntl AATGCAAGGGAGGCCCACA CCATCCTTAGCACGGTGAGT

Apoa1 AACAGCTGAACCTGAATCTCCT CCCAATCTGTTTCTTTCTCCAG

Alb TGGATGACTTTGCACAGTTCCT AAGGTTTGGACCCTCAGTCG

Ccl2 GCTGTAGTTTTTGTCACCAAGC GCTGAAGACCTTAGGGCAGA

Sftpc GTCCTTGAGATGAGCATCGG CAGGAGCCGCTGGTAGTC

Scgb CCATCATTTGAGGCTCTTTCAC CTAGACTCCTTAAACTCTGGGA

C3ar1 TGGTTTCTCCAGTGCCCAGT GCCTTTTCTTCCTGTCTACAAAG

Dnmt1 CCAAGCTCCGGACCCTGGATGTGT CGAGGCCGGTAGTAGTCACAGTAG

Dnmt3a GCACCTATGGGCTGCTGCGAAGACG CTGCCTCCAATCACCAGGTCGAATG

Dnmt3b GCCTCACGACAGGAAACAATG TGTCTGAGGACTGGTCACTG

Mecp2 GCTTCTGTAGACCAGCTCCAA ATAATGGAGCGCCGCTGTTT

Tet2 AACCTGGCTACTGTCATTGCTCCA ATGTTCTGCTGGTCTCTGTGGGAA

Tet3 TCCGGATTGAGAAGGTCATC CCAGGCCAGGATCAAGATAA

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2.8 Detection and analysis of oscillating modified cytosines (osc-mCs)

To detect reliable DNA modification signals, each mouse DNA biological sample was tested in

triplicate (three technical replicates) and the human neutrophil tested in duplicate (two technical

replicates). For each tissue and age group, technically consistent and epigenetically variable

cytosines were identified by comparing their technical and biological variation using a one-way

analysis of variance (ANOVA) between the biological samples. Only cytosines with p < 0.05

were considered for further analysis. After this step, all the technical replicates were averaged

according to the medians.

A linear model was used to identify circadian oscillations. The period was fixed to 24 h, and the

phase and amplitude were added as a linear combination of sine and cosine terms as:

where y is the observed modification level, β, a, b are regression coefficients, ZT is the time of

observation, and ε is the error term. The sine and cosine terms can be written as:

Peak-to-peak amplitude (A):

Acrophase (φ):

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P-values were obtained by comparing this model to the null intercept-only model by using an F

test. For the HumanMethylation450 BeadChip experiment, in order to consider the Sentrix

position effect, an additional covariate representing the BeadChip row was added to both the

inspected model and the null model.

To determine whether real circadian time explained more variance than randomized time, 10,000

permutations were performed by shuffling ZT times, and the mean R2 value was calculated

across all cytosines for each permutation. The p-value was derived as a fraction of permutations

with the permuted mean R2 value greater than the observed.

All odds ratio p-values were calculated using a two-sided Fisher’s exact test. All analyses were

performed in R (R Development Core Team, 2011). Principal components were calculated by a

singular value decomposition of the mean centered data matrix (function ‘prcomp’ in R). The

resulting component scores were inspected for oscillations by fitting a harmonic regression

model described above. and scores fit to a harmonic model using the method described above.

2.9 Analysis of the transcriptomic datasets A public liver and lung circadian transcriptomic microarray dataset (GEO accession:

GSE54650(R. Zhang et al., 2014)) and aging transcriptomic datasets (GEO accession:

GSE57809(Bochkis, Przybylski, Chen, & Regev, 2014) and GSE6591(Misra et al., 2007) for

liver and lung, respectively) were used for detection of circadian oscillations (see “Detection and

analysis of circadian epigenetic oscillations”) and nominally significant aging mRNA (see

“Aging and motif analyses for the mouse tissue and human neutrophil DNA samples”). Datasets

were matched by RefSeq/Ensembl gene ID or gene symbol. Gene Ontology (GO) enrichment

analysis on the public transcriptomic dataset was performed using GREAT 3.0(McLean et al.,

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2010). A hypergeometric test was performed using oscillating and aging transcripts as

foreground and all candidate transcripts as background. The significance threshold was set as

FDR q < 0.05.

2.10 Oscillating cytosine modification and mRNA phase shift analysis

All mRNA transcripts were paired together with the mean DNA modification of all

epigenetically variable cytosines within the gene body. Each transcript’s mRNA acrophase

estimate was used to interpolate values to cross-correlate with mean modification. These values

were shifted in 1hr intervals to obtain a distribution of cross-correlations for each phase shift and

the mean taken as the correlation estimate for that phase shift across all sites. Permutation

analysis (N = 10,000) was performed by shuffling mRNA and DNA modification ZTs and

generating random pairings of transcripts with mean modification followed by repeating the

cross-correlation procedure. The mean cross-correlations of permutations were used to generate a

null distribution for each phase shift and an empirical p-value was calculated as the fraction of

permutations greater than or equal to the real mean cross-correlation at that phase shift (corrected

for multiple testing using Bonferroni correction). The phase shift with the lowest Bonferroni

corrected p-value was reported.

2.11 Aging and motif analyses For the mouse data, cytosines which were epigenetically variable in all age groups were

considered for aging effects. Cytosines exhibiting age-dependent modification across three age

groups (9 mo, 15 mo, and 25 mo) were identified using an F-test between an intercept model and

a linear model using age as a predictor. Cytosines whose modification showed a significant

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correlation with age (Bonferroni corrected p < 0.05) were called aging-correlated cytosines, and

a linear model beta was used to determine the direction of change.

Sequence motifs were examined at the oscillating cytosine position ± 100 bp. Overlapping 200

bp regions (i.e., redundant sequences) were merged into one sequence. MEME suite(Bailey et

al., 2009) was used to identify overrepresented sequences using the following parameters: -dna, -

mod anr, -maxsites 1000, -nmotifs 20, -evt 1e-10, -revcomp, -maxsize 10000000.

2.12 Analysis of public DNA modification datasets For the neutrophil data, a public dataset of whole blood samples interrogated on

HumanMethylation450 BeadChip (N = 656; GEO accession: GSE40279(Hannum et al., 2013))

was used for the aging analysis. White blood cell count estimates were obtained using a DNA

methylation age calculator dnamage.genetics.ucla.edu (Horvath, 2013). Age-dependent cytosine

modifications were identified using a linear model fit with CD8+ T-cell, CD4+ T-cell, NK cell,

B-cell, monocyte, and granulocyte estimates as covariates.

Normalized beta values from two public HumanMethylation450 BeadChip methylation datasets,

the Human Aging (Hannum et al., 2013) (GEO accession: GSE40279) and schizophrenia EWAS

(Hannon, Spiers, et al., 2016) (GEO accession: GSE80417; healthy controls only), were used to

estimate stochastic epigenomic variation. Probes from sex chromosomes and probes with a SNP

in or within 10 bp of the target CpG site, as well as known methylation quantitative trait locus

probes (Gaunt et al., 2016), were removed. Blood cellular composition estimates were obtained

using dnamage.genetics.ucla.edu (Horvath, 2013). Each probe was fitted with a linear model that

included all available technical and biological information as covariates: sentrix ID, row, age,

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sex, and white cell composition. For the schizophrenia dataset, smoking status was included as

an additional covariate.

For the acute myeloid leukemia dataset (von der Heide et al., 2016) (GEO accession:

GSE79695), raw data were obtained from GEO and processed using the minfi package and

SWAN normalization (Maksimovic et al., 2012). Probes containing a SNP in or within 10 bp of

the target CpG were removed. Eight samples that deviated by more than two standard deviations

from the mean of any of the first three PCs were considered to be outliers and removed. Limma

(Ritchie et al., 2015) was used to identify significantly differentially modified probes (p < 0.05

used as threshold).

Chronic lymphocytic leukemia specific probe IDs were obtained from Supplementary Tables 5,

6 and 7 of a previous study (Kulis et al., 2012). The schizophrenia EWAS significant probes

were obtained from supplementary tables of published manuscripts (Hannon, Dempster, et al.,

2016; Montano et al., 2016). Alzheimer’s disease-associated CpGs were obtained from

Supplementary Table 2 of a published manuscript (De Jager et al., 2014). The brain osc-mCs

were defined as the top 10% of probes with the highest ‘proportion of variance explained’ values

in a previous study (Lim et al., 2014).

2.13 Analysis of association between circadian, aging, disease and variable CpGs

Associations between osc-mCs and aging or disease CpGs were estimated using two-sided

Fisher’s exact test. Only epigenetically variable cytosines were used to compute the contingency

table except for association with disease CpGs where both epigenetically variable cytosines as

well as all interrogated CpGs were used as reference sets in two separate tests. Association of

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osc-mC and variability was modeled using generalized linear model with binomial distribution of

the response variable (osc-mC status).

2.14 Ethics approval All experiments were approved by the Centre for Addiction and Mental Health Research Ethics

Board (protocols 030/2014-01 and 567), the University of Toronto Animal Care Committee

(protocol 20010315), and performed in accordance with relevant guidelines and regulations.

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Chapter 3 Results

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3.1 Circadian oscillation of DNA modification profiles

3.1.1 Oscillating modified cytosines in the mouse liver and lung

To investigate circadian oscillations of cytosine modification (sum of 5-mC and 5-hmC; modC),

we harvested tissues from 9-, 15-, and 25- months old (mo) mice (n = 36, 30, and 30,

respectively) in a circadian manner. All mice were individually housed, with access to food ad

libitum, and entrained on a 12 h light:12 h dark cycle, where Zeitgeber time (ZT) 0 is light onset

and ZT12 is light offset. Entrainment of mice was verified using locomotive activity where

average wheel-running activity was measured in number of turns per 6 minute intervals (Fig

3.1a-d). Harmonic regressions with varying periodicities (1 h – 48 h) were used to model the

mean wheel-running activity in the three age groups and 24 hours was observed to have the best

fit (Fig 3.1e). Expectedly, the oldest mice displayed less robust activity in both the actograms

and the spectral analysis.

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Figure 3.1. Locomotor activity verifying circadian entrainment of mice. a-c) Representative actograms of individual mice from (a) 9-mo, (b) 15-mo, and (c) 25-mo cohorts. The colored bars represent wheel running activity over 30 days. The light and grey columns show periods of lights on and off. By convention, each line depicts 2 days of activity, with the second day appearing again in the line below (i.e. double-plotted). d) The summarized mean activity of each age cohort, measured by the mean number of turns per 6 minute intervals, as a function of ZT. e) Spectral analysis of the actograms in each age cohort showing the R2 of harmonic fits at varying periodicities (1 hour intervals).

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Liver and lung tissues, which exhibit robust transcriptional oscillations and have been frequently

used in circadian studies (R. Zhang et al., 2014), were collected every 2 hours for a total of 72

hours (3 days) in the 9 mo cohort and a total of 54 hours (2.5 days) in the 15- and 25- mo

cohorts. This sampling approach equated to 3 biological replicates in the 9 mo and a minimum

of 2 biological replicates in the older cohorts within a 24-hour time-frame. In order to gain

precise approximations of oscillating modified cytosines (osc-mCs), we targeted chromosome 7,

which is relatively small and gene-dense (Mayer et al., 2005). We performed mapping of osc-mC

at the single nucleotide resolution using 10,696 bisulfite padlock probes (BPP-seq), each

targeting a unique 130-140 bp region, and sequenced to an average mapped read depth of

~4000x per sample (Fig 3.2h). All experiments were performed in technical triplicates, where

three aliquots of gDNA from each tissue collection were subjected to separate bisulfite

conversion and padlock probe reactions independently of each other. All sequencing experiments

showed similar DNA modification density distributions (Fig 3.2a-g) and a similar distribution of

interrogated cytosines across different genomic elements (Fig 3.2i). To ensure the accuracy and

sensitivity of our approach, we included 187 padlock probes targeting the differentially

methylated regions of imprinted genes in our library preparation. An accurate representation of

the epigenome can be inferred if these regions exhibit ~50% modification density – which was

observed in our dataset (Fig 3.2j). In total 37,306 CpGs were targeted by the padlock probes,

32,788 CpGs were sufficiently covered in at least one experiment, and 20,661 CpGs achieved

sufficient coverage (>30 ×) across all padlock probe experiments (Appendix 2). DNA

modification densities were subsequently filtered for sites that showed greater biological than

technical variation in cytosine modification at CpG dinucleotides using a one-way ANOVA.

These CpGs are henceforth referred to as epigenetically variable cytosines (Fig 3.3).

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Figure 3.2. Bisulfite and oxidative bisulfite padlock sequencing of mouse chromosome 7. the DNA modification density plot for liver tissue from 9-mo (a), 15-mo (b), and 25-mo (c) mice, as well as for lung tissue from 9-mo (d), 15-mo (e), and 25-mo (f) mice. g) 5-mC density plot from oxidative bisulfite sequencing of liver tissue from 9-mo mice. h) Density of the read depth of interrogated cytosines in each age or tissue group; the mean depth was more than 1,000 reads. i) Distribution of the interrogated cytosines across genomic elements. j) Density of the mean DNA modification stratified by genomic elements. modC, modified cytosines; mC, methylcytosine; mo, month-old. All density plots smoothed using Gaussian kernel density estimation with default bandwidth.

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Figure 3.3. Experimental workflow summary for the mouse experiments. For the mouse experiments, we investigated liver and lung samples harvested every 2 h from 9-, 15-, and 25-month old (mo) mice entrained on a 12 h light:12 h dark cycle. All sequencing experiments were performed in technical triplicates, and the cytosine modification densities were filtered for sites that showed a greater degree of biological variation than technical variation in CpG modification using ANOVA, which we refer to as epigenetically variable cytosines. 5-hydroxymethylcytosine densities in 9-mo livers were estimated by subtracting 5-mC (OxBS data) from modC densities (BS data) on CpG sites that are intersected across epigenetically variable cytosines. Aging and oscillating cytosines were identified using linear and harmonic regression, respectively, on epigenetically variable cytosines.

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A chromosome-wide analysis of our data revealed a wave-like, synchronized pattern of

oscillation (Fig 3.4). In the liver and lung, chromosome 7 was divided into 150 × 1 Mb tiles and

the mean of the epigenetically variable cytosine modification densities for each tile was

calculated and then scaled to a range of 0-1 across the 24 hours, where 0 represents the minimum

and 1 represents the maximum of modification densities. Visual inspection of the lung heatmap

revealed most tiles to be at their maximum during the first 12 hours and to reach their nadir in

the second half of the day (Fig 3.4b). A more scattered pattern, representing peak modifications

in both parts of the day, was observed in the liver (Fig 3.4a). To identify temporal patterns of

oscillation across the chromosome, the average modification densities across all epigenetically

variable cytosines for each ZT were fitted to a harmonic regression model and p-values were

calculated by comparison of this regression to an intercept only model using an F test (see

methods). Chromosome-wide average modification was observed to oscillate in the lung

(harmonic regression p = 2.9 × 10-7) but not in the liver (harmonic regression p = 0.74).

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Figure 3.4. Chromosome-wide analysis of circadian oscillations in DNA modification. a-b) A heatmap of the mean modification densities of epigenetically variable cytosines across chromosome 7 of (a) the liver and (b) the lung, normalized to a range of 0-1 in each 1 Mb bin. The horizontal bar plots display the number of epigenetically variable cytosines in each bin (bins with no epigenetically variable cytosines appear black in the heatmap) and the plots in the bottom panel display the chromosome-wide mean of cytosine modification as a function of ZT, fitted using the harmonic regression model, and shading around the regression lines represents the 95% confidence band. All data were double-plotted to aid with visualization.

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To identify temporal patterns of oscillation for individual epigenetically variable cytosines, the

methylation densities for each CpG were fitted to the previously described harmonic regression

model. Visual inspection of the data revealed that some sites were clearly rhythmic while others

were not (Fig 3.5 and 3.6). To determine whether this apparent rhythmic variation was due to

chance, 10,000 permutations were performed wherein the ZT labels were shuffled and the mean

R2 value of the harmonic regression was subsequently calculated across all epigenetically

variable cytosines. An empirical p-value was derived as the fraction of permutations that

produced a higher average R2 value than the observed. This permutation procedure is a robust

alternative to the Bonferroni method of correcting for multiple testing when faced with

potentially dependent tests (Sham & Purcell, 2014). In the 9-mo mice, we found osc-mCs in

8.2% (permuted p = 0.046) and 35.6% (permuted p < 10-4) of epigenetically variable cytosines in

the liver and in the lung, respectively (Fig 3.7a-b and e-f). In the lung, the oscillating first

principal component (harmonic regression p = 1.8 × 10-6) explained 25% of the DNA

modification variance (Fig 3.7c), while in the liver, principal components 2 and 3 were found to

be oscillating (harmonic regression p = 0.028 and 0.015, respectively) and cumulatively

explained 13% of the variance (Fig 3.7g). To explore the peak-time, or acrophase, distribution of

osc-mCs, we measured the number of CpGs that were significantly oscillating (harmonic

regression p<0.05) for a given acrophase time (Fig 3.7d and h). Lung osc-mCs were observed to

predominantly peak during the first part of the day while liver acrophases were more evenly

represented in both parts of the day - consistent with the chromosome-wide patterns (Fig 3.4) and

the oscillation patterns of principal components. The mean amplitudes of osc-mCs were 3.2 ±

1.8% (mean ± SD) and 4.4 ± 2.2%, with maximum amplitudes of 14% and 17%, in the liver and

lung, respectively. We observed a significant overlap of osc-mCs between the liver and lung

tissues (odds ratio (OR) [95% confidence interval (CI)] = 2.0 [1.7-2.4]; p = 1.2 × 10-20). These

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osc-mCs displayed a significant degree of synchrony in their acrophases, with 93% (396 out of

425) of the CpGs common to both tissues peaking in the same light phase (Fig 3.8).

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Figure 3.5. Representative oscillating and non-oscillating CpGs in the 9-mo mouse liver detected by padlock probes. a) The top 4 sites (ranked based on p-values) displaying oscillations of cytosine modification over the course of 24 hours, double-plotted for clarity. Each represents the mean of 3 biological replicates (each represented by the median of its technical replicates) and error bars represent standard error of the mean (SEM). The coordinates for each CpG is indicated on the top left of the plot while the p-value is indicated in the top-right corner of the plot. b) The bottom 4 non-oscillating CpGs. X-axis: ZT (h); Y-axis: cytosine modification (indicated as % modC).

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Figure 3.6. Representative oscillating and non-oscillating CpGs in the 9-mo mouse lung detected by padlock probes. a) The top 4 sites (ranked based on p-values) displaying oscillations of cytosine modification over the course of 24 hours, double-plotted for clarity. Each represents the mean of 3 biological replicates (each represented by the median of its technical replicates) and error bars represent SEM. The coordinates for each CpG is indicated on the top left of the plot while the p-value is indicated in the top-right corner of the plot. b) The bottom 4 non-oscillating CpGs. X-axis: ZT (h); Y-axis: cytosine modification (indicated as % modC).

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Figure 3.7. osc-mC properties of liver and lung DNA from 9-month-old mice. a) P-value histogram of harmonic regression fits of the liver, showing enrichment (8.2%; 983 of 11,941 epigenetically variable cytosines). b) 10,000 permutations of ZT labels, showing that the average proportion of variance explained by osc-mCs in the liver is significant. c) Harmonic regression fits on the first 4 principal components (PCs) and the variance explained by each PC in the liver. PCs with harmonic regression p < 0.05 are depicted in red. d) Acrophase rose plot showing enrichment of osc-mCs in the liver. Values indicate the number of CpGs represented at each height. e) p-value histogram of harmonic regression fits of the lung, showing osc-mC enrichment (35.6%; 5,054 of 14,199 epigenetically variable cytosines), f) permutation, g) PC analysis, h) Acrophase rose plot of the lung.

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Figure 3.8. Acrophase synchrony of osc-mCs common to both lung and liver. a) Venn diagram displaying the degree of overlap between osc-mCs in the 9 mo mouse liver and lung. The analysis was performed on the epigenetically variable cytosines that were common in both datasets. b) Scatter plot of acrophase times for the 425 osc-mCs common to both liver and lung. 93% of these osc-mCs peaked within the same light or dark phase (i.e. lights-on/lights-off). c) The absolute acrophase difference between common liver and lung osc-mCs. The red line represents the median absolute acrophase difference. d) The distribution of expected median absolute acrophase differences obtained by permuting pairs of common osc-mCs in the liver and lung. The red line represents the observed value depicted in (c).

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Next, we observed a decreasing proportion of osc-mCs in the older animals. Compared to the 9-

mo cohort, lung osc-mCs were reduced slightly in 15-mo mice (28.1%, permuted p = 4.4 × 10-3)

but dropped more dramatically in 25-mo mice (13.9%, permuted p = 4.8 × 10-3) (Fig 3.9c-h).

Acrophase distributions for both groups mirrored the same trend as 9 mo mice, namely that osc-

mCs predominantly peaked during the first half of the day with a slight forward shift (Fig 3.9i-j).

Liver samples displayed diminished and statistically insignificant oscillations in both 15-mo

mice (3.3%, permuted p = 0.84) and 25-mo mice (6.8%, permuted p = 0.15). Since only tissues

from 9-mo mice showed consistent oscillations, we focused primarily on this group for our

subsequent circadian analyses.

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Figure 3.9. Characterization of oscillating modified cytosines in the 15-mo and 25-mo mouse liver and lung. a-d) Histogram of harmonic regression p-values with the proportion of oscillating modified cytosines (p < 0.05) in (a) the liver 15-mo and (b) 25-mo, and (c) the lung 15-mo and (d) 25-mo. e-h) Average proportion of variance explained (R2) by the harmonic regression fits across all investigated cytosines in each of 10,000 permutations of ZT labels. The red line depicts the observed average R2 in (e) the liver 15-mo and (f) 25-mo, and (g) the lung 15-mo and (h) 25-mo. i-j) Acrophase rose plot showing modification peak times of osc-mCs in (i) the lung 15-mo and (j) 25-mo. Only epigenetically variable cytosines were investigated in (a-j). ZT, Zeitgeber time; osc-mCs, oscillating cytosine modifications; mo, month-old.

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3.1.2 Oscillations of DNA modification involve both methylation and demethylation

Mechanistically, genuine osc-mCs should involve demethylation – likely by TET-mediated

oxidation of 5-mC to 5-hmC. Combining a separate oxidative bisulfite experiment with our

previously described regular bisulfite sequencing, we were able to distinguish between 5-mC and

5-hmC densities. 5-hmC is initially converted into 5-fC in the oxidation step and is subsequently

converted to uracil alongside unmodified cytosines during the bisulfite conversion. 5-mC

remains unchanged, as it lacks the reactive hydroxy group targeted by the oxidative reaction, and

theoretically becomes the sole source of cytosine calls during sequencing. 5-hmC densities are

then generated through the comparison and subtraction of signal densities in the oxidative

bisulfite experiment from the regular bisulfite experiment. We detected significant oscillations

for both modifications (5-hmC: 8.5%, permuted p = 0.019; 5-mC: 7.5%, permuted p = 9.7 × 10-3,

respectively; Fig 3.10a-d) in the 9 mo mouse liver. Circadian 5-hmC overlapped with circadian

5-mC significantly (OR = 3.6 [2.7–4.7]; p = 2.9 × 10-16), and within overlapping regions their

respective acrophases were in antiphase with one another (11.2 h median absolute acrophase

difference between the 5-mC to the 5-hmC acrophases; permuted p < 1 × 10-4; Fig 3.10e-f), thus

indicating coordinated timing of DNA demethylation and remethylation.

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Figure 3.10. osc-mC properties of 5-methyl- and 5-hydroxymethylcytosines (5-mC and 5-hmC) in 9-mo mouse liver tissue. a) P-value histogram of harmonic regression fits for 5-mC, showing enrichment (7.5%; 443 of 5,883 epigenetically variable cytosines). b) 10,000 permutations of ZT labels, showing that the average proportion of the variance explained by 5-mC oscillation is significant. c) P-value histogram (8.5%; 375 of 4,403 epigenetically variable cytosines) and d) permutation analysis of 5-hmC. e) The absolute acrophase difference between commonly oscillating mC and hmC in the liver. The red line represents the median absolute acrophase difference of 11.2 h. f) The distribution of the expected median absolute acrophase differences obtained by permuting acrophase pairs of common oscillating mCs and hmCs in the liver. The red line represents the observed value depicted previously in (e).

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3.1.3 Circadian variation of DNA modification is not simulated by the oscillatory influx of white blood cells

Circadian oscillations of cytosine modifications may be confounded by cyclic influx of white

blood cells into solid tissues (Haspel et al., 2014; Scheiermann, Kunisaki, & Frenette, 2013). In

such cases, changes in the proportions of cells may simulate osc-mCs due to the contrasting

epigenomes of penetrating blood cells and native tissue cells. We addressed this issue by testing

non-circadian, cell-specific mRNAs for oscillations in the lungs and livers of our 9-mo (N=36),

15-mo (N=30), and 25-mo (N=30) mice. Evidence of circadian oscillations of such mRNAs

would indicate changes in cell counts. Liver, lung, and macrophage specific mRNA targets were

selected using a public microarray expression dataset from BioGPS (Su et al., 2004). The data

were queried for genes whose expression exhibited the highest fold change between the tissue of

interest and other tissue types. The tissue specific genes (hepatocyte, pneumocyte, and

macrophage) were verified for lack of circadian variation using the CircaDB database (Pizarro et

al., 2013). The mRNAs levels of two key circadian genes, Per2 and Arntl, were measured using

RT-qPCR to establish circadian rhythmicity at the molecular level (Fig 3.11b). We subsequently

investigated the following non-circadian mRNAs: I) albumin (Alb) and apolipoprotein a1

(Apoa1) representing hepatocytes; II) secretoglobin family 3a member 2 (Scgb3a2) and

surfactant protein c (Sftpc) representing pneumocytes; and III) complement c3a receptor 1

(C3ar1); and c-c motif chemokine ligand 2 (Ccl2) representing macrophages. None of these cell-

specific transcripts showed significant circadian oscillations (Fig 3.11a) arguing that osc-mC

were unlikely to have been simulated by cell-type complexities in the tissue.

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Figure 3.11: RT-qPCR analysis of non-oscillating tissue-specific mRNA and oscillating positive control mRNA in mouse liver and lung tissues: a) The figure displays Bonferroni-corrected p-values plotted across the acrophase time for the harmonic regression fitting of each mRNA with the line marking the p = 0.05 significance threshold. Hepatocyte- and macrophage-specific mRNA transcripts in the liver did not show significant circadian oscillation across the age cohorts. The same applied to the pneumocyte- and macrophage-specific transcripts in the lung. b) mRNA of two antiphasic circadian genes, Arntl and Per2, were used as positive controls and exhibited strong oscillations in both tissues and all three age cohorts. Shaded area represents the 95% confidence interval.

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3.1.4 Steady state mRNA of DNA modification enzymes display tissue-specific and circadian patterns of expression

Next, we explored the expression patterns of the genes encoding enzymes and proteins involved

in the DNA methylation and demethylation pathways in order to explain the differences

observed between the two tissues in their proportions of osc-mCs (Fig 3.12). Dnmt3a, Dnmt3b,

Mecp2, and Tet3 displayed significant circadian oscillations (Bonferroni corrected p < 0.05) in

the mouse liver but not in the mouse lung, where no oscillations were detected. Dnmt1 and Tet2

did not show oscillations in either tissue. Interestingly, Dnmt3a and Dnmt3b oscillated in

antiphase, with each displaying peak levels of expression in between the two clusters of liver

acrophases. Dnmt3b peaks around the start of the day (ZT0) while Dnmt3b peaks around mid-

day (ZT8). This pattern was not observed in the lung, which also displays one predominant peak

of osc-mC acrophases. Consequently, variations in the expression patterns of

methylation/demethylation enzymes may explain the observed bimodal distribution of osc-mC

acrophases.

A note of caution regarding normalization of steady state mRNA levels relative to housekeeping

genes is required here. Protein products of housekeeping genes, such as Gapdh used here, are

linked to metabolic activity of the cell and are frequently assumed to be stable in terms of their

steady state mRNA levels. Even housekeeping genes may, however, be subjected to circadian

oscillations, which may simulate (false positives) or eliminate (false negatives) circadian mRNA

expressions during the normalization process. In this connection, Gapdh was previously ruled

out as a circadian transcript (Hughes et al., 2009), but a reanalysis of this same dataset using a

different algorithm identified Gapdh as a circadian transcript with a low amplitude of oscillation

and a 27-hour periodicity (Hughes, Hogenesch, & Kornacker, 2010). While we cannot entirely

rule out the potential confounding effects of this oscillation in our analysis, particularly with

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respect to low-amplitude oscillators, it remains unlikely that systematic effects could have been

simulated given misaligned 27-hour periodicity of Gapdh. This underscores the necessity of

using multiple housekeeping genes from different cellular pathways in future circadian mRNA

analyses.

Figure 3.12. RT-qPCR analysis of mRNAs of enzymes and proteins involved in the demethylation- remethylation pathway in the 9-mo mouse liver and lung. a) Dnmt1, Dnmt3a, Dnmt3b, Mecp2, Tet2, and Tet3 mRNA levels were measured and oscillation was detected using harmonic regression. All expression data were normalized to the mRNA of a housekeeping gene Gapdh. Each dot represents the average expression for a biological replicate. Shaded areas represent the 95% confidence band (n = 3 for each time point). b-c) Bonferroni-corrected p-values plotted across the acrophase time for the harmonic regression fitting of each mRNA with the dotted line marking the p = 0.05 significance threshold in the liver (b) and lung (c), respectively.

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3.1.5 osc-mCs are overrepresented in highly-expressed genes, genes encoding circadian mRNA transcripts, and E-box motifs

To test for a functional association of osc-mCs with mature mRNA levels, we compared tissue

matched public circadian transcriptomic datasets (R. Zhang et al., 2014) with our cytosine

modification oscillations. The public dataset includes transcriptional data from different tissues

of 7-week-old C57/BL6 male mice that were euthanized in triplicates every 2 hours for 48 hours.

The robustness of osc-mCs for a given gene was summarized as the median R2 of the harmonic

regression for all epigenetically variable cytosines within that gene and weighted by the number

of measured cytosines in the gene. Mature mRNA levels positively correlated with this

robustness of cytosine modification oscillation in liver and lung tissues (weighted Pearson’s r =

0.14 and 0.19; p = 4.1 × 10-6 and 1.4 × 10-10, respectively), suggesting that genes with more

robust osc-mCs tend to be highly expressed. Furthermore, circadian oscillations of mRNA,

quantified as the R2 of the harmonic regression, correlated with robustness of cytosine

modification oscillation (weighted Pearson’s r = 0.075 and 0.19; p = 0.015 and 4.3 × 10-10, for

liver and lung, respectively), indicating that circadian transcripts and osc-mCs oscillate in a

coordinated fashion.

Next, we examined the timing of circadian expression relative to oscillations of cytosine

modification to uncover evidence of coordination in the circadian epigenetic-transcription

process. We measured cross correlations of the osc-mC and mRNA oscillations and performed

iterative 1 hour shifts of the mRNA data to identify the phase shift that would produce the

strongest cross correlation between the two datasets. Acrophases of mRNAs were shifted by 5

hours from the osc-mC acrophases in liver and by 2 hours in the lung (Bonferroni corrected

permuted p = 0.038 and 0.0048 in liver and lung, respectively), whereby peak modification was

followed by the nadir of mRNA abundance (Fig 3.13a-b).

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Sequences flanking osc-mCs contained both canonical (CANNTG) and non-canonical

(CANNNTG, GANNTG) (G. C. Han et al., 2016; Virolle et al., 2002) E-box motifs (e-value =

8.3 × 10-11 - 5.2 × 10-152; Fig 3.13c-d, Appendix 4-5), suggestive of a potential role for osc-mCs

in regulation of transcription. Methylation of E-box response elements can inhibit transcription

factor binding (Perini, Diolaiti, Porro, & Della Valle, 2005), (Jin et al., 2016), which suggests

that osc-mCs may be involved in regulating the binding of transcription factors to E-box

elements. In summary, our findings show that osc-mCs are intricately linked to circadian

transcriptomics.

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Figure 3.13: The relationship between circadian transcription and osc-mCs. a-b) Chromosome 7-wide mean of the cross-correlations of the mRNA and its paired cytosines’ oscillations as the mRNA acrophase was iteratively phase shifted by 1 h. mRNA transcripts were paired with the mean modification density of epigenetically variable cytosines within the gene body. Grey lines represent cross-correlations of 10,000 permutations where the mRNA and osc-mC ZT labels were shuffled mean cross-correlations were measured as a function of iterative 1 h shifts. Red bars represent Bonferroni corrected permuted p<0.05. c-d) Representative E-box motifs from (c) the mouse liver and (d) the lung.

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3.2 Circadian oscillations of DNA modification and the aging epigenome

3.2.1 osc-mCs show differential properties with respect to their acrophase timing

We categorized osc-mCs based on the peak of their cytosine modification density in a bimodal

fashion: either during lights off (ZT12–24; wake acrophase) or lights on (ZT0 - 12; sleep

acrophase). The acrophase time was associated with the osc-mC average modification density. In

both liver and lung tissues, osc-mCs that peaked during the wake phase showed a significantly

higher mean cytosine modification density (mean ± standard error; 67 ± 1.19% and 62 ± 0.36%,

respectively) compared with osc-mCs that peaked during the sleep phase (54 ± 1.25% and 50 ±

0.74%, respectively). Modification densities of osc-mCs from the two acrophase peaks exhibited

the largest difference during the wake phase, while during the sleep phase the densities were

closest to each other (Fig 3.14). Borrowing from the field of astronomy, the cytosine

modification densities were at their “apogee” during the wake phase, as the distance between the

two harmonic lines arrived at its maximum, and conversely reached their “perigee” during the

sleep phase, as the distance between the two harmonic lines arrived at its minimum. In other

words, osc-mCs displayed a regression to the mean during the sleep phase, reminiscent of aging

and malignant epigenomes.

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Figure 3.14. Average modification density of osc-mCs within wake and sleep acrophases in 9-mo mouse liver and lung. Circadian DNA modification “perigee” and “apogee” during the sleep and active phases in (a) the mouse liver and (b) the lung; blue-colored lines represent harmonic smoothed mean densities, and shading is the 95% CI. Double plotted for clarity.

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3.2.2 osc-mCs are strongly associated with age-correlated cytosine modification changes

Next, we investigated whether osc-mCs are related to age-correlated cytosine modifications (age-

mCs). For this, we identified age-mCs in the 9-, 15-, and 25-mo mice using linear modelling to

detect cytosine modification changes, measured as the mean of all ZT densities, that either

increased or decreased linearly with age (Figures 3.15 and 3.16). We found that liver samples

had more age-mCs compared to the lung (24.1% and 8.4% of epigenetically variable cytosines,

p<0.05 after Bonferroni correction, in liver and lung, respectively; Fig 3.17). osc-mCs from the

9-mo mice showed a strong association with age-mC in both the liver and lung tissues (OR = 2.3

[2.0–2.7] and 1.4 [1.2–1.6]; p = 2.6 × 10-24 and 1.3 × 10-5, respectively). In addition, the

circadian amplitudes were correlated with the magnitude of the epigenetic aging effects (Fig

3.18a-b) and accounted for 18.4% (Pearson’s r = 0.43; p = 1.3 × 10-14) and 72.8% (Pearson’s r =

0.85; p = 3.4 × 10-111) of the age-dependent variance, in the liver and lung, respectively (Fig

3.18a-b). The association between sleep or wake acrophases and age-dependent gain or loss of

cytosine modification was highly asymmetric: the cytosines whose modification peaked during

the sleep phase exhibited increased modification with age, whereas the cytosines representing

wake acrophases predominantly lost modification with age (OR = 68 [30-166] and 394 [113–

1,875]; p = 1.4 × 10-44 and 9.6 × 10-46, in liver and lung, respectively; Fig 3.18c-f). This finding

suggests that the acrophase peaks likely represent different parts of the epigenome: the hyper-

modified regions of the wake acrophase losing modification with age, and the hypo-modification

regions of the sleep acrophase becoming more densely modified with age.

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Figure 3.15. Representative CpGs displaying age-associated changes in the 9-mo mouse liver. a) The top 4 sites (ranked based on most significant Bonferroni corrected p-values) increasing or decreasing in their average modification density across 3 age groups. Each dot represents the mean density of all biological replicates and the error bars represents SEM. The coordinates for each CpG is indicated on the top left of the plot while the p-value and regression coefficient are indicated in the plot. b) The bottom 4 age-mC CpGs. X-axis: Age (mo); Y-axis: cytosine modification (indicated as % modC).

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Figure 3.16. Representative CpGs displaying age-associated changes in the 9-mo mouse lung. a) The top 4 sites (ranked based on most significant Bonferroni corrected p-values) increasing or decreasing in their average modification density across 3 age groups. Each dot represents the mean density of all biological replicates and the error bars represents SEM. The coordinates for each CpG is indicated on the top left of the plot while the p-value and regression coefficient are indicated in the plot. b) The bottom 4 age-mC CpGs. X-axis: Age (mo); Y-axis: cytosine modification (indicated as % modC).

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Figure 3.17. Age- dependent cytosine modification changes in mouse tissues. a-d) Volcano plot illustrating the direction, magnitude, and statistical significance of age-dependent cytosine modification changes in (a) the liver and (c) the lung, and their corresponding p-value histogram of linear regression fits using age as a predictor across all mouse age cohorts in (b) the liver and (d) the lung. 24.1% (1933 out of 8011) and 8.4% (770 out of 9212) of the interrogated cytosines passed the Bonferroni corrected significance threshold (p < 0.05). Bonferroni corrected significance threshold (p = 0.05) is indicated by the dotted line in the volcano plots. Only epigenetically variable cytosines common across all age cohorts were investigated.

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Figure 3.18. The relationship between oscillating and aging-correlated cytosine modification in mice. a-b) Density contour plots of the age-mC magnitude and the osc-mC amplitude in (a) the mouse liver and (b) the lung. Contours were rendered using 2D kernel density estimation, and the lines represent linear regression fits. Shading around the regression lines represents the 95% confidence band. c-d) The mean modification levels of 9-mo osc-mCs, stratified by cytosines with wake and sleep acrophases, converge with increasing age in (c) the mouse liver and (d) the lung. The curves show harmonic regression fits of the mean cytosine modification and the shading around the regression lines show 95% confidence bands. e-f) The relationship between acrophase time and aging direction in (e) the liver and (f) the lung. The white areas represent periods of lights on (i.e. sleep) while the shaded area show periods of lights off (i.e. wake). The grey bars show count of cytosines with gain of modification with age, while the black bars show loss of modification.

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If osc-mCs are a cause of age-dependent epigenetic changes, the former would precede the latter

chronologically. Therefore, osc-mCs that are exclusively present in younger animals (i.e. 9-mo)

should be enriched for cytosines whose modifications show an aging effect after 9 months.

Conversely, if aging induces osc-mCs, age-mCs should become more abundant amongst the osc-

mCs specific to the older groups (i.e. 15- and 25-mo). Consistent with a circadian causative

direction, cytosines oscillating only in the 9-mo showed an enrichment of age-mCs (binomial p =

3.5 × 10-8; 12.4% were age-mCs), whereas cytosines oscillating only in the 15-mo or only in the

25-mo showed no enrichment or depletion of age-mCs (binomial p = 0.42 and 0.0018; 7.6% and

4.3% were age-mCs, respectively; Fig 3.19). Interestingly, osc-mCs common to all three age

groups also showed a significant depletion of age-mCs (binomial p = 0.0057; 5.3% age-mCs)

suggesting that the circadian-aging conversion has not occurred yet but may take place in the

animals living longer than 25 months. We repeated this analysis using matched sample size and

matched proportions of osc-mCs across all age-groups and arrived at the same conclusions (Fig

3.20). These findings suggest that oscillation of cytosine modification precede age-correlated

cytosine modification changes.

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Figure 3.19. The direction of the association between osc-mCs and age-mCs in the mouse lung. If osc-mCs transition into age-mCs, cytosines exclusively oscillating in the youngest group should contain a larger proportion of age-mCs. If the association is in the other direction, cytosines exclusively oscillating in the oldest group should be enriched for age-mCs. Epigenetically variable cytosines were categorized by their osc-mC status amongst the three cohorts (represented in the heatmap). The percentage of cytosines in each category which were also age-mCs are represented by bars with binomial 95% confidence intervals. Red bars represent binomial p < 0.05. The vertical line represents the percentage of age-mCs amongst all epigenetically variable cytosines in the mouse lung (8.4%).

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Figure 3.20. osc-mC overlap with age-mC after adjusting for sample size or proportion of osc-mCs in the mouse lung. a) A Venn diagram of osc-mCs in the three age cohorts, where all age cohorts were matched to have the same sample size (n=28) and harmonic regression analysis was repeated for each. The various the elements in the Venn diagram that represents groups of osc-mC with specific overlapping properties are indicated by roman numerals. b) Bar plots showing the percentage of age-mC in each osc-mC category, where red bars indicate significant enrichment or depletion of age-mCs. The error bars show binomial 95% confidence intervals, and the dashed line represents the proportion age-mCs relative within all epigenetically variable cytosines. c) Boxplots of 10,000 permutations showing the percentage of age-mC in each osc-mC category, where proportion of osc-mCs in all three age groups were matched by iteratively changing the sample size to produce a similar effect size.

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3.2.3 Circadian and aging transcriptomes are associated

Since epigenetic factors are intimately related to gene transcription, we asked if the association

between circadian rhythms and aging is also applicable to the transcriptome. We compared

publicly-available circadian (R. Zhang et al., 2014) and aging (Bochkis et al., 2014; Misra et al.,

2007) transcriptomes of mouse tissues. In the lung aging dataset, male C57BL/6J mice were

euthanized at ages of 2, 18, and 26 months old (3 animals per group) to detect aging-related

expression profiles (Misra et al., 2007). In the liver aging dataset, male C57BL/6J mice were

euthanized at 3 months (N = 4) and 21 months (N = 3) of age for mRNA analysis (Bochkis et al.,

2014). The circadian dataset was described in section 3.1.5.

Circadian and aging mRNAs were identified using the same approach as the osc-mCs and age-

mCs. We detected a significant overlap between the circadian and aging transcriptomes (OR =

1.3 [1.2–1.4] and 1.6 [1.5–1.8]; p = 1.3 × 10-8 and 1.3 × 10-25 for liver and lung, respectively).

We also found that amplitudes of oscillating transcripts correlate with magnitudes of aging

effects (r = 0.65 and 0.56; p = 4.4 × 10-170 and 9.4 × 10-83 in liver and lung respectively; Fig

3.21). As expected, gene ontology analysis of the mRNAs common to both datasets showed

enrichment of various terms previously identified in circadian and aging studies, including

catabolic and metabolic processes in the liver (FDR q = 0.05 - 1.8 × 10-8) and cell adhesion and

migration in the lung (FDR q = 0.05 - 8.3 × 10-8; Appendix 7-8). These findings, together with

the gene ontology analysis, suggest that circadian-aging association could be universal and

therefore also detectable in metabolites and proteins.

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Figure 3.21. Density contour plots of the magnitude of the age-associated mRNA changes as a function of the amplitude of mRNA oscillations in the mouse liver and lung. a-b) Density contour plots of the magnitude of age-dependent mRNA changes and the amplitude of circadian oscillation from independent public datasets for matching genes in (a) the mouse liver (Bochkis et al., 2014; R. Zhang et al., 2014) (b) and lung (Misra et al., 2007; R. Zhang et al., 2014). All data were normalized according to the methods described in their respective publications. Significant age-dependent changes were detected by linear regression (p < 0.05). Significant oscillating mRNAs were detected using the harmonic regression method used for osc-mC detection. Contours were rendered using 2D kernel density estimation, and the lines represent linear regression fits. Shading around the regression lines represents the 95% confidence band.

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3.3 Circadian oscillations of DNA modification in humans

3.3.1 White blood cell fractions exhibit circadian variations

To expand upon the mouse findings in humans and uncover new aspects of osc-mCs, we next

investigated human blood samples. Previous studies have shown that white blood cell (WBC)

counts may oscillate in a circadian manner (Scheiermann et al., 2013), and we confirmed this

finding in our study. Briefly, four human male subjects ranging in age from 27 to 52 were

recruited to measure their WBC fractions every 3 hours for a minimum of 2 days. The subjects

were provided with the HemoCue WBC DIFF System, which is a point-of-care device that can

allow the subjects to collect real-time data of their WBC counts in the comfort of their homes.

Our results indicate that cell counts of neutrophils (harmonic regression p = 0.01), lymphocytes

(p = 0.04), and eosinophils (p = 0.01) oscillate in phase with the total blood count (p = 0.02) (Fig

3.22a). Interestingly, the relative proportion of the fractions were not in phase: blood was

proportionally enriched for lymphocytes (harmonic regression p =0.008) and eosinophils (p =

0.02) in the early morning and enriched for neutrophils (p = 0.006) in the evening (Fig 3.22b).

Monocytes did not exhibit any oscillations in both count (harmonic regression p = 0.05) and

proportion (p = 0.95).

The difference in the relative proportion of cells in each fraction can potentially simulate

circadian oscillations of cytosine modifications. To avoid oscillations of osc-mCs due to the

circadian cell count changes, we interrogated cytosine modification profiling using gDNA

extracted from antibody-purified neutrophils, the largest fraction of nucleus containing cells in

human blood, from a healthy 52-year-old male every 3 hours for 60 h.

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Figure 3.22. Circadian rhythmicity of WBC counts and composition in peripheral blood. Daily variation of cell counts (a) and WBC composition (b). The lines represent harmonic regression and the dots represent the mean cell counts or mean percentage at each time point as measured by HemoCue WBC DIFF System in 4 male subjects. The panel on the right includes the mean levels across all time points and individuals. Shaded area represents the 95% CI.

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3.3.2 Finding osc-mCs in the mouse liver and human neutrophils using MRE-chip

We further investigated circadian oscillations of DNA modification using the methylation-

sensitive restriction enzyme (MRE-chip) method which selectively enriches the unmethylated

fraction of genomic DNA and interrogates it on tiling microarrays. As with the previous

methodology, all samples were interrogated in technical triplicates and only the epigenetically

variable cytosines were considered for further analysis. Mock controls, also performed with three

technical replicates, were generated from a common pool of genomic DNA and analyzed in the

same manner as the real samples. Briefly, aliquots of gDNA from multiple samples of the

corresponding tissue were pooled to create a “reference” sample. This sample was used in a

mock parallel experiment where the same input material was used every time but the experiment

was otherwise performed in identical conditions – this is referred to as the control condition. In

this condition, there is an absence of biological variability a priori and differences between

samples, and any potential oscillations, are artefacts.

The human neutrophil samples were similarly subjected to the same MRE-chip experimental

conditions described above. Quality control analysis showed all array intensities were

comparable and different genomic elements were represented in similar proportions (Fig 3.23).

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Figure 3.23. Quality control of the tiling microarrays. a-b) boxplot of signal intensities across microarray batches (colored) for (a) real neutrophil dataset and negative controls, and (b) real mouse liver dataset and negative controls. c-d) average signal intensity stratified by fragment lengths for the two restriction enzymes used in the enrichment of unmodified DNA fractions in the (c) the neutrophil, and (d) the mouse liver experiments. The dotted lines represent the smallest and largest fragment size used in the analysis. e) the distribution of the interrogated DNA fragments across genomic elements.

Filtering for sites that showed greater biological than technical variation in cytosine modification

(i.e. epigenetically variable fragments) revealed clear differences between the real and control

datasets (Fig 3.24). In the mouse liver, only 388 out of 52,341 fragments (OR = 0.9 [0.79-0.98];

p = 0.02) were common in the epigenetically variable fragments of the control and real datasets,

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while 555 out of 81,937 (OR = 0.9 [0.84-1.00]; p = 0.06) were common in the human

neutrophils.

Figure 3.24. Scatterplot of the epigenetic variability p-values (one way ANOVA) in human neutrophils and 9-mo mouse livers as measured by MRE-chip. In human neutrophil samples 11.4% of the fragments (9,298 out of 81,937) were epigenetically variable in the real dataset while only 6.4% (5,253 out of 81,937) were variable in the control. There were 555 common fragments between the two. In the mouse liver, there were 14.2% (7,443 out of 52,341) and 5.8% (3,025 out of 52,341) epigenetically variable fragments in real and control experiments, respectively. Only 388 were common between the two datasets. Dotted lines indicate p = 0.05.

In a primary visual inspection of the epigenetically variable fragments, day and night DNA

modification patterns revealed a wave-like surface that propagated throughout the chromosome

in both human neutrophils and mouse liver, corroborating our previous observations in mice (Fig

3.25). Formal harmonic regression analysis revealed that 12.0% of the fragments exhibited a 24-

hr oscillation pattern in human neutrophils (permuted p=0.026; Fig 3.26). Analysis of the mouse

liver revealed osc-mCs in 8.6% of the fragments (permuted p=0.046) (Fig 3.27).

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Figure 3.25: DNA modification surface plots of all epigenetically variable microarray probe signals. a) 2D Gaussian smoothed chromosome 1 surface plot of all epigenetically variable microarray signals. The data were duplicated (2 x 24 hours) for smoothing purposes. b) human neutrophil chromosome 1 from the negative control dataset. b-c) mouse chromosome 2 of the liver dataset (b) and the (c) negative control dataset.

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Figure 3.26. osc-mC properties in human neutrophils (tiling microarray experiment). a) the p-value histogram for harmonic regression fits show enrichment of osc-mCs in the real dataset (12%; 1,112 of 9,298 epigenetically variable fragments) but not in the negative control dataset. b) 10,000 permutations of CT labels showing that the average proportion of variance explained by osc-mCs is significant (permuted p=0.026) in the real dataset but not in the negative controls (permuted p=0.79).

Figure 3.27. osc-mC properties in the mouse liver (tiling microarray experiment). a) the p-value histogram for harmonic regression fits showed 8.6% enrichment of osc-mCs in the real dataset (641 of 7,443 probes) but only 5.3% in the negative control dataset. b) 10,000 permutations of ZT labels showing that the average proportion of variance explained by osc-mC is significant (permuted p=0.045) in the real but not the negative control dataset (permuted p=0.36).

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3.3.3 osc-mCs in human neutrophils

To explore oscillations of DNA modifications in human neutrophils at the single cytosine

resolution, we used the Infinium HumanMethylation450 BeadChip (450K) array. The

experiment was performed in technical duplicates where two aliquots of gDNA collected from

homogenous neutrophil cells was subjected to two independent bisulfite reactions and analyzed

on two separate arrays. Harmonic regression analysis detected that 11.5% (permuted p = 0.029)

of epigenetically variable cytosines exhibited oscillations with a mean amplitude of 2.8 ± 1.4%

and a max amplitude of 10.4%., peaking predominantly during wake hours (Fig 3.28a-e).

Principal component analysis showed significant oscillation of principal components 1 and 3

(harmonic regression p = 0.038 and 0.037, respectively), which cumulatively explained 32.1% of

the variance (Fig 3.28f). Furthermore, sequences surrounding neutrophil osc-mCs were enriched

for non-canonical (CANNNTG and GANNTG) E-box motifs (e-value = 4.6 × 10-101 - 3.7 × 10-

119, Appendix 6).

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Figure 3.28. osc-mC properties of individual cytosines in human neutrophils tested on 450K microarrays. a-b) The top 4 oscillating (a) and non-oscillating CpGs (b) (ranked based on p-values) in human neutrophils plotted over 60 h. Each dot represents a technical replicate. c) p-value histogram of harmonic regression fits showing that 11.5% (3,912 of 34,137) of epigenetically variable cytosines were oscillating (p < 0.05). d) Average proportion of variance explained (R2) by the harmonic regression fits across all epigenetically variable cytosines in each of 10,000 permutations of circadian time labels. The red line depicts the observed average R2. e) Acrophase rose plot showing modification peak times of osc-mCs. f) Harmonic regression fits on the first 4 principal components (PCs) and the variance explained by each PC. PCs with harmonic regression p < 0.05 are depicted in red.

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3.3.4 The association between circadian and aging- dependent DNA modification in humans

Next, we investigated the relationship between osc-mCs and age-mCs in humans. We utilized a

publicly available 450K dataset (Hannum et al., 2013) with measurements from whole blood

methylome of 656 humans aged 19-101 years. Methylation levels were adjusted for cell count

variation, and age-mCs were identified using linear modelling. We found 2.9% (13,555 out of

473,034, p<0.05 after Bonferroni correction) of CpGs displayed an aging effect. osc-mCs,

identified in our subject, were observed to be associated with these age-mCs (OR = 1.7 [1.4–2];

p = 1.4 × 10-9) and further displayed an acrophase-dependent gain or loss of cytosine

modification with age (OR = 8.3 [1.3–90.6]; p = 1.1 × 10-2).

Figure 3.29. Age-dependent cytosine modification changes in populational study of human blood. a) Volcano plot of 10,000 randomly selected CpGs and (b) the p-value histogram for all CpGs for human whole blood (Hannum et al., 2013) with 2.9% (13,555 out of 473,034) of the cytosines passing the Bonferroni corrected significance threshold (p < 0.05). Bonferroni corrected significance threshold (p = 0.05) is indicated by the dotted line in the volcano plots.

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3.3.5 Contribution of osc-mCs to epigenetic variability

It is generally accepted that inter-individual DNA modification differences are induced by

stochastic factors and non-shared environments, whereas DNA sequence variation plays a lesser

role (Busche et al., 2015). Our analysis of inbred (i.e., nearly genetically identical) mice with

uniform housing environments, suggests that osc-mCs are another factor that can explain a

portion of the inter-individual epigenetic variation. Thus, it is likely that circadian epigenetic

variation may contribute significantly to what has traditionally been treated as stochastic

epigenetic variation since they would be difficult to identify if samples are not collected with

circadian variation in mind. Given that our neutrophil samples were collected from a single

individual across multiple time points, thus avoiding effects of non-shared environment and

DNA variation, it is possible to explore the contributions of osc-mCs to stochastic epigenetic

variations. Indeed, we found that osc-mCs were significantly associated with the most

epigenetically variable cytosines (logistic regression p = 4.1 × 10-219). Contribution of osc-mC to

epigenetic variance was different at different genomic elements: CpG islands, shores (2 kb

flanking the islands), shelves (2kb flanking the shores), and seas (regions outside the previous

three categories). The difference in variance increased gradually from the islands (t = 9.34,

Welch's t-test p = 1.5 × 10-19) towards the sea (t = 24.46, Welch's t-test p = 5.8 × 10-121),

suggestive of a more dynamic and variable state in the shores, shelves, and seas (Fig 3.30a).

Interestingly, human neutrophil osc-mC were underrepresented at CpG islands (OR = 0.47 [0.43

– 0.52]; p = 2.41 × 10-67) and overrepresented in the shelves and seas (combined OR = 1.31

[1.18–1.45]; p = 2.6 × 10-7) (Fig 3.30b).

Next, we examined possible links between neutrophil osc-mCs and populational epigenetic

variation, which, unlike intra-individual variation, is influenced by a large variety of

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environmental and genetic factors. Neutrophil osc-mCs were strongly associated with the most

epigenetically variable CpGs after accounting for age, sex, race, and cellular composition in two

large whole blood datasets(Hannon, Dempster, et al., 2016; Hannum et al., 2013) (logistic

regression p = 1.2 × 10-4 and p = 1.2 × 10-17, respectively). Similar to the neutrophil osc-mCs, the

most variable probes (top 10%) were enriched in the seas (OR = 1.4 [1.3–1.5]; p = 1 × 10-12 and

OR = 1.5 [1.3–1.6]; p = 1.3 × 10-16), gradually diminished in shelves and shores, and were

depleted in the islands (OR = 0.44 [0.39–0.5]; p = 4.3 × 10-44 and OR = 0.44 [0.39–0.5]; p = 3.8

× 10-46) (Fig 3.30b). These findings are consistent with previous studies showing higher

epigenetic variance in regions outside of CpG islands which performed the best in distinguishing

normal and malignant tissues (A. Doi et al., 2009; Irizarry et al., 2009). Our data suggest that

osc-mCs are an important constituent of this variation, which by corollary suggests a potential

involvement of osc-mCs in development and carcinogenesis.

Figure 3.30. Properties and distributions of osc-mCs within neutrophil promoters. a) Comparison of variances at oscillating and non-oscillating cytosines across promoter elements in human neutrophils. b) Odds ratios of osc-mCs and the top 10% most variable CpGs from populational data 1 (Hannum et al., 2013) and populational data 2 (Hannon, Dempster, et al., 2016) within different elements. osc-mCs were enriched in CpG island shelves and seas while they were depleted within CpG islands and shores. Populational datasets similarly display gradual enrichment outwards from islands to seas. Black circles indicate Fisher’s exact p < 0.05.

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3.3.6 osc-mCs are linked with DNA modification hallmarks of complex diseases

Our findings that circadian cytosine modifications are associated with age, contribute to

epigenetic variation in functionally important genomic regions and exhibits daily dynamics (i.e.,

epigenetic perigee) similar to malignant epigenomes prompted us to directly investigate

involvement of osc-mC in human complex diseases (Fig 3.31). First, we analyzed the EWAS

results of two malignant blood diseases, chronic lymphocytic leukemia (CLL) (Kulis et al., 2012)

and acute myeloid leukemia (AML) (von der Heide et al., 2016). Both of these diseases are more

prevalent among night shift workers and display epigenetic and age-associated elements (Costas

et al., 2016; Gundestrup & Storm, 1999; Pukkala et al., 2012).

In the CLL study, the authors identified DNA methylation differences that distinguished two

molecular subtypes of CLL from normal B cells in 139 patients. Neutrophil osc-mCs were

significantly overrepresented in CpGs that showed differential modification of B-cells in both the

poor prognosis (U-CLL) subtype (OR = 1.7 [1.6–1.9]; p = 6.1 × 10-24) and the more favorable

prognosis (M-CLL) subtype (OR = 2.6 [2.1–3.2]; p = 1.7 × 10-18). In addition, osc-mCs were

associated with the modified cytosines distinguishing the two subtypes (OR = 2.6 [2–3.3]; p =

4.7 × 10-12). The same analysis was repeated a second time with all probes, as opposed to only

the epigenetically variable probes, used as the background and yielded similar results (Fig 3.31).

In the AML study, the authors compared bone marrow mesenchymal stromal cells of healthy (N

= 12) and AML patients (N = 32) to identify DNA methylation differences between the two

groups. The CpGs that were differentially modified (p < 0.05) in patients with AML compared

with controls were also associated with neutrophil osc-mCs (OR = 1.2 [1.1–1.3]; p = 5.1 × 10-4)

when all probes were used as background. Restricting the analysis to only the epigenetically

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variable probes did not produce significant results (OR = 1.1 [0.99–1.2]; p = 0.062). These

findings suggest that cancer-associated cytosine modification changes could arise from

disruption of circadian variations in cytosine modification.

Next, we re-analyzed three large EWAS of two brain diseases: two blood-based datasets of

schizophrenia (Hannon, Dempster, et al., 2016; Montano et al., 2016) and one post-mortem brain

study of Alzheimer’s disease (De Jager et al., 2014). In the first schizophrenia study (Montano et

al., 2016), cytosine modification differences between patients and controls (corrected for WBC

count differences, 895 controls and 936 cases) were significantly associated with neutrophil osc-

mCs (OR = 6.1 [2.5–14]; p = 4.2 × 10-5). Using all probes as the background produced similar

results (OR = 8.1 [4–15]; 2.9 × 10-7) (Fig 3.31, SCZ DATA 1). The second schizophrenia study

(Hannon, Dempster, et al., 2016) includes 1714 individuals (863 cases and 851 controls) and osc-

mCs, taken against a background of all probes, were observed to be associated with differentially

modified CpGs after correction for age, sex, experimental batch, cell composition and smoking

(OR = 3.1 [1.4–6]; p = 3.2 × 10-3) (Fig 3.31, SCZ DATA 2). Restricting the analysis to only the

set of epigenetically variable cytosines identified in our osc-mC analysis did not reveal a

significant overlap (OR = 1.5 [0.66–3.2]; p = 0.28).

The Alzheimer’s study (De Jager et al., 2014), includes 708 dorsolateral prefrontal cortex

samples with 429 meeting the pathological diagnosis of Alzheimer’s disease. Participants were

part of two longitudinal studies, the Religious Orders Study and the Rush Memory and Aging

Project. The same brain collection was previously used for identification of circadian rhythmicity

in DNA modifications (Lim et al., 2014). We detected that neurodegeneration-related DNA

modification changes overlap with brain osc-mCs (OR = 2.8 [1.5–5.0], p = 5.2 × 10-4).

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Figure 3.31. Association of neutrophil osc-mCs with differentially modified cytosines identified in published EWAS. Odds ratios of CpGs that exhibit both circadian oscillations in our analysis of human neutrophils and are differentially modified in publicly available blood-based studies of leukemia and schizophrenia. The association reported for Alzheimer’s is from two public datasets that utilize the same brain collection. AML: acute myeloid leukemia, M-CLL: chronic lymphocytic leukemia (favorable prognosis), U-CLL: chronic lymphocytic leukemia (poor prognosis), SCZ: schizophrenia. Circles show odds ratios that used all probes on the 450K array as the background while triangles used only epigenetically variable cytosines identified in our neutrophil study as the background. P-values were derived using Fisher’s exact test. Red markers are significant (p < 0.05) odds ratios.

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Chapter 4 Discussion, conclusions and future directions

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4.1 Widespread oscillations of DNA modification The first aim of this thesis was to explore the occurrence and extent of circadian oscillations of

DNA modifications. We mapped cytosine modifications in the mouse liver and lung as well as

purified human neutrophil samples collected in a circadian manner. We utilized both high-

resolution bisulfite based methods (padlock probes, 450K) and broader, genome-wide (MRE-

chip) approaches. Our analysis consistently revealed robust oscillations of cytosine

modifications, referred to as osc-mCs, in all examined tissues comprising between 8.2-35.6% of

the interrogated epigenetically variable cytosines.

Canonical and noncanonical E-box motifs were enriched in osc-mCs, suggesting that oscillations

of cytosine modifications may play a role in the regulation of circadian transcription by either

inhibiting transcription factor binding (Jin et al., 2016; Perini et al., 2005) or possibly through the

circadian occupancy and resultant blocking of de novo methylation by TFs. We also observed a

temporal relationship, a 2-5 hour phase difference, between acrophases of osc-mCs and circadian

mRNA transcripts, where the peaks of osc-mCs preceded the nadir of their paired mature

mRNA. Interesting to note that phase differences have also been reported between circadian

changes in histone modifications with both nascent transcription and mature mRNA levels

(Koike et al., 2012; Sun, Feng, Everett, Bugge, & Lazar, 2011). Altogether this suggests a

coordinated pattern of amongst different regulatory elements of the nucleus.

In the mouse liver, osc-mCs are composed of oscillations in both 5-mC and 5-hmC, with a

significant overlap between the two types of DNA modification. Intriguingly, the overlapping

signals peaked in antiphase with respect to one another, which supports a cyclical DNA

demethylation-remethylation mechanism. While the initial discovery of 5-hmC and TET

enzymes demonstrated that cytosine methylation is not as stable as it has been traditionally

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thought, the biological roles of active demethylation in somatic, differentiated cells are not yet

clear (Schubeler, 2015). To this end, circadian oscillations of DNA modification may be a

significant source of 5-hmC production linking the dynamic epigenome with transcriptional and

other regulatory processes in the nucleus.

We also observed a bimodal distribution of acrophases in the liver while the lung acrophases

were heavily skewed towards the sleep phase. In both tissues, osc-mCs with wake and sleep

acrophases displayed differences in their modification levels with sleep osc-mCs exhibiting a

lower level of modification. This is likely mediated by the differences in the genomic elements

undergoing oscillations at different times. It is also possible that the differences in peak

expressions of genes coding DNA modification enzymes and proteins, such as Dnmt3a and

Dnmt3b observed in the liver, may be mediating differential patterns of methylation in different

genomic elements. Oscillations of DNA modification in the nucleus are, however, quite distant

from steady state mRNA levels in the cytoplasm and in the absence of protein levels, genome

localization, and enzymatic activity it is difficult to draw any significant conclusions from our

mRNA findings. Additionally, the reported oscillations of the DNA methylation precursor,

SAM, in the mouse liver (Xia et al., 2015) adds another layer of complexity that needs to be

considered. For instance, it has been shown that the circadian role of the histone deacetylase

SIRT1 is not reflected in any variations of its levels, but rather by changes in its activity in

response to oscillations of its NAD+ precursor (Nakahata, Sahar, Astarita, Kaluzova, & Sassone-

Corsi, 2009).

The sparse coverage of padlock probe experiments prevented us from performing a thorough

analysis of the genomic elements and genomic distribution of osc-mCs. In the mouse liver, osc-

mCs were limited to a range of 390-1,224 CpGs, while in the lung they ranged from 1,882-5,054

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across the three age groups (Appendix 2). These numbers yield inadequate coverage within each

element that is necessary to robustly explore the genomic distribution of osc-mCs. Infinium

HumanMethylation450 BeadChip analysis of human neutrophils, on the other hand, provided us

with a sufficient number of osc-mCs to investigate differential clustering of osc-mCs within

CGIs, gene promoters, and their vicinity. We observed an increasing proportion of osc-mCs

moving outwards from CpG islands to the seas. This enrichment was concurrent with an increase

in the variability of modification densities within these regions. This is consistent with the idea

that genomic regions undergoing circadian oscillations are expected to exhibit more variability

than the more static parts of the genome. We also replicated the same findings using publicly

available datasets. These variable regions flanking the CGIs have also previously been reported

to be differentially modified in malignancies (A. Doi et al., 2009; Irizarry et al., 2009).

Our findings also indicate that circadian oscillations may explain a portion of what has

traditionally been considered to be stochastic epigenetic variation. It has been generally thought

that the largest source of inter-individual DNA modification differences is through non-shared

environments and stochasticity, whereas DNA sequence variation plays a smaller role (Busche et

al., 2015). These variations are typically tissue-specific, depleted in promoter CGIs, and are

enriched in regions with intermediate methylation densities. Our analysis of neutrophils collected

from the same individual over time, thus eliminating the non-shared environmental effects of

populational studies, demonstrates that osc-mCs are associated with the most epigenetically

variable cytosines and are depleted in promoter CGIs, supporting their contributions to the

presumed stochastic fraction. We further examined this hypothesis in two large whole blood

populational datasets, and after correction for known biological variations (age, sex, race, and

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cellular composition), the remaining epigenetic variation was significantly associated with osc-

mCs.

Our results may suggest that on average only ~5% of cells are undergoing circadian

demethylation-remethylation for a given osc-mC, and consequently may be of limited biological

significance. It is possible, however, that different cytosines within a heterogeneously modified

fragment may be subject to cycles of demethylation-remethylation in different cells and our

study has only captured the most frequently changing cytosines in this process. It remains to be

seen if the amplitudes of osc-mCs change if cytosine modifications are measured cumulatively

and in larger fragments. Irrespective of the degree of their contribution to the maintenance and

perpetuation of a circadian rhythm inferred by the amplitude of their oscillations, the observed

association of osc-mCs with aging and disease-associated epigenetic changes would indicate that

circadian oscillations of DNA modification profiles are biologically significant.

In the human neutrophil experiments, we observed oscillations in both the total WBC count and

the fractions of WBC. This finding has broad implications for other DNA modification studies

that utilize tissues. These studies need to be performed in a circadian-informed manner and

corrected for cell count oscillations whenever possible. Interestingly, the proportion of the

myeloid lineage, which oscillates in a 24-hour manner, increases with age (Jaffe & Irizarry,

2014). This provides another biological example of an oscillating element that manifests linear

changes along the age dimension – similar to what is observed with osc-mCs.

While we tried to address the potentially confounding effects of WBC oscillations by performing

our analyses on purified neutrophil cell populations, it is important to recognize that neutrophils

can be further classified based on their antigenic features (Kamp et al., 2012; Pillay et al., 2012).

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If these subsets are epigenetically distinct and oscillate in a circadian manner, then an additional

layer of cell type variation may continue to confound circadian analyses even in purified

neutrophil populations. A previous study, however, did not find significant variations in the

proportions of neutrophil subfractions within the population (Ecker et al., 2017). Along the same

lines, in the mouse liver, regenerative capacity is mediated by the extensive rate of polyploidy in

hepatocytes (Gentric & Desdouets, 2014) and a very recent study has found antiphasic circadian

oscillations of mono-nucleated tetraploid and bi-nucleated diploid hepatocytes in healthy mouse

liver without any proliferative changes (J. Wang et al., 2017). Similarly, if extensive epigenetic

variations coincide with these ploidy changes, then cellular heterogeneity concerns would still be

present even in an ideal purified hepatocyte cell population. These cell-type complexities

emphasize the need to investigate epigenetic differences in parallel to cell count analyses in

future epigenetic studies.

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4.2 osc-mCs are associated with aging The second aim of this thesis was to characterize the relationship between DNA modification

oscillations and epigenetic changes associated with aging. We observed a strong association

between the cytosines that change with age, referred to as age-mCs, and the previously described

osc-mCs in the mouse liver and lung. The amplitudes of osc-mCs were correlated with the

magnitude of change observed with age. osc-mCs reaching their acrophase during sleep were

characterized by lower modification levels (50-54%) and an age-dependent gain of modification

while osc-mCs reaching their acrophase during wake hours exhibited higher average

modification levels (62-67%) and loss of modification with age. We replicated this association in

humans using the neutrophil osc-mCs and age-mCs derived from a populational dataset. We also

expanded our circadian-aging association in the context of transcriptional studies wherein

circadian transcription was found to overlap with age-associated changes in transcription.

Altogether, our results provide a molecular framework for the observed association between

aging and circadian rhythmicity.

The observed epigenomic “perigee” during the sleep phase is reminiscent of cytosine profiles

regressing toward the mean in the aging epigenome: the hyper-modified non-CGI fraction loses

modification while the hypo-modified CGI CpGs gain modification. Interestingly, global loss of

modification and CGI-specific gain of modification are also observed in cancer.

We observed similarities and distinct differences in aging and osc-mC profiles between the

mouse liver and lung tissues, namely a smaller number of osc-mCs but a larger number of age-

mCs in the liver. It is possible that a faster rate of aging in liver can lead to diminished

oscillations and this difference in signal-to-noise ratio would reduce the robustness of the osc-

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mC/age-mC association in the liver. It is also possible that differences in environmental exposure

and indirect entrainment are mediating tissue-specific patterns. Liver’s rhythmicity, for instance,

can be decoupled from the rest of the body based on feeding schedule (Damiola et al., 2000;

Stokkan, Yamazaki, Tei, Sakaki, & Menaker, 2001) and this capacity for indirect entrainment

combined with ad libitum feeding may be one reason why osc-mCs were more asynchronous in

the liver.

Additionally, Dnmt3a and 3b oscillate in antiphase with one another in the mouse liver, while no

phase differences were observed in the lung. Previously, a 2 hour light pulse during the dark

phase was shown to decrease the SCN expression of Dnmt3a, while Dnmt3b expression was

conversely increased (Azzi et al., 2014). Interestingly, in older mice where SCN Dnmt3a levels

were reported to decline by as much as 50%, methylation-mediated re-entrainment was also

reported to be much less effective (ibid). Similarly, there may be age- and tissue-specific changes

in the expression patterns of these two enzymes, and others involved in the methylation-

demethylation process, that could explain differences between liver and lung in both circadian

and aging patterns.

What is the direction of association between osc-mCs and the aging epigenome? Since osc-mCs

precede the aging epigenome in time, it is suggestive of a unidirectional and potentially causal

relationship. There is also a biological gradient: higher amplitudes of circadian oscillation are

associated with larger age effects. This directionality would be biologically plausible. The

epigenetically and transcriptionally dynamic circadian components of the genome should be

more prone to dysregulation than the more static parts of the genome. It is, therefore, conceivable

that incomplete cycling may lead to the gradual accrual of epigenetic changes over time.

Therefore, epigenetically and transcriptionally dysregulated genes and regions may not be

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randomly distributed across the genome but instead be clustered in circadian loci. Our findings

warrant further experimentation, including genetic knockout models to further elucidate the

direction of this association. Among other outstanding questions are detailed characterizations of

species-, individual-, and tissue-specificity and the dynamics of the circadian parameters in

epigenomes from birth to old age.

The circadian clock evolved before the advent of air travel, light bulbs, and constant food

availability and does not contain the necessary counterbalances for these stressors (Bass & Lazar,

2016). The plasticity and programmability of DNA methylation and the circadian clock make

them amenable to continuous environmental inputs that can ultimately facilitate both circadian

dysregulation or the amelioration of such dysregulations. Circadian parameters can be altered by

various factors, including diet and chemical compounds, and such circadian manipulations may

have an impact on the molecular trajectories during aging. In other words, it may be possible to

potentially influence aging outcomes by modifying circadian epigenomes and transcriptomes at a

younger age.

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4.3 osc-mCs are associated with disease Finally, our findings that osc-mCs occur at loci showing epigenetic changes in leukemia,

schizophrenia and Alzheimer’s disease shed new light on the relationship between circadian

epigenomes, carcinogenesis and brain dysfunction. Prospective and longitudinal studies are

necessary to uncover the direction of the association between the circadian epigenome and

disease epimutations. Evidence that osc-mCs precede manifestations of the disease and then

become disease-specific differentially modified site would support the causal roles of circadian

DNA modifications in disease.

The osc-mC - disease association in the schizophrenia and the Alzheimer’s disease study were

derived from the same cell types or corrected for cellular composition so it is unlikely to be

strongly confounded by tissue specific effects. On the other hand, different lineages were used in

the leukemia studies and ours, and we were still able to identify significant osc-mC associations

with these two cancer types. In this connection, it remains to be seen if the osc-mC associations

with disease become stronger if identical cell types are utilized in both cases.

One possible confounding factor for the reported disease associations is that patients and controls

were mismatched for circadian phase, either due to the underlying pathology or by experimental

happenstance, and circadian differences between the groups may have simulated disease

epigenomic differences. That is, circadian effect was partially detected in the first place instead

of disease differences. Therefore, a practical implication of this study is that, in population

epigenomic studies, samples should be matched for their circadian phase to reduce the potential

for circadian mismatches.

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As we learn more about the molecular components of the circadian clock and its relationship

with aging and disease, it is important to incorporate these findings with ongoing efforts in

precision medicine. The timing of food intake can significantly influence metabolic outcomes

(Panda, 2016). Similarly, it is becoming increasing clear that the absorption, function, and

clearance of many drugs are likely to have circadian elements and are consequently subject to

optimization through timing (Dallmann, Okyar, & Levi, 2016). 56 of the top 100 best-selling

drugs in the US, half of which have half-lives of less than 6 hours, target elements of the

circadian system (R. Zhang et al., 2014). Mistimed application of these drugs is likely to

decrease their efficacy. Our own findings suggest that the growing number of therapeutic agents

that target the DNA methylation pathway, such as Azacitidine and Decitabine commonly

prescribed in blood-based malignancies (X. Yang, Lay, Han, & Jones, 2010), may also be more

efficacious if used in a circadian-informed manner.

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4.4 Recommendations for future studies There are several future directions that can be recommended based on the findings presented in

this thesis. One of the significant questions still to be answered is the distribution of osc-mCs

across different genomic elements. While our results demonstrated a depletion of osc-mCs in

CpG islands and an enrichment in shelves and seas, their distributions outside of promoters, in

regions such as enhancers, exons, introns, and insulators, remain unclear. If different cytosines

within a heterogeneously modified fragment are oscillating in different cells, then a larger scope

of analysis involving larger fragments would be more informative. In this connection, padlock

probe experiments containing significantly larger number of probes augmented by whole

genome bisulfite sequencing of samples from a select number of ZTs may allow a more thorough

analysis of osc-mCs. Consequently, we would be able to identify regions of oscillation, as

opposed to solitary CpGs, and characterize different genomic elements in terms of their osc-mC

distribution.

The field of circadian epigenomics would also benefit from in-depth analysis of more tissues,

particularly the brain. There is a large diversity of phases in the oscillatory networks of the

different regions of the brain and it would be interesting to see how the mitotically inactive

neurons, with the highest 5-hmC content in the body, utilize DNA modifications to maintain this

diversity (Abe et al., 2002; Harbour et al., 2013). The involvement of osc-mCs in the

establishment of the central clock in the suprachiasmatic nucleus is of particular interest. A

previous study reported that DNA methylation changes are only involved in re-entrainment of

this nucleus and not in the endogenous perpetuation of the clock (Azzi et al., 2014). As discussed

in the introductory chapter, technical limitations of this study likely precluded detection of osc-

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mCs, and a more thorough sampling and sensitive methodology can provide more conclusive

results.

Our mouse experiments were performed in alternating light and darkness phases with ad libitum

food access. It remains to be seen how the epigenomes of entrainment-sensitive peripheral

tissues, such as the liver, would respond in an environment devoid of external cues. In this case,

the animals would be subjected to constant darkness with time-restricted access to food and any

oscillations in their epigenetic profiles would be considered to be independent of re-entrainment

cues or ‘course corrections’ and entirely representative of their autonomous, endogenous

rhythmicity. Similarly, our mouse findings could benefit from follow-up investigations in

arrhythmic transgenic strains, such as the Per1-/-; Per2-/- double-knockout mice (Xia et al., 2015).

Changes in osc-mCs density, amplitude or distribution in the epigenomes of these mutant

animals would provide additional evidence that oscillations of DNA modification are part of the

circadian clock.

In this study, our animal cohorts were purchased in three different age groups: 2-mo, 8-mo, and

18-mo. These animals were previously group-housed but were transferred into individual cages

upon arrival at our facilities and kept there for 7 months to control for social determinants of

circadian rhythmicity (Mistlberger & Skene, 2004). Circadian studies in animals are inherently

complex and there may not be a perfect design, but it has to be noted that a difference in the

amount of time spent in a social environment exists within our animal cohorts. Additionally,

while it was not possible to track husbandry, it is likely that the animals in three age cohorts are

from different litters due to their age differences. Consequently, genetic differences may exist

between the age groups but are unlikely to affect the overall patterns observed along < 37K

interrogated CpGs in this study. An alternative design would be to purchase the animals at the

148

same age and place them into individual cages followed by euthanizing at the desired age. In this

design, however, the tissues are not harvested at the same time, seasonal variations become a

confounder, animals spent different durations in isolation, and the in-house aging of the animals

becomes time and cost prohibitive.

An additional limitation in our mouse study is the age mismatching of the transcriptional dataset.

We utilized a large public dataset of circadian transcriptomes from 2-mo mice (R. Zhang et al.,

2014). While we observed an association with osc-mCs measured in 9-mo mice of the same

strain, it remains to be seen if associations are improved in age-matched animals, or when

transcriptional and DNA modification analyses are performed in the same mice.

Although our study has only captured the most common osc-mCs in the bulk of a tissue, it is

highly unlikely individual cells have identical oscillations – particularly in the context of

intermediate levels of modification. Even subtle cellular variation in daily reprogramming of

DNA modifications of individual cells can create significant heterogeneity of DNA modification

within the tissue of the same organism. New analytical tools can register DNA modification and

transcriptional dynamics within the same individual cells and help establish the degree of

epigenetic and transcriptional heterogeneity within the same tissue (Angermueller et al., 2016).

Additionally, circadian samples collected from subjects suffering from leukemia, schizophrenia,

and Alzheimer’s disease will allow us to potentially identify genuine osc-mC dysregulations in

these diseases and build upon the associations reported in this thesis. It would also be interesting

to investigate the extent to which these osc-mC parameters contribute to other diseases.

Populational studies are necessary to register inter-individual variation of circadian epigenomes.

Longitudinal monitoring of osc-mCs within the same individual would demonstrate whether or

149

not typical or atypical oscillations of the epigenome precede the occurrence of aging or morbid

epigenome, and provide further insights into etiological roles of osc-mCs in common complex

diseases. This would also identify predictors of a deviant epigenetic trajectory and offer new

possibilities to correct them by altering circadian parameters with diet, medicine, or lifestyle

changes.

Deterioration of stem cell function is another element of aging and age-associated diseases that

can be explored in the context of our findings. Studies have identified accumulations of

epigenetic lesions in aged stem cells that have gone through repeated rounds of differentiation

and self-renewal (J. Oh, Lee, & Wagers, 2014). Since some elements of cell fate decisions in

some tissues, including intestinal stem cells (Karpowicz, Zhang, Hogenesch, Emery, &

Perrimon, 2013), epidermal stem cells (Janich et al., 2011), and hematopoietic stem cells

(Mendez-Ferrer, Lucas, Battista, & Frenette, 2008), are subject to circadian oscillations, our

results can potentially link differentiation, circadian physiology, and aging through their

common epigenetic elements. In this connection, it is now possible to generate self-organizing

organoids, comprised of a variety of cell types, by culturing stem cell- containing intestinal

crypts in specialized 3D cell cultures (Grun et al., 2015; T. Sato et al., 2009). Recent advances

have also made it possible to establish and track circadian rhythmicity within these organoids

using bioluminescence (Moore et al., 2014). Subsequently, it may be possible to generate

successive generations of organoids from one original intestinal crypt. In this experimental

approach, we can interrogate DNA modification profiles of the progeny in each organoid for

evidence of both daily oscillation and age-associated changes as the stem cells are subjected to

replicative senescence. This system can recapitulate the parameters of a longitudinal study in a

150

more reductionist manner and in a shorter time-frame, with the added benefit of pharmaceutical

manipulations.

Future experiments using in vitro and ex vivo protocols can allow successive sampling from the

same pool of cells. This can, in turn, minimize interindividual, environmental, and genetic

variation as all the cells will be coming from the same tissue of the same animal. If primary

hepatocyte tissue cultures from one animal can be maintained in a state of circadian homeostasis

for a minimum of 24 hours, the entire circadian epigenomics experiment could be performed on

one homogenous population of cells maintained in non-variable conditions. These approaches

are not trivial as cells from peripheral tissues do not maintain their endogenous rhythmicity for

too long outside of the body (Brown & Azzi, 2013). Nevertheless, successful utilization of these

approaches will also make it possible to experiment with chemical modifiers of circadian,

epigenetic, and longevity parameters to further elucidate their relationships investigated in this

thesis.

The underpinnings of the circadian machinery include numerous interdependent feedback loops

which precludes a clear demarcation of the hierarchy of such elements. To uncover the nuclear

organization of the chromatin, various molecular layers and their dynamics all have to be

investigated at the same time. Without consideration of the temporal dimension, a substantial

part of the molecular circuitry will remain obscure. Subsequently, future experiments need to

establish the interactions between the numerous cogs of the circadian clock. This can be done by

utilizing both in vivo and in vitro methods to examine the entire nucleome, including epigenetic

modifications, chromatin architecture, chromatin interactions, splicing, and transcription, at the

same circadian time.

151

4.5 Conclusions In this study, we demonstrate circadian oscillations of DNA modification densities, referred to as

osc-mCs, in the mouse lung, liver, and human neutrophils which make DNA modifications a

member of the cell’s circadian machinery alongside oscillating RNA, histone modifications,

proteins, and metabolites. We also report the involvement of methylation and demethylation

processes as liver osc-mCs included both 5-mC and 5-hmC oscillations, frequently emerging

from the same CpG and in antiphase with one another. This is a novel role for 5-hmC in

terminally differentiated somatic cells, where its function may be closely related to one of the

oldest evolutionary adaptations to the daily variation of sun exposure. Furthermore, the

oscillations in DNA modification are linked to transcriptional regulation as E-box motifs were

enriched in the vicinity of osc-mCs, which were also associated with circadian mature mRNA

levels. Our analysis of human neutrophils also revealed that osc-mCs are depleted in CpG islands

but gradually increase in proportion moving outward from the island and are enriched in the sea.

This was concurrent with the gradient of epigenetic variance described by other researchers and

indicates that osc-mCs significantly contribute to the epigenetic variation.

Daily oscillations of DNA modification densities were found to be associated with aging-

correlated changes in DNA modification, referred to as age-mCs. A similar circadian-aging

association was also observed for gene expression. The osc-mC/age-mC association displayed a

biological gradient: the amplitude of oscillations correlated with the degree of age-dependent

changes. Circadian DNA modifications peaked in two distinct phases which corresponded to

higher levels of modification while the animal was awake and lower levels while the animal was

asleep. These two phases displayed differences in their age-mC association: sleep-phase osc-mCs

gained modification with age while wake-phase osc-mCs lost modification with age. This is

152

consistent with the regression to the mean observed in aging epigenomes. In human neutrophils,

osc-mCs were also found to overlap with differentially modified CpGs identified in epigenomic

studies of leukemia, schizophrenia, and Alzheimer’s disease.

In summary, daily oscillations of DNA modification profiles are robust and can be measured

with sufficient time sampling in a number of mammalian tissues and across a multitude of

platforms. These oscillations are biologically significant as they are implicated in the epigenomic

trajectories of aging and common diseases. This is the first demonstration of the epigenetic

underpinnings connecting circadian rhythmicity, aging, and disease. Based on our findings,

further studies are warranted for a more complete understanding of how evolutionary

advantageous cellular processes can mediate an organism’s demise.

153

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Appendices

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Appendix 1. List of primers used in padlock probe sequencing and library preparations

Sequencing Primers: SE_Read1 ACACTCTTTCCCCAGATGTTATCGAGGTCCGAC SE_Read2 GTGACTGGAGTTCAGGAACGATGAGCCTCCAAC SE_IndexRead GTTGGAGGCTCATCGTTCCTGAACTCCAGTCAC

Library Preparation Forward Primer: Amp_F_SE AATGATACGGCGACCACCGAGATCTACACTCTTTCCCCAGATGTTATCGAGGTCCGAC

Library Preparation Reverse (Index) Primers (SE_Amp_IndX): Ind # Barcode Primer Ind1 ATCACG CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind2 CGATGT CAAGCAGAAGACGGCATACGAGATACATCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind3 TTAGGC CAAGCAGAAGACGGCATACGAGATGCCTAAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind4 TGACCA CAAGCAGAAGACGGCATACGAGATTGGTCAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind5 ACAGTG CAAGCAGAAGACGGCATACGAGATCACTGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind6 GCCAAT CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind7 CAGATC CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind8 ACTTGA CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind9 GATCAG CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind10 TAGCTT CAAGCAGAAGACGGCATACGAGATAAGCTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind11 GGCTAC CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind12 CTTGTA CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind13 CCATGA CAAGCAGAAGACGGCATACGAGATTCATGGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind14 AAGACA CAAGCAGAAGACGGCATACGAGATTGTCTTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind15 CTTCCT CAAGCAGAAGACGGCATACGAGATAGGAAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind16 GGGGTT CAAGCAGAAGACGGCATACGAGATAACCCCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind17 CTCATC CAAGCAGAAGACGGCATACGAGATGATGAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind18 AGTTCA CAAGCAGAAGACGGCATACGAGATTGAACTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind19 GACGCA CAAGCAGAAGACGGCATACGAGATTGCGTCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind20 CCTGTC CAAGCAGAAGACGGCATACGAGATGACAGGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind21 CAACCC CAAGCAGAAGACGGCATACGAGATGGGTTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind22 CTCGGA CAAGCAGAAGACGGCATACGAGATTCCGAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind23 TCGAAA CAAGCAGAAGACGGCATACGAGATTTTCGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind24 ATTCGC CAAGCAGAAGACGGCATACGAGATGCGAATGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind25 TACTGC CAAGCAGAAGACGGCATACGAGATGCAGTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind26 TCGTGA CAAGCAGAAGACGGCATACGAGATTCACGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind27 TACGCG CAAGCAGAAGACGGCATACGAGATCGCGTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

186

Ind28 AGGTGC CAAGCAGAAGACGGCATACGAGATGCACCTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind29 ACGAAC CAAGCAGAAGACGGCATACGAGATGTTCGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind30 TTAGTG CAAGCAGAAGACGGCATACGAGATCACTAAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind31 CACCAC CAAGCAGAAGACGGCATACGAGATGTGGTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind32 GCAAGG CAAGCAGAAGACGGCATACGAGATCCTTGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind33 CGCAAC CAAGCAGAAGACGGCATACGAGATGTTGCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind34 AACTGA CAAGCAGAAGACGGCATACGAGATTCAGTTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind35 ATCGGG CAAGCAGAAGACGGCATACGAGATCCCGATGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind36 CAAGCA CAAGCAGAAGACGGCATACGAGATTGCTTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind37 GCTACA CAAGCAGAAGACGGCATACGAGATTGTAGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind38 TCACGC CAAGCAGAAGACGGCATACGAGATGCGTGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind39 CCAGAG CAAGCAGAAGACGGCATACGAGATCTCTGGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind40 TGACGG CAAGCAGAAGACGGCATACGAGATCCGTCAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind41 GGGAAC CAAGCAGAAGACGGCATACGAGATGTTCCCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind42 GAAAAG CAAGCAGAAGACGGCATACGAGATCTTTTCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind43 AGTGCC CAAGCAGAAGACGGCATACGAGATGGCACTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind44 GCATCC CAAGCAGAAGACGGCATACGAGATGGATGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind45 ACTACG CAAGCAGAAGACGGCATACGAGATCGTAGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind46 CATTTC CAAGCAGAAGACGGCATACGAGATGAAATGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind47 CTCTCC CAAGCAGAAGACGGCATACGAGATGGAGAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind48 ACGTTA CAAGCAGAAGACGGCATACGAGATTAACGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind49 CTGTGT CAAGCAGAAGACGGCATACGAGATACACAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind50 ACCTTT CAAGCAGAAGACGGCATACGAGATAAAGGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind51 TATCGC CAAGCAGAAGACGGCATACGAGATGCGATAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind52 GACACG CAAGCAGAAGACGGCATACGAGATCGTGTCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind53 TTCTAC CAAGCAGAAGACGGCATACGAGATGTAGAAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind54 ACGTCC CAAGCAGAAGACGGCATACGAGATGGACGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind55 TCGACT CAAGCAGAAGACGGCATACGAGATAGTCGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind56 TCAGAC CAAGCAGAAGACGGCATACGAGATGTCTGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind57 TCCTTC CAAGCAGAAGACGGCATACGAGATGAAGGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind58 CAGCAT CAAGCAGAAGACGGCATACGAGATATGCTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind59 GATAGA CAAGCAGAAGACGGCATACGAGATTCTATCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind60 ACAGAT CAAGCAGAAGACGGCATACGAGATATCTGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind61 CTCTAT CAAGCAGAAGACGGCATACGAGATATAGAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind62 TTTAGC CAAGCAGAAGACGGCATACGAGATGCTAAAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind63 CCTGGT CAAGCAGAAGACGGCATACGAGATACCAGGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind64 AGTTGG CAAGCAGAAGACGGCATACGAGATCCAACTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind65 TTCCTT CAAGCAGAAGACGGCATACGAGATAAGGAAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind66 TGGAGG CAAGCAGAAGACGGCATACGAGATCCTCCAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind67 GACGTG CAAGCAGAAGACGGCATACGAGATCACGTCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

187

Ind68 GTTATG CAAGCAGAAGACGGCATACGAGATCATAACGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind69 ATATGG CAAGCAGAAGACGGCATACGAGATCCATATGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind70 GACTTC CAAGCAGAAGACGGCATACGAGATGAAGTCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind71 TCTTTG CAAGCAGAAGACGGCATACGAGATCAAAGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind72 CTGCCA CAAGCAGAAGACGGCATACGAGATTGGCAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind73 GGACTC CAAGCAGAAGACGGCATACGAGATGAGTCCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind74 TGGCGA CAAGCAGAAGACGGCATACGAGATTCGCCAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind75 CGACTT CAAGCAGAAGACGGCATACGAGATAAGTCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind76 CCTATT CAAGCAGAAGACGGCATACGAGATAATAGGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind77 ACGGGT CAAGCAGAAGACGGCATACGAGATACCCGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind78 CGTGTT CAAGCAGAAGACGGCATACGAGATAACACGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind79 CCAAGC CAAGCAGAAGACGGCATACGAGATGCTTGGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind80 TGGTAA CAAGCAGAAGACGGCATACGAGATTTACCAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind81 ACCTGG CAAGCAGAAGACGGCATACGAGATCCAGGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind82 CAAACG CAAGCAGAAGACGGCATACGAGATCGTTTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind83 GTGGTC CAAGCAGAAGACGGCATACGAGATGACCACGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind84 TCTTGT CAAGCAGAAGACGGCATACGAGATACAAGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind85 TGCGGT CAAGCAGAAGACGGCATACGAGATACCGCAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind86 TACCCA CAAGCAGAAGACGGCATACGAGATTGGGTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind87 CGGAAT CAAGCAGAAGACGGCATACGAGATATTCCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind88 ACATTC CAAGCAGAAGACGGCATACGAGATGAATGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind89 ATCAGC CAAGCAGAAGACGGCATACGAGATGCTGATGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind90 AGCACT CAAGCAGAAGACGGCATACGAGATAGTGCTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind91 TCCCTG CAAGCAGAAGACGGCATACGAGATCAGGGAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind92 CGCATG CAAGCAGAAGACGGCATACGAGATCATGCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind93 TAGGCA CAAGCAGAAGACGGCATACGAGATTGCCTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind94 GTATAG CAAGCAGAAGACGGCATACGAGATCTATACGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind95 ACTCGG CAAGCAGAAGACGGCATACGAGATCCGAGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind96 GCAGGT CAAGCAGAAGACGGCATACGAGATACCTGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind97 GTCCTG CAAGCAGAAGACGGCATACGAGATCAGGACGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind98 CATCTT CAAGCAGAAGACGGCATACGAGATAAGATGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind99 GAAGCC CAAGCAGAAGACGGCATACGAGATGGCTTCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind100 AAGCAC CAAGCAGAAGACGGCATACGAGATGTGCTTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind101 TGCGCA CAAGCAGAAGACGGCATACGAGATTGCGCAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind102 GCTAGT CAAGCAGAAGACGGCATACGAGATACTAGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind103 CTTCGA CAAGCAGAAGACGGCATACGAGATTCGAAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind104 TAGTCT CAAGCAGAAGACGGCATACGAGATAGACTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind105 AACCCG CAAGCAGAAGACGGCATACGAGATCGGGTTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind106 CAGTCA CAAGCAGAAGACGGCATACGAGATTGACTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind107 ACACAA CAAGCAGAAGACGGCATACGAGATTTGTGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

188

Ind108 CAGCGA CAAGCAGAAGACGGCATACGAGATTCGCTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind109 TGTATC CAAGCAGAAGACGGCATACGAGATGATACAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind110 TAAGGA CAAGCAGAAGACGGCATACGAGATTCCTTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind111 AAATCG CAAGCAGAAGACGGCATACGAGATCGATTTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind112 CCGTAA CAAGCAGAAGACGGCATACGAGATTTACGGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind113 GTCACA CAAGCAGAAGACGGCATACGAGATTGTGACGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind114 TTCCAA CAAGCAGAAGACGGCATACGAGATTTGGAAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind115 TATGGT CAAGCAGAAGACGGCATACGAGATACCATAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind116 CTCGAC CAAGCAGAAGACGGCATACGAGATGTCGAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind117 GGCAGT CAAGCAGAAGACGGCATACGAGATACTGCCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind118 ACCCGA CAAGCAGAAGACGGCATACGAGATTCGGGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind119 CATGCC CAAGCAGAAGACGGCATACGAGATGGCATGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind120 AGAAAC CAAGCAGAAGACGGCATACGAGATGTTTCTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind121 ATTGGT CAAGCAGAAGACGGCATACGAGATACCAATGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind122 ATGCAT CAAGCAGAAGACGGCATACGAGATATGCATGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind123 GCCGTA CAAGCAGAAGACGGCATACGAGATTACGGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind124 GGGACT CAAGCAGAAGACGGCATACGAGATAGTCCCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind125 CTGCAG CAAGCAGAAGACGGCATACGAGATCTGCAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind126 CAACAG CAAGCAGAAGACGGCATACGAGATCTGTTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind127 TGTCCG CAAGCAGAAGACGGCATACGAGATCGGACAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind128 CGCTTA CAAGCAGAAGACGGCATACGAGATTAAGCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind129 ACTCTC CAAGCAGAAGACGGCATACGAGATGAGAGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind130 CGGGTA CAAGCAGAAGACGGCATACGAGATTACCCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind131 TACGAA CAAGCAGAAGACGGCATACGAGATTTCGTAGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind132 CACTTT CAAGCAGAAGACGGCATACGAGATAAAGTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind133 ACCAAA CAAGCAGAAGACGGCATACGAGATTTTGGTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind134 AGGGAC CAAGCAGAAGACGGCATACGAGATGTCCCTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind135 AAGCTA CAAGCAGAAGACGGCATACGAGATTAGCTTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind136 CAGTGC CAAGCAGAAGACGGCATACGAGATGCACTGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind137 CATAGT CAAGCAGAAGACGGCATACGAGATACTATGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind138 AAGTTC CAAGCAGAAGACGGCATACGAGATGAACTTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind139 CTCAAA CAAGCAGAAGACGGCATACGAGATTTTGAGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind140 AGGCCT CAAGCAGAAGACGGCATACGAGATAGGCCTGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind141 GCGTGT CAAGCAGAAGACGGCATACGAGATACACGCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind142 CGACAA CAAGCAGAAGACGGCATACGAGATTTGTCGGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind143 GTGAGA CAAGCAGAAGACGGCATACGAGATTCTCACGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

Ind144 GGTCTA CAAGCAGAAGACGGCATACGAGATTAGACCGTGACTGGAGTTCAGGAACGATGAGCCTCCAAC

189

Appendix 2. Summary of the number of CpGs and fragments investigated in this thesis

Tissue Experiment CpG or Fragment

# Total # ANOVA P < 0.05

% ANOVA P < 0.05

# osc-mC % osc-mC Perm P-val

Mouse Lung BPP-seq (9 mo) CpG 28,652 14,199 49.56% 5,054 35.59% <10e-4 BPP-seq (15 mo) CpG 29,017 15,479 53.34% 4,353 28.12% 0.0044 BPP-seq (25 mo) CpG 20,712 13,551 65.43% 1,882 13.89% 0.0048

Mouse Liver BPP-seq (9 mo) CpG 31,549 11,941 37.85% 983 8.23% 0.046 OxBS (9 mo) – 5-mC CpG 21,609 5,883 27.22% 443 7.53% 0.0097 – 5-hmC CpG 21,327 4,403 20.65% 375 8.52% 0.019 BPP-seq (15 mo) CpG 29,687 11,761 39.62% 390 3.32% 0.84 BPP-seq (25 mo) CpG 27,872 17,907 64.25% 1224 6.84% 0.15 MRE-chip (9 mo, Real) Fragment 52,341 7,443 14.22% 641 8.61% 0.045 MRE-chip (9 mo, Control) Fragment 52,341 3,025 5.78% 159 5.26% 0.36

Human Neutrophils

MRE-chip (Real) Fragment 81,937 9,298 11.35% 1,112 11.96% 0.026 MRE-chip (Control) Fragment 81,937 5,253 6.41% 227 4.32% 0.79 450K array CpG 485,512 34,137 7.03% 3,912 11.46% 0.029

190

Appendix 3. Quantile-quantile plot of observed harmonic regression p values for BPP-seq experiments in mouse liver and lung

191

Appendix 4. MEME output for the oscillating cytosines in the mouse 9 mo liver

192

Appendix 5. MEME output for the oscillating cytosines in the mouse 9 mo lung

193

Appendix 6. MEME output for the oscillating cytosines in the human neutrophil

194

Appendix 7. Top 20 Gene Ontology terms significantly enriched in genes with significant oscillation and aging in the mouse liver (terms with dispensability score < 0.7 merged)

195

Appendix 8. Top 20 Gene Ontology terms significantly enriched in genes with significant oscillation and aging in the mouse lung (terms with dispensability score < 0.7 merged)

196

Appendix 9. Scree plots of principal component analyses